advise
Critical thinking analysis - validates alignment, challenges assumptions, identifies risks
Resume mode for /implement command
$ npx -y skills add akaszubski/autonomous-dev --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
/implement-resumeContext preview
What this command does when you run it.
Resume mode for /implement command
name: implement-resume description: Resume mode for /implement command version: 1.0.0 user-invocable: false user_facing: false
Invoke the implementer agent to resume the interrupted batch from the last checkpoint.
Codifies the state-recovery sequence the coordinator MUST follow when `/implement --resume <id>` targets a SINGLE-RUN id (not a batch). Prior to Issue #1149 this path was improvised: a coordinator that detected a cached pre-validated plan in STEP 3 would dispatch the planner to "ratify" the plan rather than re-plan, but the legitimacy of that shortcut depended on session-gap, HEAD-state, and alignment conditions that no spec captured. This section is the codified protocol.
This protocol applies when ARGUMENTS contain `--resume <id>` AND `<id>` matches one of the single-run forms:
Batch resume (`--resume batch-*`) follows the existing STEP R1–R4 protocol below and is NOT subject to this section.
All four checks MUST pass in this order. If any check fails, the coordinator MUST NOT dispatch any agent from the cached/pre-validated artifacts; the failed-check resolution governs what happens instead.
1. **HEAD hash check** (Issue #1149):
2. **Staging clean check**:
3. **Alignment re-verification**:
4. **Run lock available**:
Even when all pre-resume checks pass, the cached pre-validated plan MAY still be too stale to use directly. Compute the **session gap** as `time.time() - os.path.getmtime(plan_file)` where `plan_file` is the cached plan referenced in `/tmp/pipeline_state_<run_id>.json`.
Once the pre-resume checks pass and the staleness gate has classified the resume, restore the per-run state:
1. Load completed agents via `get_completed_agents(session_id, run_id=run_id)` from `pipeline_completion_state.py`. 2. Restore `PIPELINE_STATE_FILE` env var to point at `/tmp/pipeline_state_<run_id>.json`. 3. Identify the last-completed agent (or the last successful HARD GATE) in the completions list. 4. Continue the pipeline at the step AFTER that agent or gate. Do NOT re-dispatch agents that already have a completion entry — the prompt-integrity gate and the ordering gate will block legitimate work if a completed agent is re-run without a fresh prompt context.
The following behaviors are explicitly forbidden during single-run resume. They are the failure modes the protocol is codifying against; coordinator judgment alone has historically chosen the wrong branch in each case.
A harness that wraps Claude Code with enforcement, specialist agents, and alignment gates to deliver consistent, production-grade software engineering outcomes.
Repo: akaszubski/autonomous-dev
Critical thinking analysis - validates alignment, challenges assumptions, identifies risks
Comprehensive quality audit - code quality, documentation, coverage, security
Autonomous experiment loop — hypothesize, modify, benchmark, commit or revert
Create GitHub issue with automated research (--quick for fast mode)
Autonomous queue drainer — picks the top /triage cluster, applies safety gates, drains via /implement --issues, pushes, deploys.