Add RSA extraction bootstrap and working notes

HANDOFF-2026-04-12-rsa-extraction.md

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+# Handoff: RSA Extraction Bootstrap
+
+Date: 2026-04-12
+
+## Scope Completed
+
+This pass stayed inside the `whetstone_RSA` semantic/bootstrap lane for
+`whetstone_DSL` integration.
+
+Completed artifacts:
+
+- `semantic/rsa_gate_extraction_contracts_v0.json`
+- `tools/extract_gate_rows.py`
+- `docs/case_studies/whetstone_dsl_extraction_yield_snapshot.md`
+- `semantic/extracted_gate_rows_sample.ndjson`
+- `semantic/extracted_gate_rows_slice.ndjson`
+- updates to `sprints/SPRINT-003-whetstone-dsl-gate-semantic-bootstrap.md`
+
+Existing supporting docs from the same line of work that remain relevant:
+
+- `docs/case_studies/whetstone_dsl_gate_inventory.md`
+- `docs/case_studies/whetstone_dsl_semantic_mapping.md`
+- `docs/case_studies/whetstone_dsl_training_data_strategy.md`
+- `docs/case_studies/whetstone_dsl_run_artifact_audit.md`
+- `docs/case_studies/whetstone_dsl_gate_extraction_specs_v0.md`
+- `docs/case_studies/whetstone_dsl_execution_contract_shape_skeleton.md`
+- `semantic/rsa_semantic_bootstrap_v0.json`
+
+## Current State
+
+The integration is still semantic-first, but it now has a parser-facing
+artifact path instead of only prose docs.
+
+What is now true:
+
+- first-pass extraction contracts exist in machine-readable form
+- a parser can read `whetstone_DSL/logs/taskitem_runs/*` and emit normalized
+  per-gate rows
+- a 50-run slice has already been extracted and reviewed
+
+Most important conclusion from the yield pass:
+
+- `prereq_op_selector` is the first extraction-ready supervised gate candidate
+
+Current caution:
+
+- `target_file_selection`
+- `acceptance_command_selection`
+- `required_tool_selection`
+
+are all explicitly present, but in the current slice they are strongly
+sibling-uniform and look too template-driven for naive broad mining.
+
+## Evidence
+
+Key outputs:
+
+- sample rows: `semantic/extracted_gate_rows_sample.ndjson`
+- broader slice: `semantic/extracted_gate_rows_slice.ndjson`
+- yield summary:
+  `docs/case_studies/whetstone_dsl_extraction_yield_snapshot.md`
+
+Observed on the 50-run slice:
+
+- runs processed: `50`
+- tasks processed: `106`
+- rows emitted: `424`
+- rows with rejection flags: `330`
+
+Rejected execution-contract child rows were dominated by:
+
+- `sibling_uniform_target_files`
+- `sibling_uniform_acceptance_commands`
+- `sibling_uniform_required_tools`
+
+The rejected `prereq_op_selector` rows were informative:
+
+- some fallback runs polluted `prerequisiteOps` with tool ids such as
+  `whetstone_generate_taskitems`, `whetstone_queue_ready`, and
+  `whetstone_validate_taskitem`
+- the current rejection logic is correctly catching that schema drift
+
+Representative drift artifact:
+
+- `CLionProjects/whetstone_DSL/logs/taskitem_runs/01a_fallback_intake_spec_20260226_170014/02_generate_taskitems.json`
+
+## Next Step
+
+The next active task is narrow and concrete:
+
+Turn `prereq_op_selector` into the first accepted dataset slice, split into:
+
+- accepted rows
+- rejected rows
+- explicit schema-drift examples
+
+After that:
+
+1. decide whether to hard-filter or separately model the schema-drift variants
+2. only then broaden extraction to more runs
+3. keep `target_file_selection`, `acceptance_command_selection`, and
+   `required_tool_selection` review-gated until a more variant-rich artifact
+   family is found
+
+## Commit Scope
+
+This commit should include only the RSA extraction-bootstrap work above.
+
+There are unrelated untracked files in the repo, including business strategy and
+smartwatch docs plus presentation material. Those should stay out of this
+commit.

docs/business_strategy_options.md

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+# Business Strategy Options
+
+This document is a working note, not a final decision.
+
+The goal is to capture plausible strategic paths for `whetstone_RSA` while the
+broader Whetstone portfolio is still taking shape.
+
+---
+
+## Option 1: Private-First / Paid Pilot Strategy
+
+### Position
+
+Keep the implementation and methodology private while validating demand through
+selective partner conversations, demos, and paid pilot work.
+
+### Why Choose This
+
+- the implementation may be easy for well-resourced teams to replicate once the
+  architecture is fully legible
+- the project may be more valuable as private know-how than as public code
+- early leverage may come from speed, proof, and integration support rather than
+  broad adoption
+
+### What This Optimizes For
+
+- capturing direct value from early implementations
+- private pilot engagements
+- selective design partnerships
+- preserving implementation advantage while the category is still young
+
+### Risks
+
+- lower public visibility
+- slower ecosystem growth
+- less community pull
+- harder to establish category ownership in public
+
+---
+
+## Option 2: Open-Source Portfolio Strategy
+
+### Position
+
+Release `whetstone_RSA` as one open-source library in the broader Whetstone
+portfolio, using it to build reputation, attract collaborators, and establish
+Whetstone as a source of strong architectural ideas.
+
+### Why Choose This
+
+- `whetstone_RSA` is only one of several strong architectural patterns across the
+  Whetstone project family
+- public exposure may be more valuable than trying to protect one library in
+  isolation
+- open source can create trust, reach, and portfolio coherence
+- the real long-term asset may be the broader Whetstone ecosystem, not just the
+  RSA implementation
+
+### What This Optimizes For
+
+- public recognition
+- category association
+- credibility with programmers and technical teams
+- inbound opportunities such as collaborations, consulting, contracts, and
+  future product adoption
+- positioning Whetstone as an open-source software company with a distinctive
+  systems-and-AI architecture philosophy
+
+### What This Does **Not** Assume
+
+- that RSA itself becomes the direct revenue engine
+- that the library alone needs to be defensible as a business moat
+- that open source guarantees monetization
+
+### Strategic Framing
+
+In this option:
+
+- `whetstone_RSA` is a flagship library
+- Whetstone is the larger brand
+- monetization may come from the broader ecosystem:
+  - consulting
+  - integrations
+  - custom work
+  - future products
+  - related libraries and tools
+
+### Risks
+
+- larger companies may adopt or replicate the pattern quickly
+- public explanation may accelerate competitive copying
+- direct monetization from the library itself may be limited
+
+### Why It Still Might Be Worth It
+
+If the broader Whetstone portfolio contains many strong ideas, then publishing
+RSA may be less about protecting one asset and more about proving that Whetstone
+consistently produces valuable software concepts.
+
+In that case, the upside is not just adoption of RSA itself. The upside is:
+
+- reputation
+- trust
+- visibility
+- portfolio legitimacy
+- strategic surface area for future work
+
+---
+
+## Current Thinking
+
+This decision does not need to be made immediately.
+
+The practical stance for now can be:
+
+- keep developing RSA
+- keep refining how to present it
+- treat both private-first and open-source-portfolio as live options
+- make the release decision based on which broader Whetstone strategy becomes
+  more compelling
+
+---
+
+## Key Strategic Question
+
+Is `whetstone_RSA` primarily:
+
+- a monetizable private capability that should be protected early
+
+or
+
+- a high-signal open-source library that helps establish Whetstone as a company
+  and architectural voice?
+
+The answer to that question should drive the release model.

