whetstone_DSL/FEATURE_REQUESTS.md

Feature Requests

Backlog of feature ideas to triage into future sprints (e.g., Sprint 6/7).

Security Vulnerability Awareness (Dependencies) — IMPLEMENTED (Sprint 6, Steps 190–195)

Goal: Warn when a dependency has known vulnerabilities and surface safer alternatives.

Concept:

• Maintain a vulnerability knowledge base (local cache + optional remote sources).
• When a dependency is added/updated, show immediate warnings.
• Surface findings in the Dependencies panel and Problems list.
• Optionally block auto-upgrade to vulnerable versions.

Potential Data Sources:

• OSV (Open Source Vulnerabilities) API / datasets
• NVD (CVE/NVD feeds)
• GitHub Security Advisories (GHSA)
• OWASP references (for categorization)

Candidate Data Model:

• VulnerabilityRecord
  • ecosystem (pypi/npm/crates/maven/go/vcpkg/etc)
  • package
  • affected_versions
  • severity
  • summary
  • references

UI/UX:

• Dependencies panel: inline warning badges and �View Advisory�.
• Problems panel: security diagnostics with severity.
• Agent hints: prefer safe alternatives when available.

---

Semantic Annotations for Library APIs — IMPLEMENTED (Sprint 6, Steps 193–194)

Goal: Tag library functions/types with semantic annotations (e.g., @serialize, @crypto, @io) so humans/agents can discover intent-driven APIs quickly.

Concept:

• Add annotation metadata for library symbols (by library + symbol).
• Attach annotations to ExternalModule / TypeSignature nodes.
• Use annotations to filter in Library Browser and guide agent completion.

Candidate Storage:

• annotations/library_semanno.json (or similar)
• Format: { library: { symbol: [annotations...] } }

UI/UX:

• Library Browser: filter by annotation tag.
• Completion ranking: prioritize annotated matches for task keywords.
• Agent prompts: �Use @serialize APIs� guidance.

---

Notes

• Treat these as separate features to schedule independently.
• Likely Sprint 6/7, after core library-aware flow is stable.

LLM Tooling & MCP Bridge — PLANNED (Sprint 7, Steps 202–234)

Goal: Make the agent API easy for LLMs to use and optionally expose it via MCP.

Status: Full plan written in sprint7_plan.md. 33 steps across 6 phases:

• Phase 7a: API documentation & JSON schemas (Steps 202–206)
• Phase 7b: MCP server with tools/resources/prompts (Steps 207–213)
• Phase 7c: Synthetic trace generation for training data (Steps 214–219)
• Phase 7d: Evaluation harness for LLM tool-use accuracy (Steps 220–224)
• Phase 7e: Model-specific tool definitions — Claude, Codex, open-source (Steps 225–229)
• Phase 7f: Session recording pipeline — capture, anonymize, export (Steps 230–234)

---

PHP Language Support (Full Pipeline) — PROPOSED

Goal: Add full PHP support (parse, AST, generate, project, and annotations) to Whetstone.

Scope:

• Tree-sitter PHP parser integration
• PHP AST mapping to SemAnno concepts
• PHP generator with annotation-aware output
• Cross-language projection to/from PHP
• Tests: parse/generate round-trip, annotation preservation, projection matrix coverage

Notes:

• Start with core PHP syntax (functions, classes, namespaces, arrays, exceptions, traits).
• WordPress-specific libraries on top of core PHP support.

---

WordPress Support (Library + Semantics) — PROPOSED

Goal: Enable WordPress-aware tooling on top of PHP support.

Scope:

• Library symbol stubs for WordPress core APIs
• Semantic tags for WP concepts (hooks, filters, actions)
• Agent suggestions for safe WP patterns

---

Rust Plugin Packaging (Conflict Resolver) — PROPOSED

Goal: Package a Rust-based topological conflict resolver as a Whetstone plugin.

Scope:

• Define plugin interface for external tools
• Packaging format and plugin discovery/loading
• Example integration with a Rust binary or shared library

Julia Language Support (Full Pipeline) — PROPOSED

Goal: Add full Julia support (parse, AST, generate, project, and annotations) to Whetstone.

Scope:

• Tree-sitter Julia parser integration
• Julia AST mapping to SemAnno concepts
• Julia generator with annotation-aware output
• Cross-language projection to/from Julia
• Tests: parse/generate round-trip, annotation preservation, projection matrix coverage

---

Julia ML Projection Layer — PROPOSED

Goal: Map common Python ML/Numerical APIs to Julia equivalents while preserving optimization intent.

Scope:

• API mapping table (NumPy/Pandas/Torch core calls ? Julia equivalents)
• Semantic tags for numerical/tensor operations
• Fallback interop for unmapped calls (PyCall/JuliaCall)
• Dual projections: clean surface Julia + preserved optimization annotations

Julia Packaging Strategy — PROPOSED

Goal: Define a reliable packaging path for Julia-based artifacts.

Scope:

• Document options: runtime install, PackageCompiler.jl, embedding Julia as a library
• Provide recommended defaults for CLI tools vs GUI apps
• Include guidance for minimizing startup latency and bundle size