muratcankoylan@Agent-Skills-for-Context-Engineering

Imported from gabrielmoreira/agent-skills-mirror (mirrors/repos/muratcankoylan@Agent-Skills-for-Context-Engineering/AGENTS.md). Install upstream with npx skills add gabrielmoreira/agent-skills-mirror --skill muratcankoylan@Agent-Skills-for-Context-Engineering. Copyright stays with the author.

AGENTS.md

Workspace memory for agents collaborating on this repository. Keep entries durable and broadly applicable; one-off task state belongs in chat or in a run thread, not here.

Learned User Preferences

• For autonomous research and repo-improvement work in this workspace, prefer proceeding through concrete research loops, subagents, validation, and edits when the scope is clear rather than asking broad process questions.
• Avoid stale regex or keyword-list heuristics in skills and scripts; prefer mechanism-level criteria, rubrics, and evidence-backed validation.
• Never push to GitHub or merge a PR without explicit user approval. Preparing branches, commits, and PRs is permitted only when the user has approved that specific action.
• Tone is technical CTO: direct, no marketing language, no exclamation marks, no emojis, no em dashes. State trade-offs and complexity upfront.
• When the scope spans multiple architectural decisions or irreversible changes, propose a plan first instead of executing.
• For benchmarks and evaluation work, hold to research-paper-grade methodology (statistical discipline, bias mitigation, ablations, reproducibility) over speed. Don't rush.

Learned Workspace Facts

• This repo is an autonomous research-to-skill organization. External AI research is curated through rubrics and distilled into context-engineering and harness-engineering skill updates.
• researcher/ is repo-native and file-based so agents can resume, audit, validate, and prepare PR-ready skill changes without a hosted scheduler.
• Per-run state lives in researcher/runs/<run-id>/run-state.json with explicit transitions (initialized -> retrieved -> evaluated -> proposed -> novelty_checked -> validated -> pr_ready -> closed). Use research_loop.py subcommands to advance state, never hand-edit run-state.json.
• Repo health (validate_repo.py) and per-run readiness (validate_run.py) are different questions. CI runs validate_platform_compat.py --require-reference-validator, validate_repo.py --strict, skill_health.py --strict --no-history, run_benchmarks.py, and check_activation_cases.py on every PR via .github/workflows/validate.yml.
• The mechanism registry (researcher/mechanisms/registry.jsonl) is the encyclopedia backbone. Promotion is gated by research_loop.py promote-mechanisms with a recorded reviewer; ledgers live under researcher/mechanisms/ledgers/.
• Claim provenance for numeric or volatile claims lives in researcher/claims/index.jsonl. Add an entry for any new benchmark or volatility-sensitive claim.
• The corpus index (researcher/corpus/index.json) is the machine-readable map of skills, activation scenarios, mechanisms, and claims. Update it when adding or restructuring skills.
• The continuous loop (researcher/scripts/loop_*.py) runs from launchd via researcher/orchestration/launchd/. It never invokes paid LLMs; HTTP retrieval is stdlib-only with a 1.5 MB cap and a 30-second timeout.
• Runtime state is not committed: researcher/queue/*.jsonl, researcher/queue/.locks/, researcher/reports/{logs,snapshots,loop-events.jsonl,loop-failures.jsonl,status.md,parked-review.md}, and researcher/runs/*/ are gitignored. The seed run 20260515-035228-executable-autonomous-research-frameworks is the only committed run; it is closed as reference-only and serves as a worked example.
• The current prepared release version is 2.6.0 across .claude-plugin/marketplace.json, .plugin/plugin.json, and root SKILL.md. There are 18 skills (latent-briefing covers KV cache sharing between agents; self-improvement-loops covers RSI, meta-harness search, and evolutionary scaffold optimization; long-horizon-prompting covers pseudo-formal task briefs for long-running and parallel agent runs; self-managed-context covers model-controlled live context editing).
• New accepted-ledger events must carry event_type, source_commit, and a committed source_path; a run_dir pointer alone dangles on a clean checkout because run directories are gitignored. research_loop.py promote-mechanisms still emits the run_dir shape, so write durable events directly until it is fixed.
• Detailed lessons from building the researcher OS live in researcher/insights/auto-research-experiment.md (engineering rationale) and researcher/insights/how-we-built-this.md (project narrative and sharing templates); read both before extending the harness or writing release-facing prose.
• Benchmarks are staged in researcher/benchmarks/: Stage 0 deterministic harness (shipped), Stage 1 per-skill health via researcher/scripts/skill_health.py (shipped; output researcher/reports/skill-health.json is gitignored), Stage 2 router (shipped; results in researcher/benchmarks/router/results-published/), Stage 3 effectiveness (scaffolded, one task built), Stage 4 composition (future). researcher/benchmarks/PLAN.md is the methodology source of truth.
• Current corpus counts and compatibility status are generated in researcher/generated/corpus-summary.md; do not copy live totals into workspace memory. Published benchmark reports remain dated snapshots. Do not describe a skill improvement as complete unless the prose, mechanism registry, claim index, corpus index, activation fixtures, generated inventory, and validators all agree.
• Public export uses validate_export.py plan|render|check and registered transforms from governance/export-policy.yaml. Private plans and receipts are ignored; public manifests contain projection and output digests, never private source paths or input digests.
• Durable cross-runtime records resolve through researcher/schemas/registry.json. JSON digests use the integer-only jcs-rfc8785-integer-v1 profile; blobs hash exact bytes. ArtifactRef never contains a private locator or grants authority, and reads must validate its private StorageBinding. Candidate evaluation must use a SPEC-003 freeze receipt created under an explicit editable-surface policy, not a mutable worktree.
• Benchmark execution uses the Cursor SDK runner at researcher/benchmarks/sdk-runner/ (TypeScript, @cursor/sdk 1.0.13). The runner supports --concurrency N, --no-resume, per-run progress logging, format-failure retry, and worst-case retry-aware cost forecasting; default behavior is to resume by skipping plan items that already have result files. Result artifacts under researcher/benchmarks/{router,effectiveness}/results/ and history JSONLs (router-history.jsonl, effectiveness-history.jsonl) are gitignored.
• Published Stage 2 router-benchmark results: researcher/benchmarks/router/results-published/2026-05-15.md (baseline), researcher/benchmarks/router/results-published/2026-05-15-v2.md (post-rewrite with delta-vs-baseline table), and researcher/benchmarks/router/results-published/2026-05-19.md (post-corpus-hardening validation: 600/600 usable records, 0 format failures, top-1 Gemini 0.920 / Composer 0.913 / GPT-5.5 0.913 / Claude Opus 4.7 0.840). Headline finding: targeted description rewrites moved context-fundamentals top-1 by +23.4pp and project-development top-1 to 1.000; corpus-wide hardening did not cause broad routing collapse.