docs/case_studies/whetstone_dsl_extraction_yield_snapshot.md

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+# WhetstoneDSL Extraction Yield Snapshot
+
+## Slice
+
+Parser:
+
+- `tools/extract_gate_rows.py`
+
+Contracts:
+
+- `semantic/rsa_gate_extraction_contracts_v0.json`
+
+Input slice:
+
+- first 50 run directories under
+  `CLionProjects/whetstone_DSL/logs/taskitem_runs`
+
+Output:
+
+- `semantic/extracted_gate_rows_slice.ndjson`
+
+## Summary
+
+Observed counts from the 50-run slice:
+
+- runs processed: `50`
+- tasks processed: `106`
+- rows emitted: `424`
+- rows with rejection flags: `330`
+
+Per-gate emitted rows:
+
+- `prereq_op_selector`: `106`
+- `target_file_selection`: `106`
+- `acceptance_command_selection`: `106`
+- `required_tool_selection`: `106`
+
+Per-gate rejected rows:
+
+- `prereq_op_selector`: `12`
+- `target_file_selection`: `106`
+- `acceptance_command_selection`: `106`
+- `required_tool_selection`: `106`
+
+## Dominant Rejection Flags
+
+- `sibling_uniform_target_files`: `106`
+- `sibling_uniform_acceptance_commands`: `106`
+- `sibling_uniform_required_tools`: `106`
+- `invalid_prerequisite_values:whetstone_generate_taskitems,whetstone_queue_ready,whetstone_validate_taskitem`: `12`
+
+## Interpretation
+
+### `prereq_op_selector`
+
+This is the strongest current extraction surface.
+
+It survives most of the slice cleanly, and the rejection cases are informative
+rather than noisy. The rejected rows come from schema-drift runs where
+`prerequisiteOps` was contaminated with tool ids such as:
+
+- `whetstone_generate_taskitems`
+- `whetstone_queue_ready`
+- `whetstone_validate_taskitem`
+
+That means the parser is catching real label pollution rather than inventing
+false negatives.
+
+### `target_file_selection`
+
+Present explicitly, but not yet trustworthy as broad supervision in this slice.
+
+The dominant issue is sibling-uniform output: all tasks in a run often carry the
+same target-file list, which makes naive extraction look more like template
+reuse than local bounded choice.
+
+### `acceptance_command_selection`
+
+Present explicitly, but also highly template-like in this slice.
+
+The same sibling-uniform pattern appears across runs, which suggests these rows
+should remain review-gated until we either:
+
+- find more diverse artifact families
+- or factor the decision more tightly
+
+### `required_tool_selection`
+
+Same current status as acceptance commands.
+
+The field exists and is easy to parse, but the observed slice suggests strong
+template carryover rather than high-confidence local decision variance.
+
+## Immediate Conclusion
+
+The current first supervised RSA gate should be:
+
+- `prereq_op_selector`
+
+The following remain promising, but should stay review-gated or filtered more
+aggressively before broader extraction:
+
+- `target_file_selection`
+- `acceptance_command_selection`
+- `required_tool_selection`
+
+## Next Steps
+
+1. Tighten parser-side filtering for schema-drift prereq rows and optionally
+   split accepted vs rejected outputs.
+
+2. Search for artifact families with higher local variance for:
+   - `target_file_selection`
+   - `acceptance_command_selection`
+   - `required_tool_selection`
+
+3. Promote `prereq_op_selector` to the first extraction-ready gate dataset.

docs/legache_smartwatch_concept.md

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+# Legache Smartwatch Concept
+
+This note sketches a possible collaboration concept between `whetstone_RSA` and
+Legache.
+
+The goal is not "general AI on a watch."
+
+The goal is a **bounded local knowledge interface** that can interpret short
+voice or text requests, retrieve the right local information, and present fast,
+useful results with local latency.
+
+---
+
+## Core Idea
+
+If Legache already has:
+
+- compact local document indexing
+- strong metadata
+- efficient retrieval
+- structured organization of messy business information
+
+then `whetstone_RSA` could sit on top of that as the **semantic interpretation
+layer**.
+
+In that architecture:
+
+- Legache organizes and retrieves the right local knowledge
+- RSA interprets the user's request into a bounded contract
+- deterministic UI logic renders the response or action
+
+---
+
+## What The Watch App Would Actually Be
+
+Not:
+
+- a chatbot
+- arbitrary long-form question answering
+- open-ended reasoning over a large corpus
+
+Instead:
+
+- a fast local query bar
+- a bounded voice-command interface
+- a local knowledge lookup tool
+- a semantic front-end for deterministic retrieval and display
+
+---
+
+## Example User Requests
+
+- "What's our refund deadline?"
+- "Who approves travel over two thousand dollars?"
+- "Show the latest warehouse checklist."
+- "Open the PTO policy."
+- "Find the client note from yesterday."
+
+These all have a useful bounded structure even if the phrasing is messy.
+
+---
+
+## Proposed Architecture
+
+```text
+synced local corpus + metadata
+    ->
+local retrieval / index lookup
+    ->
+RSA intent + slot interpretation
+    ->
+bounded answer contract
+    ->
+deterministic watch UI
+```
+
+Alternative flow:
+
+```text
+voice input
+    ->
+speech-to-text
+    ->
+RSA gate
+    ->
+retrieval + deterministic rendering
+```
+
+---
+
+## What RSA Would Do
+
+RSA would not try to "know everything in the documents."
+
+RSA would help determine:
+
+- what kind of request this is
+- what data source or document family to search
+- what entities or filters are being referenced
+- whether the request is best handled as:
+  - exact lookup
+  - filtered search
+  - policy answer
+  - latest note/document retrieval
+  - escalation / low-confidence fallback
+
+RSA could also fill a small structured contract such as:
+
+- mode
+- entity type
+- search domain
+- filters
+- requested action
+- confidence
+
+---
+
+## Why A Watch Is Interesting
+
+A smartwatch is a good fit if:
+
+- latency matters
+- privacy matters
+- the requests are short
+- the answer space is bounded enough
+- hands-free voice interaction is useful
+
+That makes this especially interesting for:
+
+- field operations
+- executives
+- support staff
+- warehouse / logistics contexts
+- personal enterprise knowledge access
+
+---
+
+## What Makes This Plausible
+
+This becomes much more plausible if Legache already solves:
+
+- document sync
+- metadata extraction
+- retrieval quality
+- local indexing
+
+In that case, the watch does **not** need to solve open-ended question answering.
+
+It only needs to:
+
+- interpret the request
+- retrieve the right local subset
+- render a bounded answer or action quickly
+
+---
+
+## Good MVP Scope
+
+Start with one narrow workflow.
+
+Examples:
+
+- policy lookup
+- latest-note retrieval
+- checklist lookup
+- contact/account ownership lookup
+- recent event / report lookup
+
+The MVP should:
+
+- use local synced data
+- support short voice/text requests
+- map requests into a bounded contract
+- retrieve and display results locally
+
+---
+
+## Bad MVP Scope
+
+Avoid:
+
+- arbitrary multi-document reasoning
+- long-form answer generation
+- freeform research assistant behavior
+- "answer anything about the whole company"
+
+Those stretch beyond the bounded local-watch value proposition.
+
+---
+
+## Why This Could Be A Good Collaboration
+
+Legache appears to already own an important part of the stack:
+
+- organizing messy information into compact usable local structure
+
+RSA would complement that by supplying:
+
+- semantic intent interpretation
+- bounded contract generation
+- confidence / abstain behavior
+- a small-model path for local interaction
+
+That means the collaboration is not redundant.
+
+It is potentially additive:
+
+- Legache: local knowledge substrate
+- RSA: bounded semantic interface layer
+
+---
+
+## Suggested Positioning
+
+If discussed with Legache, position this as:
+
+**A smartwatch-scale local knowledge interface built on top of Legache's indexed
+corpus, with RSA acting as the semantic interpretation layer for bounded voice or
+text requests.**
+
+This is stronger than saying:
+
+"let me build a watch AI app."
+
+It is more specific, more technical, and more defensible.
+
+---
+
+## MVP Pitch Shape
+
+Suggested framing:
+
+"If Legache already has efficient local indexing and retrieval, I think there may
+be a strong fit for a smartwatch or wearable interface that uses bounded semantic
+interpretation on top of that. The goal would not be open-ended AI on a watch. It
+would be fast local lookup and action for short enterprise knowledge requests,
+using RSA-style intent and slot interpretation to drive deterministic retrieval
+and display."

