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Get Started Free →Seal current session with knowledge candidate review and DAG progression
.claude/skills/catlog22-maestro-session-seal/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 87% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 90% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 56% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 60% | 0% |
<required_reading> @~/.maestro/workflows/run-mode.md @~/.maestro/workflows/codex-run-mode.md </required_reading>
If any required file above was not expanded into context by the host, or its content is no longer in context, Read it explicitly before executing any step.
<purpose> Complete the current Session after verifying all Runs are immutable and terminal, review the durable knowledge candidate backlog, and recommend the next dep-ready Session from the DAG.
Run completion already stages accepted decisions, locked constraints, and explicit maestro knowledge stage entries. This command reviews those receipts; it does not re-extract the same artifacts or write project knowledge through a second path. </purpose>
<context> $ARGUMENTS -- optional session ID and flags.
Flags: | Flag | Effect | Default | |------|--------|---------| | --session <id> | Target session (slug or full ID) | active_session_id | | -y / --yes | Auto mode — skip confirmations | false | | --skip-knowledge | Leave candidate backlog pending and continue sealing | false | </context>
<execution>
Note: maestro-next suggests session-seal when 'Tests green + active session'. This command additionally requires verify/review gates (or W002 if absent). Both conditions should be met for clean seal.
--session flag or active_session_idmaestro session status --session {session_id} --json — verify status is open (a completed/archived Session is terminal)review step first")maestro knowledge review {session_id} --json. Treat its Run ledgers, reconciliation policies, diversified matches, and candidate IDs as authoritative; do not rescan outputs to recreate candidates. Use --refresh only when the review reports missing or stale source receipts.cited, validated, and contradicted are explicit Run relations.review_required candidates cannot be promoted.--skip-knowledge, report the pending/promoting/review-required/suppressed counts and continue. The backlog and reconciliation receipts remain durable after seal.maestro knowledge review {session_id} --resolve <candidate-id> --as duplicate|related|conflict|supersede|unique [--target <knowledge-id>] --reason "<reason>". A target must come from that candidate's evidence-backed matches.request_user_input: question: "以下知识候选项值得晋升到项目知识库吗?" options:
maestro knowledge promote {session_id} --allmaestro knowledge promote {session_id} --candidate <candidate-id> for each selection (comma-separated compatibility remains supported)-y may run --all, which promotes all eligible candidates (observed-only emits a warning) and skips review-required and suppressed candidates. It MUST NOT auto-resolve a candidate without explicit user selection.--as supersede and then promote it; promotion creates the successor and links the evolution chain. For coexisting valid rules, confirm related or conflict as appropriate. Never direct-write a candidate that was already promoted successfully.orchestration_revision from the retained run-response/1.2 state.maestro session complete command from run-mode.md, supplying the exact session_id, --participant, --actor, --request-id, --reason, --expected-orchestration-revision, and --json.state.json.sessions[] — find sessions that became dep-ready (all depends_on sealed) question: "Session {slug} 已 sealed。推荐激活下一个 session: {next-slug},是否确认?" options:
active_session_id to selected session</execution>
<completion>
=== SESSION SEALED ===
Session: {session_id}
Knowledge: {promoted_count} promoted, {pending_count} pending, {review_required_count} review required, {suppressed_count} suppressed
