comprehensive-learning — Session Learning Pipeline
Single fire-and-forget skill running Phases 1–9 on all machines and Phases 10a+10
(cross-machine compilation + report) on dev-primary only.
Full phase specs: references/pipeline-detail.md — read it when you need
signal sources, extraction rules, candidate formats, or state file details.
Mode-Based Routing
MACHINE=$(hostname -s 2>/dev/null || hostname | cut -d. -f1 | tr '[:upper:]' '[:lower:]')
case "$MACHINE" in
dev-primary) CL_MODE="full" ;;
dev-secondary) CL_MODE="contribute" ;;
licensed-win-1|licensed-win-2) CL_MODE="contribute" ;;
*) CL_MODE="contribute" ;;
esac
Cross-Machine Data Flow
| Machine | Commits | Notes |
|---|
| dev-secondary | candidates/, corrections/, patterns/, session-signals/ | Open-source CFD/dev |
| licensed-win-1 | candidates/, corrections/, session-signals/, patterns/ | OrcaFlex/ANSYS |
| licensed-win-2 | candidates/ | Windows; no AI CLIs |
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) |
| 10a | Cross-Machine Compile | full only | git pull + aggregate from all machines |
| 10 | Report | always | Write report (trap EXIT) |
For phase details (signal sources, extraction rules, YAML formats):
→ read references/pipeline-detail.md
Session Design: Lean by Default
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.
Scheduling
Crontab entry (dev-primary, 22:00 nightly):
0 22 * * * cd /mnt/local-analysis/workspace-hub && \
bash scripts/cron/comprehensive-learning-nightly.sh \
>> .claude/state/learning-reports/cron.log 2>&1
Script: scripts/cron/comprehensive-learning-nightly.sh
— git pull is a hard gate; each rsync is independently || true.
Related
- workstations skill: machine registry and
cron_variant fields
- WRK-299: implementation tracking | WRK-304: Stop hook cleanup | WRK-305: signal emitters
- WRK-303: Ensemble planning → Planning Quality Loop (in references/pipeline-detail.md)
/insights, /reflect, /knowledge, /improve: individual pipeline stagesscripts/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:
- "let me quickly run /insights before we continue"
- "I should capture this learning now"
- "running /improve won't take long"
- "the session is winding down anyway"