docs/legache_smartwatch_mvp_proposal.md

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+# Legache Smartwatch MVP Proposal
+
+This document proposes a narrowly scoped MVP for exploring a collaboration
+between Legache and `whetstone_RSA`.
+
+The goal is not to build general-purpose AI on a smartwatch.
+
+The goal is to validate whether a **bounded local knowledge interface** can
+deliver useful enterprise value on wearable hardware with local latency and a
+tightly constrained semantic interpretation layer.
+
+---
+
+## 1. MVP Goal
+
+Build a smartwatch-scale interface that allows a user to issue short voice or
+text requests against a locally synced Legache knowledge corpus and receive fast,
+bounded, useful results.
+
+The MVP should demonstrate:
+
+- local or near-local latency
+- useful interpretation of messy short requests
+- deterministic retrieval and display behavior
+- clear separation between semantic interpretation and execution
+
+---
+
+## 2. Product Hypothesis
+
+If Legache already provides efficient local indexing, metadata, and retrieval for
+messy business information, then a small RSA-style semantic layer can turn short
+watch interactions into structured retrieval actions without needing a large
+generative model on-device.
+
+In short:
+
+Legache supplies the local knowledge substrate.  
+RSA supplies the bounded semantic interface.
+
+---
+
+## 3. MVP User Experience
+
+The user can:
+
+- speak or type a short request
+- receive a fast answer card, result list, or target action
+- interact with local information without waiting on a cloud LLM round-trip
+
+Example requests:
+
+- "What's our refund deadline?"
+- "Open the PTO policy."
+- "Find the latest warehouse checklist."
+- "Who owns the Acme account?"
+- "Show yesterday's client note."
+
+Example outputs:
+
+- policy answer card
+- latest matching document card
+- filtered results list
+- ownership/contact card
+- open-on-phone or continue-on-device handoff
+
+---
+
+## 4. Narrow MVP Scope
+
+Pick one domain first.
+
+Recommended options:
+
+- policy lookup
+- latest-note retrieval
+- checklist retrieval
+- account/contact ownership lookup
+
+Recommended starting choice:
+
+`policy lookup`
+
+Why:
+
+- bounded answer style
+- easy to evaluate
+- useful in enterprise settings
+- strong fit for short watch interactions
+
+---
+
+## 5. What RSA Would Own
+
+For the MVP, RSA would handle:
+
+- request type classification
+- entity / topic extraction
+- filter extraction when relevant
+- confidence / abstain behavior
+- generation of a bounded retrieval contract
+
+Example contract:
+
+```text
+mode: policy_lookup
+topic: refund_deadline
+document_family: finance_policy
+time_scope: current
+confidence: 0.94
+```
+
+RSA would **not** own:
+
+- full document indexing
+- full-text retrieval
+- synchronization
+- long-form answer generation
+- general reasoning over the entire corpus
+
+---
+
+## 6. What Legache Would Own
+
+Legache would provide:
+
+- synced local document corpus or local-accessible subset
+- metadata and document organization
+- retrieval/index/query interface
+- source-of-truth content
+- watch-side or companion-device data delivery path
+
+Legache remains the knowledge substrate.
+
+RSA sits on top as the semantic interpretation layer.
+
+---
+
+## 7. Proposed Architecture
+
+```text
+voice/text request
+    ->
+speech-to-text if needed
+    ->
+RSA semantic gate
+    ->
+bounded retrieval contract
+    ->
+Legache retrieval/index layer
+    ->
+deterministic answer rendering
+    ->
+watch UI
+```
+
+Possible deterministic UI outputs:
+
+- answer card
+- snippet card
+- result list
+- "open on phone"
+- "low confidence, refine query"
+
+---
+
+## 8. Suggested Technical Shape
+
+### Input
+
+- short voice command or short text query
+
+### Interpretation Layer
+
+- tiny transformer or other compact bounded decision model
+- bounded output space
+- optional abstain/escalate behavior
+
+### Retrieval Layer
+
+- Legache local retrieval over synced documents and metadata
+
+### Output Layer
+
+- deterministic answer card or result list
+- no freeform generative answer requirement in MVP
+
+---
+
+## 9. Success Criteria
+
+The MVP is successful if it shows:
+
+- fast response time on target hardware or realistic companion-device setup
+- good interpretation accuracy on a small bounded domain
+- low-friction user experience for short enterprise knowledge requests
+- reliable handoff from semantic interpretation to deterministic retrieval
+
+Suggested measurable targets:
+
+- median latency acceptable for interactive watch use
+- high accuracy on bounded request classes
+- safe abstain behavior on out-of-scope requests
+- usable result presentation on small-screen UI
+
+---
+
+## 10. Evaluation Plan
+
+Use a small curated query set for the chosen domain.
+
+For example:
+
+- 50 to 200 natural-language requests
+- multiple phrasings per intent
+- in-scope and out-of-scope examples
+- confidence and abstain evaluation
+
+Evaluate:
+
+- intent accuracy
+- slot extraction accuracy
+- retrieval success rate
+- end-to-end usefulness
+- latency
+
+---
+
+## 11. Risks
+
+### Risk 1: Watch UI is too constrained
+
+Mitigation:
+
+- use answer cards and handoff flows
+- avoid trying to show too much text
+
+### Risk 2: Voice input is noisier than expected
+
+Mitigation:
+
+- begin with text or constrained voice
+- keep request classes narrow
+
+### Risk 3: Query interpretation is not the real bottleneck
+
+Mitigation:
+
+- start with a bounded domain where retrieval quality is already strong
+
+### Risk 4: Open-ended expectations creep in
+
+Mitigation:
+
+- keep the MVP framed as bounded local knowledge lookup
+
+---
+
+## 12. Proposed MVP Deliverable
+
+A prototype demonstrating:
+
+- one bounded knowledge workflow
+- short voice/text input
+- RSA-based request interpretation
+- Legache-backed local retrieval
+- watch-scale answer rendering
+
+This could be:
+
+- a native watch prototype
+- a wearable mockup with companion device execution
+- or a phone-hosted prototype that simulates the target watch interaction model
+
+---
+
+## 13. Why This MVP Is Interesting
+
+This MVP would test a meaningful product claim:
+
+Can compact local retrieval plus bounded semantic interpretation create a useful
+enterprise knowledge interface on wearable hardware without depending on a large
+generative model?
+
+If yes, that creates a strong story around:
+
+- privacy
+- latency
+- local-first operation
+- bounded AI behavior
+- practical enterprise utility
+
+---
+
+## 14. Suggested Collaboration Framing
+
+This should be positioned as a tightly scoped exploration, not a large product
+commitment.
+
+Suggested framing:
+
+"Let's test one narrow wearable knowledge workflow where Legache supplies the
+local retrieval substrate and RSA supplies the bounded semantic interpretation
+layer. If the prototype shows strong latency and usability, that gives us a
+clear basis for deciding whether a broader wearable interface is worth building."

