agent-communication
AI DevKit · Exchange information with active Codex, Claude Code, and other AI agents using ai-devkit agent list, detail, and send. Use when an agent needs to…
AI DevKit · Track dev-lifecycle / structured-debug progress on a durable task with the ai-devkit task CLI. Use to record phase, progress, next step, blockers, and validation evidence.
$ npx -y skills add codeaholicguy/ai-devkit --skill task --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/taskContext preview
The summary Claude sees to decide when to auto-load this skill.
AI DevKit · Track dev-lifecycle / structured-debug progress on a durable task with the ai-devkit task CLI. Use to record phase, progress, next step, blockers, and validation evidence.
name: task description: AI DevKit · Track dev-lifecycle / structured-debug progress on a durable task with the ai-devkit task CLI. Use to record phase, progress, next step, blockers, and validation evidence.
Record development progress on a durable task: phase, progress, next step, blockers, and validation evidence.
Requires the optional task command. Use `npx ai-devkit@latest` for task and agent commands. Before recording task events, run a real read probe:
npx ai-devkit@latest task list --json # or, when a task name is known: npx ai-devkit@latest task list --name <task-name> --json
Only treat task tracing as available when the read probe exits 0. If it fails, continue without task logging and include the failed command plus stderr/stdout summary in the final report. Do not block the user's work just because optional task tracing is unavailable or unusable.
moves through the lifecycle or debug workflow.
place of a task id, resolving to the latest non-terminal task. Prefer `<task-name>` so agents do not track task ids.
name. For debugging or review work, choose a short kebab-case task name.
immediate next-step changes, fresh evidence, blockers discovered/resolved. A handful of calls per session.
same task. Each mutation reads the current task snapshot and writes it back; parallel writes can clobber snapshot fields even though events append. Run create/assign/phase/next/progress/evidence/blocker/artifact/close commands one at a time, then read back with `show --events --json` when the final state matters.
mutation commands.
Use `agent-management` when attribution is needed:
1. Run the `agent-management` self-identification workflow with `npx ai-devkit@latest agent list --json`. 2. Match the current agent entry from that list. Prefer an exact session match when available; otherwise use the unambiguous entry for the current project/worktree. 3. Build actor flags from the matched entry: `--agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId>`. Map JSON fields directly: `name` -> `--agent`, `type` -> `--agent-type`, `pid` -> `--pid`, and `sessionId` -> `--session`. 4. If identity is ambiguous, do not guess. Continue task logging without actor flags rather than fabricating attribution. 5. Add `--agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId>` to every mutation command once known. If a task already exists, run `npx ai-devkit@latest task assign <task-name> --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json` once so the task snapshot has current ownership. 6. If actor identity is unknown, run the same mutation commands without the four actor flags.
When self identity is known, add all four actor flags to every mutation command: `--agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId>`.
# Create the task once (capture taskId from --json if needed) npx ai-devkit@latest task create --title "<title>" --name <task-name> --phase requirements --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json # If the task already exists, assign current ownership once when known npx ai-devkit@latest task assign <task-name> --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json # Mark real work as active after create/resume npx ai-devkit@latest task status <task-name> active --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json # Advance phase as the lifecycle moves on npx ai-devkit@latest task phase <task-name> implementation --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json # Progress (use --text; positional text is ignored) npx ai-devkit@latest task progress <task-name> --text "Implementing task CLI" --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json # Next step npx ai-devkit@latest task next <task-name> "Run validation" --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json # Blockers npx ai-devkit@latest task status <task-name> blocked --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json npx ai-devkit@latest task blocker <task-name> add "Waiting for review" --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json npx ai-devkit@latest task blocker <task-name> resolve <blocker-id> --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json npx ai-devkit@latest task status <task-name> active --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json # Validation evidence - record after a fresh verify/tdd/test run npx ai-devkit@latest task evidence <task-name> --passed --command "npm test" --exit-code 0 --summary "tests passed" --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json # Reference an artifact (never copies the file) npx ai-devkit@latest task artifact <task-name> docs/ai/testing/foo.md --kind test-report --description "Testing notes" --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json # Read current status / list npx ai-devkit@latest task show <task-name> --json npx ai-devkit@latest task list --name <task-name> --json # Close at lifecycle end npx ai-devkit@latest tas
The control plane for AI coding agents. AI DevKit gives Claude Code, Codex CLI, Gemini CLI, opencode, Pi, Cursor, GitHub Copilot, Devin, and other coding agents one local-first operating layer: one config, one console, local memory retrieval, cross-agent
Repo: codeaholicguy/ai-devkit
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