task
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.
git clone --depth 1 https://github.com/codeaholicguy/ai-devkit /tmp/task && cp -r /tmp/task/skills/task ~/.claude/skills/taskSKILL.md
# Task Progress Tracking 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: ```bash 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. ## Core idea - **One task per work item.** Create it once; advance its `phase` field as work moves through the lifecycle or debug workflow. - **`<id>` can be a task name.** Every command below accepts the task name in place of a task id, resolving to the latest non-terminal task. Prefer `<task-name>` so agents do not track task ids. - **Choose stable names.** For lifecycle work, use the feature key as the task name. For debugging or review work, choose a short kebab-case task name. - **Emit at checkpoints, not streaming.** Phase transitions, task toggles, immediate next-step changes, fresh evidence, blockers discovered/resolved. A handful of calls per session. - **Sequence mutations.** Never run task mutation commands in parallel for the 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. - **Attribution is explicit.** Identify self once, then pass actor flags on mutation commands. ## Identify self 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. ## Canonical commands 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>`. ```bash # 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 task close <task-name> completed --agent <agent-name> --agent-type <agent-type> --pid <pid> --session <sessionId> --json ``` ## When to emit (by workflow) - **dev-lifecycle** - real read probe first; `create` at start
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 find another active agent, read its recent context, send it information, or request information back.
AI DevKit · Manage running AI agents with ai-devkit agent commands. Use when an agent needs to identify itself, list agents, start workers, inspect agent detail, assign work, group agents, resume sessions, stop agents, or delegate work to other agents.
AI DevKit · Supervise multi-agent workflows over repeated passes: poll progress, unblock waiting agents, coordinate dependencies, relay outputs, resolve conflicts, and verify completion. Use only for ongoing multi-agent coordination, not one-off list/detail/send/start/kill actions.
AI DevKit · Use when the user asks to brainstorm, ideate, generate ideas, expand options, challenge ideas, pressure-test ideas, compare concepts, narrow choices, name something, plan content angles, explore strategy, evaluate product ideas, technical approaches, experiments, or decisions.
AI DevKit · Compare implementation with design and requirements docs to ensure alignment.
AI DevKit · Pre-push code review against design docs.
AI DevKit · Safe git commit workflow for AI coding agents. Use when the user asks to commit, prepare a commit, stage changes, create a PR-ready checkpoint, or finish work with a conventional commit while avoiding unrelated user changes.
AI DevKit · Design phase guidance for reviewing feature design against requirements. Use when the user wants to validate architecture, review design docs, resolve design trade-offs, or run dev-lifecycle phase 3.