Repository Operating Defaults

• Deterministic checks before model judges. Always run validate_platform_compat.py --require-reference-validator and validate_repo.py --strict before claiming a skill-format or packaging change is complete.
• Adversarial benchmarks before declaring the harness safe. Add a scenario when a new failure mode is discovered.
• Append-only ledgers for accepted and rejected mechanisms so future agents do not rediscover failed paths.
• Atomic writes (tempfile + os.replace) and fcntl locks for any shared file the loop touches.
• Live execution is the highest-signal validation for orchestration code; smoke-test changes against the actual loop before declaring them safe.
• Cursor SDK is the only paid-API surface allowed for benchmarks. Privacy Mode required, apiKey passed explicitly per call, never settingSources: "all" in benchmarks (use [] for control, ["project"] with a curated .cursor/skills/ for ablation). Cost gates (--max-runs, --max-budget-usd, or --dry-run) must be set before any SDK call.
• Description quality is measurable. When changing skill activation descriptions, re-run the router benchmark with the same seed and fixture and publish the delta. Aggregate accuracy is a misleading unit; per-skill effect sizes and the confusion matrix are the right view.
• A skill is a multi-surface artifact. Changing the frontmatter description is not enough; the SKILL.md body When to Activate and Integration sections must be audited the same day so the body does not contradict the description that routed the agent to it. The router benchmark only sees descriptions (settingSources: []) and cannot catch body inconsistencies; only Stage 3 effectiveness benchmarks (which actually load skill bodies) measure body-alignment impact.
• Any runner that calls a paid API in a loop must have three features before execution: bounded parallelism via --concurrency, resume capability via results-folder scan, and per-run progress logging that surfaces stalls inside one call's duration.
• API keys provided in chat should be considered exposed; rotate immediately after use. Runner enforces this via apiKeyFingerprint() which only logs the last 4 characters.