presentations/rsa_tiny_models_talk.md

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+# Tiny Models For Bounded NLP Decisions
+
+`whetstone_RSA`  
+Fast-slide draft for a 5-10 minute programmer talk
+
+This version is intentionally structured as many short slides with minimal text.
+The goal is to support a visual talk with one idea per slide.
+
+## Slide 1: Title
+
+**Tiny Models For Bounded NLP Decisions**
+
+`whetstone_RSA`
+
+Turning messy user requests into deterministic tool calls
+
+Visual idea:
+Large title, small diagram of `user request -> contract -> tool`
+
+Speaker notes:
+This talk is about a middle layer between regex and LLMs.
+
+## Slide 2: A Common Shape
+
+Users type messy things.
+
+Software needs clean actions.
+
+Visual idea:
+Messy speech bubble on left, clean typed API object on right
+
+Speaker notes:
+This shows up all over software.
+
+## Slide 3: The Gap
+
+We often have:
+
+- fuzzy input
+- bounded output
+- deterministic execution
+
+Visual idea:
+Three stacked boxes with arrows
+
+Speaker notes:
+That middle combination is the important one.
+
+## Slide 4: Today’s Typical Options
+
+- regex
+- rules
+- LLM prompt
+
+Visual idea:
+Three boxes, with regex looking brittle and LLM looking oversized
+
+Speaker notes:
+Most teams bounce between brittle and oversized.
+
+## Slide 5: Regex Is Cheap
+
+Regex and rules are:
+
+- fast
+- local
+- inspectable
+
+Visual idea:
+Green checkmarks next to those three traits
+
+Speaker notes:
+This is why people keep reaching for rules.
+
+## Slide 6: Regex Is Also Annoying
+
+Regex and rules are:
+
+- brittle
+- tedious
+- bad at paraphrases
+
+Visual idea:
+Same rule box cracking under many user phrasings
+
+Speaker notes:
+The moment users say the same thing many ways, rules get ugly.
+
+## Slide 7: LLMs Feel Great
+
+LLMs are:
+
+- flexible
+- natural-language friendly
+- good with paraphrases
+
+Visual idea:
+Many different speech bubbles converging successfully
+
+Speaker notes:
+This is why people like them.
+
+## Slide 8: LLMs Are Often Overkill
+
+For bounded tasks, LLMs can be:
+
+- expensive
+- heavy
+- hard to control
+
+Visual idea:
+Huge engine powering a tiny gear
+
+Speaker notes:
+A lot of current usage is more model than the task actually needs.
+
+## Slide 9: The Missing Middle
+
+There should be a middle layer.
+
+Visual idea:
+`regex -> RSA -> LLM`
+
+Speaker notes:
+That middle layer is what RSA is for.
+
+## Slide 10: What RSA Is
+
+RSA is a contract layer for bounded semantic decisions.
+
+Visual idea:
+Contract sheet icon between user and tool
+
+Speaker notes:
+Not a chatbot. Not a search engine. A decision contract layer.
+
+## Slide 11: What RSA Does
+
+RSA takes:
+
+- messy input
+- bounded schema
+- optional context
+
+Visual idea:
+Three inputs feeding one box
+
+Speaker notes:
+The schema is what keeps it narrow.
+
+## Slide 12: What RSA Returns
+
+RSA returns:
+
+- action
+- slots
+- confidence
+- abstain or escalate
+
+Visual idea:
+Typed JSON-like result
+
+Speaker notes:
+The output is structured, not freeform prose.
+
+## Slide 13: The Core Pattern
+
+Natural language  
+-> semantic interpretation  
+-> bounded contract  
+-> deterministic software
+
+Visual idea:
+Simple four-step pipeline
+
+Speaker notes:
+The model interprets. The software executes.
+
+## Slide 14: Example Query
+
+`show me the failed builds from yesterday`
+
+Visual idea:
+One big centered query bubble
+
+Speaker notes:
+This is the kind of input users naturally want to type.
+
+## Slide 15: Example Contract
+
+Becomes:
+
+- action: `search_builds`
+- status: `failed`
+- date: `yesterday`
+- confidence: `0.93`
+
+Visual idea:
+Search query transformed into structured object
+
+Speaker notes:
+This is not generation. It is semantic interpretation into a contract.
+
+## Slide 16: Example Execution
+
+Then ordinary software:
+
+- runs the query
+- renders the results
+- logs the action
+
+Visual idea:
+Database, search, and UI boxes after the contract
+
+Speaker notes:
+Once the contract exists, it is just normal software again.
+
+## Slide 17: Why This Can Stay Tiny
+
+The output is bounded.
+
+Visual idea:
+Huge input cloud mapped into a small action menu
+
+Speaker notes:
+The model does not need to generate arbitrary text.
+
+## Slide 18: This Is Closer To Classical NLP
+
+- intent classification
+- entity extraction
+- slot filling
+- query interpretation
+
+Visual idea:
+Old-school NLP pipeline icons
+
+Speaker notes:
+This is often closer to smart search than to modern chatbot behavior.
+
+## Slide 19: Search Bar Example
+
+Make a search/help bar feel intelligent.
+
+Stay local.
+
+Stay bounded.
+
+Visual idea:
+Application search bar with local chip or small-device icon
+
+Speaker notes:
+This is one of the clearest examples for programmers.
+
+## Slide 20: Search Bar Inputs
+
+Users type:
+
+- `where do I change export settings`
+- `find the failed import from last week`
+- `how do I reset my API token`
+
+Visual idea:
+Three stacked queries in a fake app UI
+
+Speaker notes:
+These are messy, but the outputs are still bounded.
+
+## Slide 21: Search Bar Outputs
+
+RSA decides:
+
+- mode
+- entity
+- filters
+- confidence
+
+Visual idea:
+Four labeled output chips
+
+Speaker notes:
+It interprets what subsystem should run and how.
+
+## Slide 22: Search Bar Execution
+
+Deterministic systems then:
+
+- open settings
+- run filtered search
+- show help docs
+
+Visual idea:
+Three deterministic destination panels
+
+Speaker notes:
+RSA interprets the request and fills the structured call.
+
+## Slide 23: Where Else This Fits
+
+- command palettes
+- workflow routing
+- support triage
+- tool selection
+- template selection
+
+Visual idea:
+Grid of small app examples
+
+Speaker notes:
+Same shape, different domain.
+
+## Slide 24: WhetstoneDSL / WHIMP
+
+This fits:
+
+- `whetstone_DSL`
+- `WHIMP`
+
+Visual idea:
+Two case-study boxes with arrows into RSA
+
+Speaker notes:
+Those systems have many bounded decisions that are painful to express as pure rules.
+
+## Slide 25: A Lot Of “LLM Workflows” Are Really This
+
+- classification
+- routing
+- extraction
+- slot filling
+
+Visual idea:
+Big prompt window collapsing into four small tasks
+
+Speaker notes:
+A lot of LLM spend is really being used as fuzzy routing over deterministic software.
+
+## Slide 26: The Enterprise Angle
+
+Many teams use giant prompts and context windows
+
+for tasks that are actually bounded.
+
+Visual idea:
+Huge prompt box compared to small contract box
+
+Speaker notes:
+That is the efficiency argument.
+
+## Slide 27: The Careful Claim
+
+RSA does **not** replace all LLM reasoning.
+
+RSA replaces bounded semantic decisions.
+
+Visual idea:
+Two circles: `bounded decisions` inside scope, `open-ended reasoning` outside scope
+
+Speaker notes:
+Keep the claim narrow and defensible.
+
+## Slide 28: Large Context Caveat
+
+RSA is best when the decision can use:
+
+- compact context
+- retrieved context
+- distilled context
+
+Visual idea:
+Large document pile reduced to a few selected cards
+
+Speaker notes:
+For large-context tasks, pair RSA with retrieval or preprocessing.
+
+## Slide 29: Another Example Near The Edge
+
+Script generation can sometimes be:
+
+- template selection
+- slot filling
+- deterministic rendering
+
+Visual idea:
+Prompt -> template -> filled script
+
+Speaker notes:
+Some “code generation” is really hidden template filling.
+
+## Slide 30: The Main Idea
+
+Use a tiny model only where deterministic software still needs help.
+
+Visual idea:
+Small model block at the routing boundary only
+
+Speaker notes:
+That is the whole philosophy.
+
+## Slide 31: Closing
+
+`whetstone_RSA` makes software semantically smarter
+
+without turning everything into a chatbot.
+
+Visual idea:
+Final pipeline graphic, clean and minimal
+
+Speaker notes:
+That is the punchline I want people to leave with.

