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Get Started Free →Resolve a Class C fleet decision: submit your verdict as an agent judge (fleet_submit_verdict), poll your own directive (fleet_get_escalation), see the needs-human queue (fleet_list_escalations), or record a human decision (fleet_resolve_escalation). Use when the user says 'a decision is waiting' or 'who should proceed', when you are handed a mediation_request, when acting as a mediator between two agents, or when a human chooses a winner in the dashboard. For the underlying conflict-class model
.claude/skills/syncable-dev-memtrace-fleet-resolve/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | -26% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-13 | ✗→✓ | ▲ Improved | -32% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -47% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 49% | 0% |
The tools that resolve a Class C conflict. The judging is done by the user's own agents (no API keys): an agent reads the bundle and submits a verdict; the daemon is the deterministic referee that decides the outcome and routes it back.
You were handed a mediation_request (from fleet_record_episode). Read every agent's assignment in it and decide on merit — including against your own change:
jsoncfleet_submit_verdict({ escalation_id: "01J…", agent_id: "agent-a", verdict: { "kind": "recommend", "winner": "agent-b", "rationale": "wider contract; rebase the fix onto it", "confidence": 0.8 } }) // kinds: reconcile {merge_plan} | recommend {winner, rationale, confidence} | defer_to_human {question}
Full parameter spec for every Memtrace tool: references/mcp-parameters.md (bundled at the memtrace-skills plugin root).
The response tells you the outcome (auto_apply | human_confirm | human_required | pending) and your_directive.
jsoncfleet_get_escalation({ escalation_id: "01J…", agent_id: "agent-a" }) // → your_directive: wait | proceed | defer | review
Poll until it's not wait. proceed = continue; defer = stand down and rebase onto the winner; review = read resolution.
fleet_list_escalations({repo_id}) — the per-repo "needs human" queue.fleet_resolve_escalation({escalation_id, resolution, winner}) — record a humandecision (pick which agent proceeds) and clear it. Prefer the agent-judge path; use this for genuine human/product calls.
Every verdict and resolution persists to the escalation record as the audit trail — review any decision later via fleet_get_escalation / fleet_list_escalations.
a non-destructive resolution.
bad merge.
fleet_submit_verdict and fleet_get_escalation both return the referee's decision:
jsonc{ "outcome": "human_confirm", // auto_apply | human_confirm | human_required | pending "your_directive": "wait", // wait | proceed | defer | review "resolution": "agent-b proceeds; agent-a rebases onto the wider contract" // set once the escalation is resolved (directive = review) }
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 20,083 | 7,873 | -61% | 1 | 1 | 0% | 368 | 1,078 | +193% | 0 | 0 | — |
case-02 | fail→fail | 8,357 | 4,952 | -41% | 1 | 1 | 0% | 283 | 904 | +219% | 0 | 0 | — |
case-03 | fail→fail | 12,334 | 6,760 | -45% | 1 | 1 | 0% | 506 | 1,164 | +130% | 0 | 0 | — |
case-04 | fail→fail | 11,376 | 8,045 | -29% | 1 | 1 | 0% | 1,847 | 1,276 | -31% | 0 | 0 | — |
case-05 | fail→fail | 10,063 | 9,352 | -7% | 1 | 1 | 0% | 1,688 | 1,281 | -24% | 0 | 0 | — |
case-06 | pass→pass | 10,135 | 3,084 | -70% | 1 | 1 | 0% | 1,543 | 1,218 | -21% | 0 | 0 | — |
case-07 | fail→pass | 18,604 | 2,100 | -89% | 1 | 1 | 0% | 1,401 | 1,031 | -26% | 0 | 0 | — |
case-08 | fail→fail | 9,320 | 5,044 | -46% | 1 | 1 | 0% | 405 | 992 | +145% | 0 | 0 | — |
case-09 | fail→pass | 6,888 | 2,836 | -59% | 1 | 1 | 0% | 975 | 1,046 | +7% | 0 | 0 | — |
case-10 | pass→pass | 8,726 | 1,393 | -84% | 1 | 1 | 0% | 1,396 | 893 | -36% | 0 | 0 | — |
case-11 | pass→pass | 7,623 | 4,112 | -46% | 1 | 1 | 0% | 1,091 | 1,359 | +25% | 0 | 0 | — |
case-12 | pass→pass | 8,846 | 2,378 | -73% | 1 | 1 | 0% | 1,234 | 960 | -22% | 0 | 0 | — |
case-13 | fail→pass | 12,820 | 4,135 | -68% | 1 | 1 | 0% | 1,887 | 1,286 | -32% | 0 | 0 | — |
case-14 | fail→pass | 16,092 | 3,594 | -78% | 1 | 1 | 0% | 2,398 | 1,270 | -47% | 0 | 0 | — |
case-15 | fail→pass | 6,503 | 4,248 | -35% | 1 | 1 | 0% | 930 | 1,388 | +49% | 0 | 0 | — |
case-16 | pass→pass | 8,682 | 4,026 | -54% | 1 | 1 | 0% | 1,192 | 1,414 | +19% | 0 | 0 | — |
case-17 | fail→fail | 9,210 | 4,359 | -53% | 1 | 1 | 0% | 1,298 | 1,441 | +11% | 0 | 0 | — |
case-18 | fail→pass | 13,450 | 3,247 | -76% | 1 | 1 | 0% | 1,848 | 1,203 | -35% | 0 | 0 | — |
case-19 | fail→pass | 6,673 | 2,883 | -57% | 1 | 1 | 0% | 1,024 | 1,124 | +10% | 0 | 0 | — |
case-20 | fail→pass | 13,164 | 6,992 | -47% | 1 | 1 | 0% | 2,373 | 1,872 | -21% | 0 | 0 | — |
case-21 | fail→fail | 8,724 | 5,436 | -38% | 1 | 1 | 0% | 1,457 | 1,681 | +15% | 0 | 0 | — |
case-22 | fail→pass | 13,217 | 5,502 | -58% | 1 | 1 | 0% | 2,453 | 1,482 | -40% | 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 15 counted toward the lift figure. The other 7 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 +41 percentage points is the difference between those two pass rates over the 15 comparable cases.
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.