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Parallel root-cause analysis for bugs and failing tests. Use when a test fails, a bug is reported, or behavior is unexplained — dispatches sub-agents to form and validate hypotheses in isolated worktrees.
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Orchestrates safe, large-scale structural changes across a codebase — symbol renames, API migrations, pattern standardizations, layer restructurings. Enumerates all affected sites, groups them into dependency layers via the DAG executor, applies changes in parallel per layer with worktree isolation, and verifies behavioral preservation at each layer boundary before proceeding. Exits immediately on first regression, surfacing the exact site, diff, and behavioral delta before any commit.
Set up a scheduled headless afk run that pushes a summary to Telegram. Use when the user wants to automate a recurring task via the afk daemon scheduler (cron) with push-notified results.
Dispatches two sub-agents in parallel to gather external and local context for the current task.
Dispatch a parallel adversarial verifier wave after any high-stakes sub-agent investigation (code reviews, audits, findings reports, large refactors) — or whenever a sub-agent asserts a claim with high-confidence language (confident, certain, clearly, ≥80%), since confidence is a trigger, not a verdict. Shadow verifiers independently re-derive 2–3 key claims from scratch using tool calls only, returning CONFIRMED/REFUTED/UNVERIFIABLE, and flag disagreements before the user acts. Use when sub-agent output will drive decisions, file changes, commits, or external side-effects.
Discovers incidental complexity, duplication, and dead code in a codebase and produces a ranked, behavior-preserving reduction plan — optionally applying safe changes. Dispatches four parallel read-only discovery lenses (clone detection, dead code, complexity hotspots, wrong abstraction), synthesizes into a prioritized reduction plan, and gates apply mode behind /refactor and a hard test check.
Dispatches parallel dimension agents across a diff, PR (URL or number), commit SHA, branch, staged changes, or patch file — covering security, correctness, api-compat, test-coverage, and perf-observability — synthesizes findings by severity, and emits a merge recommendation. Use when changes are ready for review before merge. Read-only: this skill analyzes and reports only — it never edits files, commits, pushes, comments on a PR, or modifies the PR description.
SOC 職業分類に基づく
| name | diagnose |
| description | Parallel root-cause analysis for bugs and failing tests. Use when a test fails, a bug is reported, or behavior is unexplained — dispatches sub-agents to form and validate hypotheses in isolated worktrees. |
| context | fork |
Gather context: read the failing test or bug description, relevant error output, and recent git changes. If no failing test exists yet, write a minimal reproducer test (or identify a concrete verification command) before proceeding — hypotheses need a pass/fail signal to validate against. Dispatch two sub-agents in parallel — one to search the codebase for code paths involved in the failure (subagent_type: research-agent, read-only), and one to check recent commits and diffs that could have introduced the regression (subagent_type: general-purpose — requires Bash for git log/git diff/git show). When both return, synthesize findings into 2–4 ranked hypotheses, each with a specific code location and proposed cause.
For each hypothesis, first create a dedicated git worktree with the worktree tool (action: "create", one per hypothesis), then dispatch a sub-agent with its cwd set to that worktree's absolute path. This is how afk isolates a sub-agent to its own tree — the agent tool has NO isolation parameter, so parallel agents that omit cwd all inherit the same working tree and would contaminate each other's speculative fixes and test runs, invalidating the pass/fail attribution. Each sub-agent applies a minimal speculative fix, runs the test or verification command, then runs the broader related test suite to check for regressions. Run all hypothesis-testing agents in parallel, each in its own worktree; after collecting a result, remove that worktree with the worktree tool (action: "remove"). Collect results: which fixes passed, which didn't, and any regressions surfaced by the broader suite.
Report the validated root cause (the hypothesis whose fix passed), the speculative fix diff, and regression status from the broader test run. If no hypothesis passes, synthesize what was learned and form a second round of hypotheses. If the user approves the fix, apply it to the main worktree.