semantic/extracted_gate_rows_sample.ndjson

@@ -0,0 +1,24 @@
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semantic/rsa_gate_extraction_contracts_v0.json

@@ -0,0 +1,468 @@
+{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "$id": "whetstone-rsa/gate-extraction-contracts-v0",
+  "title": "RSA Gate Extraction Contracts V0",
+  "version": "0.1.0-bootstrap",
+  "status": "bootstrap_source_of_truth",
+  "notes": [
+    "This file defines machine-readable extraction contracts for mining RSA-aligned gate rows from surviving whetstone_DSL artifacts.",
+    "Contracts are gate-native rather than taskitem-native.",
+    "This artifact records what context may be used, what labels are valid, how provenance should be tagged, and when a candidate row must be rejected."
+  ],
+  "enums": {
+    "provenance_class": [
+      "direct_explicit",
+      "direct_bundled",
+      "reconstructed",
+      "untrusted"
+    ],
+    "label_shape": [
+      "enum",
+      "factorized_boolean_slots",
+      "ordered_string_list"
+    ],
+    "artifact_role": [
+      "primary_context",
+      "primary_label_source",
+      "supporting_context",
+      "audit_only"
+    ]
+  },
+  "entities": {
+    "ExtractionContract": {
+      "purpose": "Defines how one RSA gate should be extracted from historical artifacts.",
+      "fields": [
+        {
+          "name": "gate_id",
+          "type": "string",
+          "required": true
+        },
+        {
+          "name": "status",
+          "type": "string",
+          "required": true
+        },
+        {
+          "name": "source_artifacts",
+          "type": "array<object>",
+          "required": true
+        },
+        {
+          "name": "source_fields",
+          "type": "object",
+          "required": true
+        },
+        {
+          "name": "allowed_context_fields",
+          "type": "array<string>",
+          "required": true
+        },
+        {
+          "name": "forbidden_context_fields",
+          "type": "array<string>",
+          "required": true
+        },
+        {
+          "name": "label_shape",
+          "type": "enum:label_shape",
+          "required": true
+        },
+        {
+          "name": "label_schema",
+          "type": "object",
+          "required": true
+        },
+        {
+          "name": "default_provenance",
+          "type": "enum:provenance_class",
+          "required": true
+        },
+        {
+          "name": "rejection_rules",
+          "type": "array<string>",
+          "required": true
+        },
+        {
+          "name": "blocking_issue",
+          "type": "string",
+          "required": false
+        }
+      ]
+    }
+  },
+  "global_rules": {
+    "row_fields": [
+      "gate_id",
+      "decision_context",
+      "decision_label",
+      "decision_provenance",
+      "source_artifacts",
+      "rejection_flags"
+    ],
+    "reject_when": [
+      "candidate label depends on downstream execution results",
+      "candidate label is inferred only from post-hoc failure or success",
+      "candidate label shape does not match the RSA gate contract",
+      "required pre-decision context is missing",
+      "artifact mixes unrelated tasks without isolatable per-task context"
+    ]
+  },
+  "contracts": {
+    "prereq_op_selector": {
+      "status": "first_pass_extractable",
+      "source_artifacts": [
+        {
+          "path_glob": "CLionProjects/whetstone_DSL/logs/taskitem_runs/*/01_intake.json",
+          "role": "primary_context"
+        },
+        {
+          "path_glob": "CLionProjects/whetstone_DSL/logs/taskitem_runs/*/02_generate_taskitems.json",
+          "role": "primary_label_source"
+        },
+        {
+          "path_glob": "CLionProjects/whetstone_DSL/logs/taskitem_runs/*/04_validate_taskitem.json",
+          "role": "audit_only"
+        }
+      ],
+      "source_fields": {
+        "context_fields": [
+          "01_intake.json.normalizedRequirements",
+          "01_intake.json.parsedSpec",
+          "01_intake.json.conflictSignals",
+          "01_intake.json.ambiguitySignals",
+          "02_generate_taskitems.json.tasks[*].title",
+          "02_generate_taskitems.json.tasks[*].milestoneId",
+          "02_generate_taskitems.json.tasks[*].dependencyTaskIds",
+          "02_generate_taskitems.json.tasks[*].confidence",
+          "02_generate_taskitems.json.tasks[*].ambiguityCount",
+          "02_generate_taskitems.json.tasks[*].reasons"
+        ],
+        "label_fields": [
+          "02_generate_taskitems.json.tasks[*].prerequisiteOps"
+        ]
+      },
+      "allowed_context_fields": [
+        "task title",
+        "local task intent",
+        "dependency ids",
+        "local ambiguity signals",
+        "local conflict signals",
+        "normalized requirements",
+        "milestone grouping"
+      ],
+      "forbidden_context_fields": [
+        "acceptance commands",
+        "target files",
+        "required tools",
+        "downstream execution results",
+        "validation pass/fail outcome",
+        "generated code"
+      ],
+      "label_shape": "factorized_boolean_slots",
+      "label_schema": {
+        "slots": [
+          {
+            "name": "needs_validate_intake",
+            "source_value": "validate-intake",
+            "type": "boolean"
+          },
+          {
+            "name": "needs_architect_review",
+            "source_value": "architect-review",
+            "type": "boolean"
+          },
+          {
+            "name": "needs_resolve_dependencies",
+            "source_value": "resolve-dependencies",
+            "type": "boolean"
+          }
+        ],
+        "accepted_source_values": [
+          "validate-intake",
+          "architect-review",
+          "resolve-dependencies"
+        ]
+      },
+      "default_provenance": "direct_bundled",
+      "rejection_rules": [
+        "prerequisiteOps contains values outside the accepted slot vocabulary",
+        "task cannot be isolated from sibling tasks in the artifact",
+        "confidence or dependency signals are missing and context becomes too thin",
+        "list appears post-processed rather than source-emitted"
+      ]
+    },
+    "target_file_selection": {
+      "status": "first_pass_extractable",
+      "source_artifacts": [
+        {
+          "path_glob": "CLionProjects/whetstone_DSL/logs/taskitem_runs/*/01_intake.json",
+          "role": "primary_context"
+        },
+        {
+          "path_glob": "CLionProjects/whetstone_DSL/logs/taskitem_runs/*/02_generate_taskitems.json",
+          "role": "primary_label_source"
+        },
+        {
+          "path_glob": "CLionProjects/whetstone_DSL/datasets/example_run_specs/*",
+          "role": "supporting_context"
+        }
+      ],
+      "source_fields": {
+        "context_fields": [
+          "01_intake.json.normalizedRequirements",
+          "01_intake.json.parsedSpec",
+          "02_generate_taskitems.json.tasks[*].title",
+          "02_generate_taskitems.json.tasks[*].milestoneId",
+          "02_generate_taskitems.json.tasks[*].dependencyTaskIds",
+          "02_generate_taskitems.json.tasks[*].reasons"
+        ],
+        "label_fields": [
+          "02_generate_taskitems.json.tasks[*].executionContract.targetFiles"
+        ]
+      },
+      "allowed_context_fields": [
+        "task title",
+        "local task intent",
+        "relevant normalized requirements",
+        "milestone grouping",
+        "dependency ids",
+        "scenario family metadata"
+      ],
+      "forbidden_context_fields": [
+        "acceptance commands",
+        "required tools",
+        "execution specificity score",
+        "validation diagnostics",
+        "downstream worker assignment",
+        "final implementation output"
+      ],
+      "label_shape": "ordered_string_list",
+      "label_schema": {
+        "field": "target_files",
