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Use this skill to review current checkout authority, run-bundle drift, legacy worktree cleanup evidence, and cleanup readiness.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Baseado na classificação ocupacional SOC
| name | worktree-health |
| description | Use this skill to review current checkout authority, run-bundle drift, legacy worktree cleanup evidence, and cleanup readiness. |
Use the command packet before applying this skill's workflow:
python3 tools/agent_tools/skill_tool_commands.py show --skill worktree-health --format text
Execute the required and task-matching conditional commands that the packet prints.
agents/skills/worktree-health.md.reports/agents/.active_run, task_authority.yaml, and team_manifest.yaml before judging current checkout write authority.WORKTREE_SCOPE.md and any legacy worktree action log as cleanup evidence only; do not treat them as scope authority for new work.python3 tools/agent_tools/worktree_scope_lint.py --current only when legacy cleanup is in scope.git status --short --branch, git diff --name-only, and git worktree list --porcelain.git branch --show-current and git worktree list --porcelain as diagnostics; branch/worktree creation is routed by agents/canonical/CODEX_WORKFLOW.md Branch Reuse Default and branch_worktree_guard.py, while this skill checks only the recorded branch_creation_reason=<reason> or worktree_creation_reason=<reason> evidence.notes/guardrails/README.md and notes/failures/README.md when drift, cleanup, or carry-over risk is not obvious.bash tools/docs/check_worktree_scopes.sh when legacy drift or stale worktrees are possible.Use when agent-side working philosophy, interaction lessons, task retrospectives, repeated routing misses, missed skill invocation, or recurrence-prevention feedback should be logged without mixing them into user preferences.
Use when exploring, refactoring, or choosing an algorithm under proof obligations; builds JIT-canonical IR, lemma dependency graphs, algorithmic blocker frontiers, and algorithm-change guidance before handing terminal proof work to formal-proof-workflow.
Use when designing, implementing, reviewing, or diagnosing numerical optimization, solvers, preconditioners, convergence, gradients, Jacobians, Hessians, KKT conditions, tolerances, or optimization benchmarks; fixes the mathematical and validation contract before code or experiment changes.
Use when a change needs oracle/spec-risk classification or resilient, adversarial static test design, including behavior contracts, oracle choice, property/metamorphic candidates, mutation adequacy, or brittle-test diagnosis.
Use when updating AgentCanon itself, refreshing a vendored vendor/agent-canon submodule pin, repairing AgentCanon root runtime views, applying AgentCanon update TODOs, or routing local AgentCanon source commits through a proper AgentCanon branch and PR before parent pin updates.
Use when accumulated AgentCanon eval evidence is missing, stale, or failing; runs registered eval producers, validates eval family accumulation, and stores evidence through the log archive instead of hand-writing reports.