/memtrace-fleet-resolve
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
$ npx -y skills add syncable-dev/memtrace-public --skill memtrace-fleet-resolve --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/memtrace-fleet-resolve
Context preview
The summary Claude sees to decide when to auto-load this skill.
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
SKILL.md
memtrace-fleet-resolve.SKILL.mdname: memtrace-fleet-resolve
description: "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 and decision loop, see memtrace-fleet-first."
Overview
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.
Submit a verdict (you are the judge)
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:
fleet_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`.
Read your own directive (you are blocked)
fleet_get_escalation({ escalation_id: "01J…", agent_id: "agent-a" })
// → your_directive: wait | proceed | defer | reviewPoll until it's not `wait`. `proceed` = continue; `defer` = stand down and rebase onto the winner; `review` = read `resolution`.
Human paths
- `fleet_list_escalations({repo_id})` — the per-repo "needs human" queue.
- `fleet_resolve_escalation({escalation_id, resolution, winner})` — record a human
decision (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`.
Safety the referee guarantees
- A destructive **removal** (delete/move) is **never** auto-applied — always a human.
- Auto-apply happens only for the clear-safe machine case or ≥2 agents agreeing on
a non-destructive resolution.
- So a wrong verdict degrades to "a human reviews a suggestion," never a silent
bad merge.
Output
`fleet_submit_verdict` and `fleet_get_escalation` both return the referee's decision:
{
"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)
}Read more
name: memtrace-fleet-resolve description: "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 and decision loop, see memtrace-fleet-first."
Overview
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.
Submit a verdict (you are the judge)
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:
fleet_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`.
Read your own directive (you are blocked)
fleet_get_escalation({ escalation_id: "01J…", agent_id: "agent-a" })
// → your_directive: wait | proceed | defer | reviewPoll until it's not `wait`. `proceed` = continue; `defer` = stand down and rebase onto the winner; `review` = read `resolution`.
Human paths
- `fleet_list_escalations({repo_id})` — the per-repo "needs human" queue.
- `fleet_resolve_escalation({escalation_id, resolution, winner})` — record a human
decision (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`.
Safety the referee guarantees
- A destructive **removal** (delete/move) is **never** auto-applied — always a human.
- Auto-apply happens only for the clear-safe machine case or ≥2 agents agreeing on
a non-destructive resolution.
- So a wrong verdict degrades to "a human reviews a suggestion," never a silent
bad merge.
Output
`fleet_submit_verdict` and `fleet_get_escalation` both return the referee's decision:
{
"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)
}Structural memory for AI coding agents. Bi-temporal graph, MCP-native, zero LLM calls. Cursor · Claude Code · Codex · Hermes · VS Code · Windsurf.
Repo: syncable-dev/memtrace-public
Other skills on memtrace-public.
- /memtrace-api-topology
Map API endpoints, outbound HTTP calls, and cross-repo service topology in indexed source code. Use when the user asks about API endpoints, HTTP routes, fetch/client calls, REST surface, service dependencies, cross-repo dependencies, or API topology. Do not use Grep, Glob, rg,
Open skill - /memtrace-change-impact-analysis
Compute what a planned source-code change will break — blast radius, affected processes, cross-repo callers, temporal stability, and Cortex decision-memory constraints — and produce a risk-rated change plan. Use for multi-symbol or multi-part edits, refactors, API changes,
Open skill - /memtrace-cochange
Find files that historically co-change with a target symbol or file, ranked by co-occurrence across git episodes. Use when the user asks about historical coupling, co-change, what changes with this, hidden dependencies, or what else needs to move for source code. Do not use git
Open skill - /memtrace-code-review
Review GitHub pull requests with Memtrace's local graph-backed review engine. Use when the user asks to review a GitHub pull request, run Memtrace code review, post Memtrace review comments, create a PR with a review step, or publish local graph-backed review findings to GitHub.
Open skill - /memtrace-codebase-exploration
Map an indexed source-code repo into a structured overview — scale, communities, central symbols, execution flows, API surface, recent activity. Use when the user wants to explore, understand, onboard to, map, or get an overview of an indexed source-code repo, architecture,
Open skill - /memtrace-continuous-memory
Keep the Memtrace index fresh while editing by watching a repo for live, incremental re-indexing. Use when the user asks to keep Memtrace fresh while editing, watch a repo, enable live or incremental indexing, set up always-on memory (meaning Memtrace index watching, not generic
Open skill