+        "item_type": "repo_path",
+        "ordered": true,
+        "non_empty": true
+      },
+      "default_provenance": "direct_bundled",
+      "rejection_rules": [
+        "targetFiles list is empty",
+        "targetFiles include unrelated repository surfaces not justified by task text",
+        "targetFiles appear copied across all sibling tasks without local variation",
+        "targetFiles depend on downstream implementation or validation feedback"
+      ]
+    },
+    "acceptance_command_selection": {
+      "status": "first_pass_extractable_with_review",
+      "source_artifacts": [
+        {
+          "path_glob": "CLionProjects/whetstone_DSL/logs/taskitem_runs/*/01_intake.json",
+          "role": "primary_context"
+        },
+        {
+          "path_glob": "CLionProjects/whetstone_DSL/logs/taskitem_runs/*/02_generate_taskitems.json",
+          "role": "primary_label_source"
+        },
+        {
+          "path_glob": "CLionProjects/whetstone_DSL/logs/taskitem_runs/*/04_validate_taskitem.json",
+          "role": "audit_only"
+        }
+      ],
+      "source_fields": {
+        "context_fields": [
+          "01_intake.json.normalizedRequirements",
+          "02_generate_taskitems.json.tasks[*].title",
+          "02_generate_taskitems.json.tasks[*].milestoneId",
+          "02_generate_taskitems.json.tasks[*].executionContract.targetFiles"
+        ],
+        "label_fields": [
+          "02_generate_taskitems.json.tasks[*].executionContract.acceptanceCommands"
+        ]
+      },
+      "allowed_context_fields": [
+        "task title",
+        "local task intent",
+        "target files",
+        "normalized requirements that imply verification surface"
+      ],
+      "forbidden_context_fields": [
+        "validation outcome",
+        "execution success or failure",
+        "downstream generated code",
+        "execution specificity score used as a target proxy"
+      ],
+      "label_shape": "ordered_string_list",
+      "label_schema": {
+        "field": "acceptance_commands",
+        "item_type": "command_id",
+        "ordered": true,
+        "non_empty": true
+      },
+      "default_provenance": "direct_bundled",
+      "rejection_rules": [
+        "acceptanceCommands are obvious template defaults unrelated to task surface",
+        "acceptanceCommands require information only known after code edits",
+        "acceptanceCommands are inconsistent with target files or implied verification surface"
+      ]
+    },
+    "required_tool_selection": {
+      "status": "first_pass_extractable_with_review",
+      "source_artifacts": [
+        {
+          "path_glob": "CLionProjects/whetstone_DSL/logs/taskitem_runs/*/01_intake.json",
+          "role": "primary_context"
+        },
+        {
+          "path_glob": "CLionProjects/whetstone_DSL/logs/taskitem_runs/*/02_generate_taskitems.json",
+          "role": "primary_label_source"
+        },
+        {
+          "path_glob": "CLionProjects/whetstone_DSL/logs/taskitem_runs/*/04_validate_taskitem.json",
+          "role": "audit_only"
+        }
+      ],
+      "source_fields": {
+        "context_fields": [
+          "01_intake.json.normalizedRequirements",
+          "02_generate_taskitems.json.tasks[*].title",
+          "02_generate_taskitems.json.tasks[*].prerequisiteOps",
+          "02_generate_taskitems.json.tasks[*].executionContract.targetFiles"
+        ],
+        "label_fields": [
+          "02_generate_taskitems.json.tasks[*].executionContract.requiredTools"
+        ]
+      },
+      "allowed_context_fields": [
+        "task title",
+        "local task intent",
+        "prerequisite ops",
+        "target files",
+        "normalized requirements"
+      ],
+      "forbidden_context_fields": [
+        "validation-recommended tools as label source",
+        "downstream execution traces",
+        "final worker selection"
+      ],
+      "label_shape": "ordered_string_list",
+      "label_schema": {
+        "field": "required_tools",
+        "item_type": "tool_id",
+        "ordered": true,
+        "non_empty": true
+      },
+      "default_provenance": "direct_bundled",
+      "rejection_rules": [
+        "requiredTools are clearly framework boilerplate with no task-local variation",
+        "requiredTools include post-hoc validator recommendations instead of generation-time selections",
+        "requiredTools cannot be mapped to a stable tool registry"
+      ]
+    },
+    "verification_type": {
+      "status": "second_pass_reconstruction_target",
+      "source_artifacts": [
+        {
+          "path_glob": "CLionProjects/whetstone_DSL/logs/taskitem_runs/*/02_generate_taskitems.json",
+          "role": "supporting_context"
+        }
+      ],
+      "source_fields": {
+        "context_fields": [
+          "02_generate_taskitems.json.tasks[*].title",
+          "02_generate_taskitems.json.tasks[*].executionContract.targetFiles",
+          "02_generate_taskitems.json.tasks[*].executionContract.acceptanceCommands"
+        ],
+        "label_fields": []
+      },
+      "allowed_context_fields": [
+        "target files",
+        "acceptance commands",
+        "task title",
+        "normalized requirements"
+      ],
+      "forbidden_context_fields": [
+        "downstream validation outcome",
+        "downstream execution result"
+      ],
+      "label_shape": "enum",
+      "label_schema": {
+        "accepted_values": [
+          "unit",
+          "schema",
+          "docs",
+          "smoke"
+        ]
+      },
+      "default_provenance": "reconstructed",
+      "rejection_rules": [
+        "verification type cannot be inferred without leaking downstream results"
+      ],
+      "blocking_issue": "No audited artifact yet shows verification_type as a clean standalone field."
+    },
+    "worker_type": {
+      "status": "second_pass_artifact_search_required",
+      "source_artifacts": [
+        {
+          "path_glob": "CLionProjects/whetstone_DSL/logs/taskitem_runs/*",
+          "role": "supporting_context"
+        }
+      ],
+      "source_fields": {
+        "context_fields": [],
+        "label_fields": []
+      },
+      "allowed_context_fields": [
+        "routing-time task metadata only"
+      ],
+      "forbidden_context_fields": [
+        "final execution outputs",
+        "post-hoc review outcome"
+      ],
+      "label_shape": "enum",
+      "label_schema": {
+        "accepted_values": [
+          "implementer",
+          "reviewer",
+          "architect",
+          "qa"
+        ]
+      },
+      "default_provenance": "reconstructed",
+      "rejection_rules": [
+        "no clean serialized worker label is present in the artifact family"
+      ],
+      "blocking_issue": "No clean serialized worker_type label found yet in audited taskitem-generation artifacts."
+    },
+    "cpp_raising_profile": {
+      "status": "second_pass_artifact_search_required",
+      "source_artifacts": [
+        {
+          "path_glob": "CLionProjects/whetstone_DSL/logs/taskitem_runs/*",
+          "role": "supporting_context"
+        }
+      ],
+      "source_fields": {
+        "context_fields": [],
+        "label_fields": []
+      },
+      "allowed_context_fields": [
+        "raising-time IR and tool invocation context"
+      ],
+      "forbidden_context_fields": [
+        "post-hoc build outcome used as the label source"
+      ],
+      "label_shape": "enum",
+      "label_schema": {
+        "accepted_values": [
+          "safe-first",
+          "perf-first",
+          "interop-first"
+        ]
+      },
+      "default_provenance": "untrusted",
+      "rejection_rules": [
+        "no clean serialized raising profile label is present in the artifact family"
+      ],
+      "blocking_issue": "No clean serialized cpp_raising_profile label found yet in audited generic taskitem corpora."
+    }
+  }
+}

