Improve Phase 1 Collect Gather Session Signals

Phase 1: COLLECT — Gather Session Signals (+6)

Phase 1: COLLECT — Gather Session Signals

Scan these data sources for improvement candidates:

1. .Codex/state/pending-reviews/memory-updates.jsonl — memory candidates
2. .Codex/state/pending-reviews/insights.jsonl — insight candidates
3. .Codex/state/pending-reviews/errors.jsonl — error patterns
4. .Codex/state/pending-reviews/skill-candidates.jsonl — skill gaps
5. .Codex/state/corrections/.recent_edits — correction patterns
6. .Codex/state/accumulator.json — aggregated metrics
7. .Codex/state/patterns/ — patterns from /reflect
8. .Codex/state/pending-reviews/ecosystem-review.jsonl — ecosystem health signals (from stop hook)
9. Current session context — what was learned in THIS session

Phase 2: CLASSIFY — Route Improvements to Targets

Phase 3: ECOSYSTEM REVIEW — Structural Health Assessment

Assess the overall health of ecosystem files and recommend reallocation. This phase is fed by signals from the ecosystem-health-check.sh stop hook AND by scanning the filesystem directly.

Data sources:

1. .Codex/state/pending-reviews/ecosystem-review.jsonl — automated health check signals
2. Direct filesystem scan of .Codex/skills/, .Codex/memory/, .Codex/rules/

Checks performed:

Note on raw count thresholds: Total skill count and per-category count limits (previously 350/50) have been removed. A large, well-indexed skill library is not a problem — stale and unreferenced skills are. Staleness detection requires the knowledge graph (WRK-205: SKILLS_GRAPH.yaml + capabilities:/requires:/see_also: frontmatter) to be effectively maintained. If the index is sparse, staleness signals will be noisy — assess index quality first.

Knowledge graph maintenance (when SKILLS_GRAPH.yaml exists — WRK-205):

• For any skill created or enhanced this session: verify it appears in SKILLS_GRAPH.yaml
• For any new relationship surfaced (A composes B, A requires B, A is alternative to B): add edge to graph
• For each new/enhanced skill: check that existing related skills have it in their related_skills: frontmatter (bidirectional linking); add missing links
• Update last_used timestamp in frontmatter for any skill loaded this session
• Flag skills missing capabilities:, requires:, or see_also: blocks for metadata enrichment

Prerequisite: Graph maintenance only runs when SKILLS_GRAPH.yaml exists at .Codex/skills/. Skip silently if absent — WRK-205 implements the graph.

Proactive skill discovery (when SKILLS_GRAPH.yaml exists — WRK-205 + WRK-215):

Four checks are run to actively surface gaps — not just react to session signals:

Broken-ref scan (runs every session):

1. For each SKILL.md frontmatter: extract all see_also: and requires: values
2. For each value: check that a SKILL.md exists at the referenced path
3. Any broken reference → emit gap candidate to .Codex/state/pending-reviews/skill-candidates.jsonl
4. Dedup: only emit each gap once per 7 days (keyed by target path + date week)

Enhancement priority queue (weekly, via /reflect):

• Rank criteria: see_also: reference frequency (most-referenced-by-other-skills) as primary; last_used recency as tiebreaker
• Output: top-5 enhancement candidates appended to .Codex/state/pending-reviews/skill-candidates.jsonl with type: enhancement

Domain saturation heatmap (on-demand: /improve --scope skills --audit):

• For each skill domain (using WRK-205 category indexes): count skills
• For each domain: count active WRK items with matching module: or tags:
• Compute ratio; flag domains where ratio < 0.5 (more than 2 WRK items per skill)
• Print summary table to stdout; do not write to state files

Prerequisite: All proactive discovery checks skip silently when SKILLS_GRAPH.yaml is absent. Log: "Skill discovery deferred — WRK-205 graph not yet built."

Outputs:

• List of consolidation recommendations (skill merges, memory splits)
• List of new skill candidates (gaps identified from session patterns)
• Responsibility reallocation suggestions (e.g., "move X from memory to rules")
• Knowledge graph updates: edges added, last_used timestamps written, missing links added
• Metrics: total_skills, archived_ratio, avg_category_size, memory_total_lines

Decision rules:

• Consolidation: Only recommend if both skills have been loaded in same session 3+ times
• New skills: Only when pattern seen 3+ sessions AND no existing skill covers it
• Reallocation: Only when content clearly belongs to a different target (e.g., repeated correction → rules, not memory)

Phase 4: GUARD — Safety Checks Before Writing (incl. ecosystem review outputs)

1. Size guards:

   • AGENTS.md: 4KB budget
   • KNOWLEDGE.md: 200-line limit
   • Rules files: 400-line max per file
   • SKILL.md files: 400-line max

2. Dedup check: Read target file, verify improvement doesn't already exist

3. No-clobber rule: If target file has uncommitted changes, skip it

4. Skill lifecycle gates:

   • Create: Only when no existing skill covers the capability (search first)
   • Deprecate: Only when unused for 90+ days AND superseded by another skill
   • Archive: Move deprecated skills to .Codex/skills/_archive/ (never delete)

Phase 5: APPLY — Write Improvements

AGENTS.md: Resource Index scan, Core Rules for patterns confirmed 3+ sessions. Use Edit tool.

Rules files (.Codex/rules/*.md): Add examples from real corrections. Append to sections, never restructure.

Repo memory (.Codex/memory/): Add debugging lessons, tool conventions, API quirks. Use env var placeholders for paths. Create new topic file if section exceeds 10 entries.

Skills (.Codex/skills/**/*.md) — FULL LIFECYCLE:

• Enhance: Add examples, fix instructions from corrections
• Create: New SKILL.md when repeated pattern has no matching skill
• Deprecate: Add deprecation notice when unused 90+ days
• Archive: Move to _archive/ with reason and date

Docs (.Codex/docs/): Update stale references, add missing documentation.

Phase 6: LOG — Record Changes

Write to .Codex/state/improve-changelog.yaml:

• Timestamp, changes list with file/action/diff_summary
• Skills lifecycle metrics: created/enhanced/deprecated/archived counts
• Ecosystem health metrics: total_skills, memory_lines, consolidation_count, reallocation_count
• signals_processed, changes_applied, signals_skipped (with reason)

Phase 7: CLEANUP — Mark Signals Consumed

Move processed signals from pending-reviews/*.jsonl to archive so they aren't reprocessed.