Single fire-and-forget command that runs the full session learning pipeline: insights → reflect → knowledge → improve → action-candidates → report. All machines run local Phases 1–9 against logs/orchestrator/ and commit derived state. dev-primary additionally runs Phase 10a (cross-machine compilation) and Phase 10 (report). Safe for cron scheduling. Use when session ends, nightly cron fires, or you want to harvest learnings from recent sessions. Replaces running 4 skills manually.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Single fire-and-forget command that runs the full session learning pipeline: insights → reflect → knowledge → improve → action-candidates → report. All machines run local Phases 1–9 against logs/orchestrator/ and commit derived state. dev-primary additionally runs Phase 10a (cross-machine compilation) and Phase 10 (report). Safe for cron scheduling. Use when session ends, nightly cron fires, or you want to harvest learnings from recent sessions. Replaces running 4 skills manually.
dev-primary git pull in Phase 10a picks up all machines' committed derived state.
Pipeline Summary
Run phases sequentially. Non-mandatory phases log failure and continue. Fatal failures
in Phases 1 or 4 set _PIPELINE_EXIT=1. Phase 10 always runs via trap EXIT.
Phase
Name
Mandatory
Short description
1
Insights
✓
Extract skill usage, tool patterns, session-quality signals from all log sources
1b
Drift Detection
dev-primary
Detect python_runtime/file_placement/git_workflow violations in yesterday's log
2
Reflect
—
Invoke /reflect against reflect-history/ and trends/
3
Knowledge
—
Invoke /knowledge; update patterns/
3b
Memory Compaction
—
Compact MEMORY.md + topic files
3c
Memory Curation
—
Promote stable patterns, expire stale entries
4
Improve
✓
Invoke /improve; update skills + rules from candidates/
5
Correction Trends
—
Analyze corrections/ for recurring failure patterns
6
WRK Feedback + Ecosystem
—
WRK quality review + skill usage frequency + ecosystem health
7
Action Candidates
—
Convert candidates/ entries to WRK items
8
Report Review
—
Review learning report for coherence
9
Skill Coverage Audit
weekly
Audit skill coverage via identify-script-candidates.sh + skill-coverage-audit.sh; include tier distribution from skill-tier-report.py (A/B/C/D per config/skills/quality-tiers.yaml)
Sessions are pure multi-agent execution engines — all brain directed at the task.
Analysis, maintenance, and learning are deferred to the nightly run.
In-session
Nightly pipeline
WRK gate check + active-wrk set
All insight/reflect/knowledge/improve runs
Multi-agent implementation
Correction trend analysis
Fast signal capture (hooks write raw signals)
Candidate → WRK auto-creation
/session-start context load
Memory and skill file updates
Cross-review (Codex gate)
Ecosystem health checks
Commit + push
Session archive rsync
Must NOT run standalone during sessions:/insights, /reflect, /knowledge,
/improve, consume-signals.sh heavy analysis, ecosystem-health-check.sh,
session-end-evaluate.sh scoring.
Stop hooks: one hook only, raw write, < 1 second. See WRK-304.
scripts/planning/ — ensemble planning outputs harvested by Planning Quality Loop
Exit-Handoff Boundary
When the user asks to "document and prepare to exit," do not run the heavyweight comprehensive-learning pipeline in-session. Instead, create or update the task-specific durable handoff/report, verify commit/push/clean-state evidence, and leave deeper insights/reflect/knowledge/improve processing to the nightly pipeline. The exit response should be concise: handoff path, pushed commit(s), known dirty-state exceptions, external-action status, and remaining next steps.
For the concrete closeout checklist, use references/exit-handoff-closeout.md. Key requirements: write the handoff under docs/session-handoffs/ when no task-specific location exists, include final clean/sync proof for every touched tier-1 repo, commit and push the handoff unless blocked, inspect any hook-generated dirt before claiming clean state, and explicitly state that no external send/action was performed unless the user approved it.
For travel-planning sessions captured primarily as GitHub issues/comments rather than repo files, use references/github-issue-backed-travel-exit-closeout.md: verify issue/comment URLs live, write the control-repo handoff when the issue repo has no local checkout, stage only the handoff, and report synced-but-dirty control repo state precisely.
Iron Law
No learning pipeline phase (/insights, /reflect, /knowledge, /improve) shall run standalone during an active work session — learning is deferred to the nightly pipeline, always.
Explicit Skill-Library Update Requests
If the user explicitly asks to "review the conversation and update the skill library," do not hide behind the nightly deferral rule. Perform a targeted skill update using skill_manage against the currently loaded class-level skill or the closest existing umbrella. This is a bounded library-maintenance action, not the heavyweight comprehensive-learning pipeline. If a referenced support file is missing, create it immediately under references/ and keep SKILL.md pointing to it.
Use references/conversation-review-skill-update-mode.md as the operating checklist for this mode. Key rules: be active by default, patch loaded/governing class-level skills first, prefer support files under existing umbrellas over narrow one-session skills, and treat user corrections about style/format/workflow as first-class skill-library signals.
After targeted skill-library edits, treat the skill ledger as part of the closeout artifact set. A skill_manage patch/write can create or later append tracked ledger entries such as logs/orchestrator/hermes/skill-patches.jsonl; inspect that dirt after the primary skill commit, commit it separately if it is intentional metadata, then fetch/verify final HEAD == origin/<branch>. Do not claim a clean exit immediately after the first commit if hooks or skill tooling generated follow-up ledger dirt.
Rationalization Defense
Excuse
Reality
"I'll just run a quick /reflect to capture this insight"
/reflect consumes significant context and token budget. The nightly pipeline captures the same signals from hooks and logs — for free.
"The session is almost over, might as well run /improve now"
"Almost over" is when context is most valuable. Defer to nightly; hooks already captured the raw signals.
"This learning will be lost if I don't process it now"
Stop hooks write raw signals in < 1 second. The nightly pipeline processes them. Nothing is lost by deferring.
"The nightly cron might not run tonight"
Fix the cron job, do not work around it by running learning mid-session. Two problems are worse than one.
Red Flags
These phrases signal you are about to violate the Iron Law:
