ruvnet / ruvnet/RuVector

[PIR][WP19] Self-distilled entity-grounded skill synthesis in the Darwin loop (SkillForge pattern, ADR-321)

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adr phase-w2-3 pir wave-2
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Description

Part of the RuV Perpetual Intelligence Runtime (PIR), Wave 2. Epic: #837. Plan: docs/research/perpetual-intelligence-runtime/07-wave2-program-plan.md · evidence: 06-wave2-evidence-review.md (both land on main with the feat/pir-wave2-adrs PR).

ADR mapping

Proposed ruvector ADR-321 — self-distilled, entity-grounded skill synthesis inside the Darwin mutation loop. Extends merged ruvector ADR-313 (SHAPER, WP9 #841) directly: a mutation-generation strategy that synthesizes test-covered-functionality issues from the target repo itself (rather than waiting for real incidents), distills entity-grounded skills, and feeds them into the existing frozen-weight harness-evolution loop as one more candidate source.

Evidence

SkillForge (arXiv:2608.18933) — "SkillForge: Self-Distilling Agents for Project-Specific Issue Resolution," submitted 2026-08-19. Grade B+ — the mechanism is confirmed verbatim, but the headline "beats issue-resolution baselines" claim is qualitative only: no percentage figure appears in the abstract, so the magnitude of improvement is not independently verifiable.

⚠️ Because of the B+ grade, no performance number may be quoted for SkillForge anywhere in this WP's deliverables — not in the ADR, code comments, commit messages, or status updates. There is no verified number to quote; any figure that appears would be fabricated or sourced from unverified material. The only citable performance claim is this program's own research-gate-measured delta.

Artifact available now: github.com/cslsolow/SkillForge (verified via GitHub API: public, 2.5 MB, real content — distilling/, synthesis/, src/, evaluation_result/) — adapt the released implementation rather than rebuilding from the paper.

Scope

  • Add synthetic-issue-from-test-coverage skill distillation as a Darwin mutation-candidate source.
  • Surfaces: harness/src/darwin.ts, examples/mragent scorePolicy — the same surfaces WP9 already owns; coordinate with WP9's remaining scope.

Candidate-mutation rule (applies to all of Wave 2)

Candidate mutation, not trusted prior art. Promotion requires this program's own research-gate paired-bootstrap-recomputed delta over the pre-WP baseline — never the paper's self-reported (here: nonexistent) numbers.

Acceptance criteria

  • SkillForge-pattern candidate source wired into the Darwin loop (ADR-321)
  • research-gate-measured delta over the pre-WP baseline recorded before any promotion
  • Zero quoted SkillForge performance numbers anywhere in the deliverable (review-enforced)
  • security scan after landing (touches the mutation-proposal surface)
  • ADR-321 merged with repo-qualified citations per ADR-305 §4

Depends on: WP9 (#841) — the SHAPER-pattern loop must exist to receive this as a candidate source. Phase W2-3.

Contributor guide

No contributing guide indexed for this repository

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with harness/src/darwin.ts and the examples/mragent scorePolicy surface, then read ADR-313, ADR-321, and the Wave 2 program plan. Review the released SkillForge artifact before defining the candidate source. Done means the source is wired into the Darwin loop, the research-gate delta is recorded, no SkillForge performance numbers are quoted, ADR-321 is merged, and a security scan passes.

Written by the indexing model from the issue text.

Assessment

Tech stack
typescript
Domain
ai, machine-learning
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Quiet
Clarity
Mostly clear
Newbie friendliness
35/100

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