sprints/SPRINT-003-whetstone-dsl-gate-semantic-bootstrap.md

@@ -246,6 +246,8 @@ Status:
 - extraction rules for `prereq_op_selector` and execution-contract child
   decisions captured in
   `docs/case_studies/whetstone_dsl_gate_extraction_specs_v0.md`
+- machine-readable extraction contracts captured in
+  `semantic/rsa_gate_extraction_contracts_v0.json`
 
 ### Deliverable D: Artifact-grounded extraction path
 
@@ -258,10 +260,13 @@ Status:
 - audit captured in `docs/case_studies/whetstone_dsl_run_artifact_audit.md`
 - first extraction rules captured in
   `docs/case_studies/whetstone_dsl_gate_extraction_specs_v0.md`
+- parser-facing extraction contracts captured in
+  `semantic/rsa_gate_extraction_contracts_v0.json`
 
 ## Active Next Task
 
-Turn the first extraction specs into machine-readable extraction contracts for:
+Use `semantic/rsa_gate_extraction_contracts_v0.json` to draft the first
+artifact parser for:
 
 - `prereq_op_selector`
 - `target_file_selection`
@@ -270,6 +275,48 @@ Turn the first extraction specs into machine-readable extraction contracts for:
 
 Status:
 
+- complete
+- parser implemented in `tools/extract_gate_rows.py`
+- sample output generated in `semantic/extracted_gate_rows_sample.ndjson`
+
+## Next Active Task
+
+Run the parser over a broader taskitem-run slice and review extraction yield by:
+
+- gate id
+- provenance class
+- rejection-flag frequency
+
+Status:
+
+- complete for first 50-run slice
+- output captured in `semantic/extracted_gate_rows_slice.ndjson`
+- yield review captured in
+  `docs/case_studies/whetstone_dsl_extraction_yield_snapshot.md`
+
+## Updated Working Conclusion
+
+The first extraction-ready dataset candidate is:
+
+- `prereq_op_selector`
+
+The following remain explicit but review-gated due to strong sibling-uniform
+template behavior in the current slice:
+
+- `target_file_selection`
+- `acceptance_command_selection`
+- `required_tool_selection`
+
+## Next Active Task
+
+Turn `prereq_op_selector` into the first accepted dataset slice, with:
+
+- accepted rows
+- rejected rows
+- explicit schema-drift examples
+
+Status:
+
 - partially complete
 - `prereq_op_selector` is now represented as structured/factorized in
   `semantic/rsa_semantic_bootstrap_v0.json`

