Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Orchestrates the tdmcp Anthropic Connectors Directory (Desktop Extension / MCPB) submission prep — designs the package, writes the privacy page, migrates .dxt→.mcpb, drafts all form answers, and QAs the result. Use for ANY work on the tdmcp directory/marketplace submission, including re-runs after a rejection ("update the submission", "redo the form answers", "re-migrate the bundle", "fix the privacy page", "the directory rejected us"). Drives a 4-agent pipeline (architect → docs-author ∥ bundle-engineer → QA).
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Coordinates four agents to prepare (and re-prepare) tdmcp's submission to the Anthropic Connectors Directory via the Desktop Extension (MCPB) path. Defines who works when; each agent's skill defines how.
TeamCreate/SendMessage are not available here, so run agents as
sub-agents with the Agent tool. For every agent:
subagent_type: "general-purpose", model: "opus"..codex/agents/*.toml
and its required skill(s) first, then do the task. Use this exact mapping:
architect → .codex/agents/submission-architect.toml,
docs-author → .codex/agents/docs-author.toml,
bundle-engineer → .codex/agents/bundle-engineer.toml, and
submission-qa → .codex/agents/submission-qa.toml. The definition file is
the source of truth (reusable next session); the prompt just points at it +
passes the task and the relevant _workspace/ paths.Agent calls in one message
(or run_in_background: true)._workspace/Create _workspace/ at the repo root for inter-agent artifacts:
00_submission-spec.md — architect → everyone01_migration-notes.md — bundle-engineer → QA/human02_form-answers.md — docs-author → QA/human (paste-ready form draft)03_qa-report.md — QA → humanFinal deliverables (privacy page, edited repo files, tdmcp.mcpb) land in the
repo proper. _workspace/ is preserved for audit / re-runs.
_workspace/ is absent → initial run: full pipeline below._workspace/ exists and the user asks to fix/update one part → partial
re-run: re-invoke only the affected agent(s), pass the prior artifacts +
the new feedback (e.g. a directory rejection reason). E.g. "privacy page
rejected" → docs-author only, then QA._workspace/ exists and the user gives fresh inputs → move it to
_workspace_prev/ and start a new initial run.00_submission-spec.md. Everything
downstream depends on it — do not parallelize anything before it finishes.config.ts nav additions,
02_form-answers.md. (Prose lane.)dxt/manifest.json, scripts/build-dxt.mjs,
package.json scripts, scripts/setup.mjs, .dxt→.mcpb text sweep in
existing docs, .github/ release workflow, 01_migration-notes.md. (Code
lane.)03_qa-report.md. If it reports blocking issues, loop back to the owning
agent (partial re-run), then re-run QA on the affected checks.--no-verify, deleting a check). A red QA gate is
the signal to fix, not to suppress.Summarize to the user (in Portuguese): what changed in the repo, the contents of
02_form-answers.md and the QA verdict, and the explicit Needs human before
submit list (support email, privacy contact, cutting a new release so the
.mcpb asset exists, and the actual form click — which only the user can do).
Then offer to address any QA nits or feedback.
_workspace/ → architect spec → privacy page + form draft +
.mcpb migration → npm run docs:build and build:mcpb pass → QA all-PASS
except human-input items → summary lists those items._workspace/ → re-invoke docs-author with that
feedback → QA re-checks the privacy gate only → updated summary.还没有评价。欢迎分享你的使用感受。
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Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
中文简介正在准备中。当前显示原文说明。
Use when a hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
中文简介正在准备中。当前显示原文说明。
Reference for how BDB structures autonomous software engineering work — the seven-node dispatcher graph (Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, Shipping) that /startcycle-graph actually runs. Use when you need the high-level lifecycle framing without inventing your own process.
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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling.
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Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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agenttrail: live map of a multi-agent build in the browser: which plan component is being worked on, by which agent or harness, what is done and what is stuck. Use when a multi-agent pipeline starts (/startcycle, /startcycle-graph, /teamwork-preview) or after a plan-canvas approve, when the user asks to see what the agents are doing ("live map", "build board", "who is running now"), or by yourself whenever a multi-agent run is under way — no user prompt needed.
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