Improve

Scope. In a solution repository, use its own evidence. In a clone of github/awesome-copilot, the evidence lives in solution folders: find recent sessions that loaded foundry-agentic-loop with session_store_sql, confirm the folders with the user, and read their records.

Budget. Present proposals within about 20 minutes. Start from the distilled records below. Use session history only to confirm or quantify a candidate cluster, and never read raw transcripts (events.jsonl) end to end.

• Flight records: .agentic-loop/runs/*.json, with phases, rounds, gate status, issues, lessons, durations and outcome.
• Lessons: docs/LOOP.md entries and their status.
• Workflow history: dynamic_workflows_manage with runs, filtered to foundry-agentic-loop-workflow, then inspect-run for each failed, blocked or slow run.
• Session history: session_store_sql for recent Agentic Loop sessions in scope: failed tools, retries, user corrections, questions asked and manual fixes after a blocked pause. Time-bound every query and use LIMIT.
• Current sources: the foundry-agentic-loop skill (SKILL.md, references/, assets/ and evals/evals.json) and the workflow lib/*.mjs.

2. Analyze

Cluster findings by root cause, not symptom. Look for:

• recurring gate failures and phases that need many rounds or run long
• issues fixed by hand in Chat
• user corrections
• adopted lessons not yet captured in a skill
• instructions that never influence outcomes (token cost)

Rank clusters by frequency × impact. Keep a cluster only when it has evidence from at least two runs, or from one verified severe failure.

Find target sections with grep instead of reading whole files. With more than three clusters, draft them in parallel, one subagent per cluster, each returning the fields in §3.

3. Propose

For each cluster, give:

• the evidence (run IDs, phases, quotes)
• the target file and section
• a minimal diff
• the expected benefit
• a regression check: an existing eval or a new eval case

Prefer tightening or deleting text over adding it. Keep SKILL.md lean and put details in references. Never weaken approvals, RBAC, safety or evidence gates, and never add secrets or tenant-specific values. Ask a fresh-context reviewer subagent to challenge each proposal against the evals, and drop those that fail. Re-verify only the claims the reviewer disputes, in one batch.

4. Apply after approval

• In a clone of github/awesome-copilot: edit skills/foundry-agentic-loop/ and extensions/foundry-agentic-loop-* directly. Validate with npm run skill:validate, npm run plugin:validate and node --test extensions/foundry-agentic-loop-*/tests/*.test.mjs, then offer a pull request.
• In a solution repository: plugin files are replaced on update, so write project overrides instead. Copy the whole skill folder to .github/skills/foundry-agentic-loop/ and edit it there; the workflow ignores a copy without SKILL.md, references/sdd.md and the four phase guides. Workflow subagents read project skills first, so overrides apply to the next run. Change workflow code only through an upstream pull request.
• Afterwards: reload with extensions_reload when extensions change, then measure with the bundled runner scripts/evals.mjs (Node 20+, Copilot CLI). It runs each case offline in a fresh workspace with the candidate skill as a project override, grades it with a second call and writes .agentic-loop/evals/<timestamp>/results.json and summary.md.
  1. Select cases without loading evals/evals.json: node <skill>/scripts/evals.mjs index --target references/<file>.md (also --mode, --capability). Add or update cases for new behavior, with mode, capabilities, targets, risk and, for stateful cases, a fixture under evals/fixtures/.
  2. Run node <skill>/scripts/evals.mjs run --target references/<file>.md --base <base-commit> (or --ids 4,29); in a solution repository, --base can be the installed plugin skill folder. Failing cases that are unchanged since the base also run against the base skill.
  3. Pass only with exit code 0: no regressions, and every new or changed case passes. Fix regressions and rerun only those, once. Report failures that also fail at the base as pre-existing instead of iterating.
  4. Exit code 2 means the runner is unavailable (Copilot CLI missing or signed out): stop, report “evals unavailable”, and leave lessons unchanged.
• Lessons: mark matching docs/LOOP.md lessons adopted only after a passing regression run, and validated only after a later real run confirms the benefit. Write only the current workspace’s docs/LOOP.md; for lessons from other solution folders, report the status change for their owners. Offer to upstream generalizable improvements.