tools/extract_gate_rows.py

@@ -0,0 +1,292 @@
+#!/usr/bin/env python3
+
+import argparse
+import json
+from pathlib import Path
+from typing import Any, Dict, Iterable, List, Tuple
+
+
+FIRST_PASS_GATES = {
+    "prereq_op_selector",
+    "target_file_selection",
+    "acceptance_command_selection",
+    "required_tool_selection",
+}
+REPO_ROOT = Path(__file__).resolve().parent.parent
+
+
+def load_json(path: Path) -> Dict[str, Any]:
+    with path.open("r", encoding="utf-8") as fh:
+        return json.load(fh)
+
+
+def iter_run_dirs(root: Path) -> Iterable[Path]:
+    for child in sorted(root.iterdir()):
+        if not child.is_dir():
+            continue
+        if (child / "01_intake.json").exists() and (child / "02_generate_taskitems.json").exists():
+            yield child
+
+
+def nonempty_list(value: Any) -> List[Any]:
+    if isinstance(value, list):
+        return value
+    return []
+
+
+def task_context(intake: Dict[str, Any], task: Dict[str, Any]) -> Dict[str, Any]:
+    return {
+        "normalized_requirements": intake.get("normalizedRequirements", []),
+        "parsed_spec": intake.get("parsedSpec", {}),
+        "conflict_signals": intake.get("conflictSignals", []),
+        "ambiguity_signals": intake.get("ambiguitySignals", []),
+        "task_id": task.get("taskId"),
+        "title": task.get("title"),
+        "milestone_id": task.get("milestoneId"),
+        "dependency_task_ids": nonempty_list(task.get("dependencyTaskIds")),
+        "confidence": task.get("confidence"),
+        "ambiguity_count": task.get("ambiguityCount"),
+        "reasons": nonempty_list(task.get("reasons")),
+    }
+
+
+def build_sibling_maps(tasks: List[Dict[str, Any]]) -> Dict[str, Dict[str, int]]:
+    target_counts: Dict[str, int] = {}
+    acceptance_counts: Dict[str, int] = {}
+    tool_counts: Dict[str, int] = {}
+    for task in tasks:
+        contract = task.get("executionContract", {})
+        targets = tuple(nonempty_list(contract.get("targetFiles")))
+        acceptance = tuple(nonempty_list(contract.get("acceptanceCommands")))
+        tools = tuple(nonempty_list(contract.get("requiredTools")))
+        if targets:
+            target_counts[json.dumps(targets)] = target_counts.get(json.dumps(targets), 0) + 1
+        if acceptance:
+            acceptance_counts[json.dumps(acceptance)] = acceptance_counts.get(json.dumps(acceptance), 0) + 1
+        if tools:
+            tool_counts[json.dumps(tools)] = tool_counts.get(json.dumps(tools), 0) + 1
+    return {
+        "targets": target_counts,
+        "acceptance": acceptance_counts,
+        "tools": tool_counts,
+    }
+
+
+def base_row(
+    gate_id: str,
+    run_dir: Path,
+    task: Dict[str, Any],
+    contract_path: Path,
+    provenance: str,
+) -> Dict[str, Any]:
+    return {
+        "gate_id": gate_id,
+        "run_id": run_dir.name,
+        "task_id": task.get("taskId"),
+        "decision_context": {},
+        "decision_label": {},
+        "decision_provenance": provenance,
+        "source_artifacts": [
+            str(run_dir / "01_intake.json"),
+            str(run_dir / "02_generate_taskitems.json"),
+            str(contract_path),
+        ],
+        "rejection_flags": [],
+    }
+
+
+def extract_prereq_row(
+    run_dir: Path,
+    intake: Dict[str, Any],
+    task: Dict[str, Any],
+    contract_path: Path,
+) -> Dict[str, Any]:
+    row = base_row("prereq_op_selector", run_dir, task, contract_path, "direct_bundled")
+    prereqs = nonempty_list(task.get("prerequisiteOps"))
+    accepted = {"validate-intake", "architect-review", "resolve-dependencies"}
+    invalid = sorted(op for op in prereqs if op not in accepted)
+    row["decision_context"] = task_context(intake, task)
+    row["decision_label"] = {
+        "needs_validate_intake": "validate-intake" in prereqs,
+        "needs_architect_review": "architect-review" in prereqs,
+        "needs_resolve_dependencies": "resolve-dependencies" in prereqs,
+        "source_values": prereqs,
+    }
+    if invalid:
+        row["rejection_flags"].append(f"invalid_prerequisite_values:{','.join(invalid)}")
+    if not row["decision_context"]["title"]:
+        row["rejection_flags"].append("missing_task_title")
+    if row["decision_context"]["confidence"] is None:
+        row["rejection_flags"].append("missing_confidence")
+    return row
+
+
+def extract_target_files_row(
+    run_dir: Path,
+    intake: Dict[str, Any],
+    task: Dict[str, Any],
+    contract_path: Path,
+    sibling_maps: Dict[str, Dict[str, int]],
+    sibling_count: int,
+) -> Dict[str, Any]:
+    row = base_row("target_file_selection", run_dir, task, contract_path, "direct_bundled")
+    execution_contract = task.get("executionContract", {})
+    targets = nonempty_list(execution_contract.get("targetFiles"))
+    row["decision_context"] = task_context(intake, task)
+    row["decision_label"] = {
+        "target_files": targets,
+    }
+    if not targets:
+        row["rejection_flags"].append("empty_target_files")
+    if sibling_count > 1 and targets:
+        key = json.dumps(tuple(targets))
+        if sibling_maps["targets"].get(key, 0) == sibling_count:
+            row["rejection_flags"].append("sibling_uniform_target_files")
+    return row
+
+
+def extract_acceptance_row(
+    run_dir: Path,
+    intake: Dict[str, Any],
+    task: Dict[str, Any],
+    contract_path: Path,
+    sibling_maps: Dict[str, Dict[str, int]],
+    sibling_count: int,
+) -> Dict[str, Any]:
+    row = base_row("acceptance_command_selection", run_dir, task, contract_path, "direct_bundled")
+    execution_contract = task.get("executionContract", {})
+    commands = nonempty_list(execution_contract.get("acceptanceCommands"))
+    row["decision_context"] = task_context(intake, task)
+    row["decision_context"]["target_files"] = nonempty_list(execution_contract.get("targetFiles"))
+    row["decision_label"] = {
+        "acceptance_commands": commands,
+    }
+    if not commands:
+        row["rejection_flags"].append("empty_acceptance_commands")
+    if not row["decision_context"]["target_files"]:
+        row["rejection_flags"].append("missing_target_files_context")
+    if sibling_count > 1 and commands:
+        key = json.dumps(tuple(commands))
+        if sibling_maps["acceptance"].get(key, 0) == sibling_count:
+            row["rejection_flags"].append("sibling_uniform_acceptance_commands")
+    return row
+
+
+def extract_required_tools_row(
+    run_dir: Path,
+    intake: Dict[str, Any],
+    task: Dict[str, Any],
+    contract_path: Path,
+    sibling_maps: Dict[str, Dict[str, int]],
+    sibling_count: int,
+) -> Dict[str, Any]:
+    row = base_row("required_tool_selection", run_dir, task, contract_path, "direct_bundled")
+    execution_contract = task.get("executionContract", {})
+    tools = nonempty_list(execution_contract.get("requiredTools"))
+    row["decision_context"] = task_context(intake, task)
+    row["decision_context"]["prerequisite_ops"] = nonempty_list(task.get("prerequisiteOps"))
+    row["decision_context"]["target_files"] = nonempty_list(execution_contract.get("targetFiles"))
+    row["decision_label"] = {
+        "required_tools": tools,
+    }
+    if not tools:
+        row["rejection_flags"].append("empty_required_tools")
+    if sibling_count > 1 and tools:
+        key = json.dumps(tuple(tools))
+        if sibling_maps["tools"].get(key, 0) == sibling_count:
+            row["rejection_flags"].append("sibling_uniform_required_tools")
+    return row
+
+
+def extract_rows_from_run(run_dir: Path, contract_path: Path) -> Tuple[List[Dict[str, Any]], Dict[str, int]]:
+    intake = load_json(run_dir / "01_intake.json")
+    generated = load_json(run_dir / "02_generate_taskitems.json")
+    tasks = nonempty_list(generated.get("tasks"))
+    sibling_maps = build_sibling_maps(tasks)
+    rows: List[Dict[str, Any]] = []
+    stats = {"runs": 1, "tasks": len(tasks), "rows": 0, "rejected_rows": 0}
+    sibling_count = len(tasks)
+    for task in tasks:
+        gate_rows = [
+            extract_prereq_row(run_dir, intake, task, contract_path),
+            extract_target_files_row(run_dir, intake, task, contract_path, sibling_maps, sibling_count),
+            extract_acceptance_row(run_dir, intake, task, contract_path, sibling_maps, sibling_count),
+            extract_required_tools_row(run_dir, intake, task, contract_path, sibling_maps, sibling_count),
+        ]
+        for row in gate_rows:
+            rows.append(row)
+            stats["rows"] += 1
+            if row["rejection_flags"]:
+                stats["rejected_rows"] += 1
+    return rows, stats
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Extract RSA gate rows from whetstone_DSL taskitem runs.")
+    parser.add_argument(
+        "--contracts",
+        default="semantic/rsa_gate_extraction_contracts_v0.json",
+        help="Path to the extraction contract JSON.",
+    )
+    parser.add_argument(
+        "--taskitem-runs-root",
+        required=True,
+        help="Root directory containing whetstone_DSL taskitem run folders.",
+    )
+    parser.add_argument(
+        "--output",
+        required=True,
+        help="Output NDJSON path for extracted rows.",
+    )
+    parser.add_argument(
+        "--limit-runs",
+        type=int,
+        default=0,
+        help="Optional limit on how many run directories to process.",
+    )
+    return parser.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    contract_path = Path(args.contracts)
+    if not contract_path.is_absolute():
+        contract_path = (REPO_ROOT / contract_path).resolve()
+    contracts = load_json(contract_path)
+    missing = FIRST_PASS_GATES.difference(contracts.get("contracts", {}).keys())
+    if missing:
+        raise SystemExit(f"missing first-pass gate contracts: {sorted(missing)}")
+
+    runs_root = Path(args.taskitem_runs_root).resolve()
+    output_path = Path(args.output).resolve()
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    rows: List[Dict[str, Any]] = []
+    totals = {"runs": 0, "tasks": 0, "rows": 0, "rejected_rows": 0}
+    for index, run_dir in enumerate(iter_run_dirs(runs_root), start=1):
+        if args.limit_runs and index > args.limit_runs:
+            break
+        run_rows, run_stats = extract_rows_from_run(run_dir, contract_path)
+        rows.extend(run_rows)
+        for key, value in run_stats.items():
+            totals[key] += value
+
+    with output_path.open("w", encoding="utf-8") as fh:
+        for row in rows:
+            fh.write(json.dumps(row, sort_keys=True) + "\n")
+
+    summary = {
+        "contracts_path": str(contract_path),
+        "runs_root": str(runs_root),
+        "output_path": str(output_path),
+        "runs_processed": totals["runs"],
+        "tasks_processed": totals["tasks"],
+        "rows_emitted": totals["rows"],
+        "rows_with_rejection_flags": totals["rejected_rows"],
+    }
+    print(json.dumps(summary, indent=2, sort_keys=True))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())