Next dep-ready: {next_slug or "none (DAG complete)"}
--- STATUS ---
Status: DONE| Condition | Suggestion | |-----------|-----------| | Next session activated | step analyze — open a v3 Session (maestro session open "<goal>" --id YYYYMMDD-analyze-{next-slug} --chain analyze --participant {p} --actor {a} --request-id {r} --reason "<reason>" --json → fenced maestro run next --session {session_id} ... --json), or route via /maestro-next | | Knowledge candidates pending | maestro knowledge review {session_id} | | Knowledge health review needed | /maestro-knowledge audit | </completion>
<error_codes> | Code | Severity | Condition | Recovery | |------|----------|-----------|----------| | E001 | error | Session not found | Check state.json.sessions[] | | E002 | error | Session already completed | Nothing to do | | E003 | error | Active runs exist | Complete or seal pending runs first | | E004 | error | Critical gates failed | Run verify/review to resolve | | W001 | warning | No knowledge candidates found | Proceed to seal | | W002 | warning | No verify/review run in session — gate check skipped | Consider running verify before seal | | W003 | warning | Candidate backlog left pending | Review later with maestro knowledge review {session_id} | | W004 | warning | Reconciliation review remains unresolved | Seal may continue; promotion stays blocked until maestro knowledge review --resolve | </error_codes>
<success_criteria>
maestro knowledge reviewmaestro knowledge promotemaestro session complete (transition receipt verified; status completed)</success_criteria>
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 8,279 | 5,529 | -33% | 1 | 1 | 0% | 1,490 | 2,160 | +45% | 0 | 0 | — |
case-02 | fail→fail | 10,947 | 5,726 | -48% | 1 | 1 | 0% | 1,760 | 2,152 | +22% | 0 | 0 | — |
case-03 | fail→fail | 9,053 | 5,373 | -41% | 1 | 1 | 0% | 1,494 | 2,106 | +41% | 0 | 0 | — |
case-04 | fail→pass | 12,916 | 4,367 | -66% | 1 | 1 | 0% | 1,808 | 2,455 | +36% | 0 | 0 | — |
case-05 | fail→fail | 9,785 | 7,141 | -27% | 1 | 1 | 0% | 1,375 | 2,332 | +70% | 0 | 0 | — |
case-06 | fail→fail | 12,364 | 5,623 | -55% | 1 | 1 | 0% | 1,766 | 2,616 | +48% | 0 | 0 | — |
case-07 | pass→pass | 5,321 | 4,699 | -12% | 1 | 1 | 0% | 846 | 2,578 | +205% | 0 | 0 | — |
case-08 | fail→pass | 9,230 | 4,810 | -48% | 1 | 1 | 0% | 1,369 | 2,559 | +87% | 0 | 0 | — |
case-09 | fail→pass | 9,225 | 4,232 | -54% | 1 | 1 | 0% | 1,264 | 2,407 | +90% | 0 | 0 | — |
case-10 | fail→pass | 12,444 | 4,388 | -65% | 1 | 1 | 0% | 1,575 | 2,462 | +56% | 0 | 0 | — |
case-11 | fail→pass | 12,030 | 5,713 | -53% | 1 | 1 | 0% | 1,658 | 2,654 | +60% | 0 | 0 | — |
case-12 | fail→pass | 12,467 | 6,266 | -50% | 1 | 1 | 0% | 1,876 | 2,727 | +45% | 0 | 0 | — |
case-13 | fail→pass | 12,811 | 2,369 | -82% | 1 | 1 | 0% | 1,710 | 2,176 | +27% | 0 | 0 | — |
case-14 | pass→pass | 10,473 | 4,238 | -60% | 1 | 1 | 0% | 1,719 | 2,464 | +43% | 0 | 0 | — |
case-15 | pass→pass | 11,903 | 4,689 | -61% | 1 | 1 | 0% | 1,715 | 2,626 | +53% | 0 | 0 | — |
case-16 | fail→fail | 11,688 | 4,169 | -64% | 1 | 1 | 0% | 1,595 | 2,457 | +54% | 0 | 0 | — |
case-17 | fail→pass | 10,499 | 4,084 | -61% | 1 | 1 | 0% | 1,575 | 2,422 | +54% | 0 | 0 | — |
case-18 | pass→pass | 9,510 | 6,590 | -31% | 1 | 1 | 0% | 1,651 | 2,890 | +75% | 0 | 0 | — |
case-19 | fail→pass | 11,559 | 6,342 | -45% | 1 | 1 | 0% | 1,671 | 2,823 | +69% | 0 | 0 | — |
case-20 | fail→pass | 8,010 | 3,735 | -53% | 1 | 1 | 0% | 1,192 | 2,468 | +107% | 0 | 0 | — |
case-21 | fail→pass | 9,385 | 2,295 | -76% | 1 | 1 | 0% | 1,370 | 2,117 | +55% | 0 | 0 | — |
case-22 | fail→pass | 9,359 | 2,641 | -72% | 1 | 1 | 0% | 1,296 | 2,182 | +68% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 18 counted toward the lift figure. The other 4 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +55 percentage points is the difference between those two pass rates over the 18 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.