Lisa Debrief Apply

Debrief Apply: $ARGUMENTS

Read the triage document at $ARGUMENTS and persist every Accepted candidate learning to its destination.

This skill is intentionally single-agent — there is no team. Routing is deterministic given the disposition column. Spawning sub-agents would only add latency.

Input

A path or URL to a Debrief triage document produced by lisa-debrief. The document is expected to follow the structure that skill produces — a header, an anomalies section, candidate-learning rows grouped by category, and a source-map appendix.

Pre-flight

1. Verify the doc exists and parses. If the file cannot be read or the expected sections are missing, stop and report — do not guess.
2. Confirm dispositions exist. If every row is unmarked, stop and ask the human to triage first. A pristine doc is a no-op, not an error to silently swallow.
3. Identify the destination map. Read the project's .lisa.config.json (or stack defaults) for: edge-case checklist file (default: plugins/src/base/rules/intent-routing.md's Edge Case Brainstorm sub-flow), the committed learnings ledger path (resolve it — never hardcode — via resolveProjectLearningsFile from @codyswann/lisa/learnings: the learnings.file override, else the default .lisa/PROJECT_LEARNINGS.md), tracker for new tickets. The three knowledge categories (recurring gotcha, process friction, convention drift) all land in the ledger now; machine-local auto-memory and host rules (.agents/rules/) / AGENTS.md are no longer knowledge destinations (see Ledger persistence).

Routing rules

For every row marked Accept:

For every row marked Reject or Defer: no action. Defer is a no-op for apply but worth surfacing in the run summary — the human may want to revisit at the next debrief.

Ledger persistence (knowledge categories)

The three knowledge categories — recurring gotcha, process friction, and convention drift — all persist to the committed learnings ledger through the SAME executable contract the learner uses (@codyswann/lisa/learnings). This is the single governed, budgeted, contract-validated, team- and cloud-visible knowledge surface. Never hand-edit the ledger markdown — every write goes through the contract, or it is a bug.

For each such Accepted row:

1. Resolve the ledger path (never hardcode). Use resolveProjectLearningsFile from @codyswann/lisa/learnings — the learnings.file override, else the default .lisa/PROJECT_LEARNINGS.md (a cold path, never an auto-loaded rules tree):

   LEARNINGS_FILE=$(node -e 'import("@codyswann/lisa/learnings").then(async m => { const c = await m.readProjectConfig(process.cwd()); console.log(m.resolveProjectLearningsFile(c)); })')
   

2. Consolidation check (mandatory before writing). Parse the existing entries with parseLearningsFile and look for one related to the row (same failure class, overlapping topic, or near-duplicate wording). Then write through the contract:

   • Related entry found → consolidate with the exact parsed versions: persistConsolidatedLearning(projectRoot, entry, { supersede: [{ id: <related id>, fingerprint: <related fingerprint> }], onStaleSupersede: targets => report(targets) }). Every stamp is checked together inside the writer lock. If any is stale, the writer removes none, safely appends the new fingerprint, and reports the mismatch. Never append a near-duplicate sibling by choice — a sibling is a bug; the stale append is the deliberate concurrent-writer preservation path.
   • No related entry → append via persistLearningEntry(projectRoot, entry).

3. Entry mapping (eight fields).

   • fingerprint — debrief- + the first 12 hex characters of sha1(normalized_rule + "\n" + sorted_provenance.join("\n")), where the rule is lowercased with whitespace collapsed and provenance is sorted by codepoint. Compute it mechanically, never estimate it.
   • id — initially id = fingerprint. On an exact stamped consolidation the writer carries forward the deterministic primary target id; report the returned id in the run summary.
   • rule / why — from the row's Summary and the category-specific guidance in the routing table above (≤240 chars, ≤2 lines).
   • provenance — the triage-doc row's evidence links. This is the same evidence link that doubles as the idempotency fingerprint below, so the row's provenance is what a later re-apply scans for.
   • first_learned = last_confirmed = today (ISO date; on consolidation keep the superseded entry's earliest first_learned).
   • confidence = high. A human marking the row Accept is corroboration — an independent human judgement that the learning is real — so a debrief-accepted entry starts higher than the learner's single-occurrence auto-capture (which defaults to low). A duplicate fingerprint fails before mutation. The writer re-asserts the entry and token budgets; an over-budget failure means consolidate harder or drop, never truncate by hand.

Machine-local memory is no longer a knowledge destination. Auto-memory (project_*.md, MEMORY.md) remains available only for the assistant's personal collaboration notes — it is invisible to cloud runs and to teammates, so it can never hold shared project knowledge. AGENTS.md is human-authored agent operating instruction — it is the source of truth, and CLAUDE.md is only a one-line @AGENTS.md pointer — and the host-rules directory .agents/rules/ is durable human-authored guidance; apply never writes to any of the three for these categories.

Idempotency

apply is safe to re-run. Each Accepted row carries an evidence link that doubles as a fingerprint. Before writing, check whether the destination already cites that fingerprint:

• Knowledge categories (gotcha, friction, drift) → ledger. Parse the ledger once with parseLearningsFile from @codyswann/lisa/learnings and scan the entries' provenance for the row's evidence link. If any entry's provenance already contains it, the row is already persisted — skip the write. This replaces the old scattered-file greps (memory files, host rules, AGENTS.md): provenance in the single governed ledger is now the one fingerprint surface.
• Other categories keep their existing destination check (the tracker for the ticket marker, the PRD for the defect comment, intent-routing.md for the edge-case citation).

If the fingerprint is already present, skip the write and note the row as already-applied in the run summary. This lets the human triage a doc incrementally (mark a few, run apply, mark more, run apply again) without producing duplicates.

Updating the triage doc

After each Accepted row is persisted, replace its [ ] Accept checkbox with [x] Applied — <one-line summary of what was written>. This makes the triage doc itself the audit log of what was acted on. If a write fails (e.g., tracker is unreachable), mark the row [!] Apply failed — <reason> and continue with the rest. Never abort the whole run because one row failed.

Output

A run summary printed to the user:

Applied <n> learnings:
  <n> edge cases → intent-routing.md
  <n> gotchas → ledger (<entry-id1>, <entry-id2>, ...)
  <n> friction → ledger (<entry-id1>, ...)
  <n> convention drift → ledger (<entry-id1>, ...)
  <n> tooling gaps (project) → <tracker> (<key1>, <key2>, ...)
  <n> tooling gaps (harness) → upstream Lisa (<issue-url1>, ...)
  <n> decomposition infidelity → upstream Lisa (<issue-url1>, ...)
  <n> PRD defects → PRD comments (<prd-link1>, ...)
  <n> missing tool access → <tracker> (<key1>, ...)
Skipped:
  <n> rejected, <n> deferred, <n> already-applied
Needs reclassification:
  <n> uncategorized (retag to one of the eight categories and re-run apply)
Failed:
  <n> (see <path> for details)
Triage doc updated in place: <path>

If anything is written to a tracker, suggest the human commit the local file changes (the learnings ledger, intent-routing) when ready — apply does not commit.