Normalize every SKILL.md description to double-quoted YAML so the corpus,
templates, and examples have one visible convention. Harden frontmatter
parsing against partial fence matches and non-string name/description values,
and extend tests to cover those regressions.
Fix stale or misleading developer-facing docs: remove phantom book-sft plugin
install instructions, document directory-copy installs, add .agents/skills
manual install guidance, fix the digital-brain setup script target, and mark
historical 13-skill analysis as historical. Cut the prepared release metadata
to 2.3.1 across manifests and root SKILL.md.
Tighten production gates by sharing dev dependencies, making orchestrators use
strict repo validation, aligning skill-health required sections with repo
validation, and linting documented plugin install commands against the actual
marketplace manifest.
Co-authored-by: Muratcan Koylan <muratcankoylan@users.noreply.github.com>
Ships a file-based research-to-skill operating system on top of the v2.1
skill corpus. The corpus now compounds across runs: mechanisms have a
gated promotion path, claims have explicit provenance, runs have an
enforceable state machine, skill activation is regression-tested, and the
harness is benchmarked against adversarial scenarios. A continuous loop
runs the whole thing unattended from launchd, retrieving sources over
stdlib HTTP and parking anything that needs human review.
Highlights
- Mechanism registry + accepted/rejected ledgers (5 seeded mechanisms)
- Claim provenance for 6 volatile claims across 6 skills
- Corpus index mapping skills to scenarios, mechanisms, and claims
- Activation regression fixtures (8 cases, all passing)
- Adversarial benchmark harness (7 scenarios) with append-only history
- Run state machine: initialized -> retrieved -> evaluated -> proposed ->
novelty_checked -> validated -> pr_ready -> closed
- Continuous loop (loop_discover, loop_step, loop_daily, loop_status)
with atomic JSONL writes and fcntl locks
- launchd service definitions and operator runbook
- CI workflow runs validate_repo --strict, run_benchmarks, and
check_activation_cases on every PR
- AGENTS.md, CHANGELOG.md, researcher/runs/README.md,
researcher/insights/auto-research-experiment.md
Skill changes
- All 14 published skills migrated from keyword triggers to
activation-scenario descriptions
- Hardened evaluation/advanced-evaluation and project-development/
harness-engineering boundaries
Repository policy
- Active research runs (researcher/runs/*/) are runtime state and
gitignored. Seed run kept as a worked example, closed reference-only.
- Runtime queue, logs, snapshots, status, and loop event files are
gitignored.
Verification
- validate_repo --strict: 0 errors, 0 warnings, 14 skills
- run_benchmarks: 3 checks, 0 failures, 7 scenarios
- check_activation_cases: 8 cases, 0 failures
- Python compile: clean (12 researcher scripts)
- Lint: clean
- Live loop: 13 iterations executed across init/retrieve/park/reap paths
No paid LLM judges, no automated source discovery beyond the manual seed,
and no log rotation. See researcher/insights/auto-research-experiment.md
for the full lessons-learned writeup.
Co-authored-by: Cursor <cursoragent@cursor.com>
Cursor Agent