.agents/skills/finish-work/SKILL.md
Pre-commit quality checklist covering lint, typecheck, tests, code-spec sync, API changes, database migrations, cross-layer verification, and manual testing. Blocks commit if infra or cross-layer specs lack executable depth. Use when code is written and tested but not yet committed, before submitting changes, or as a final review before git commit.
npx skillsauth add basui01/agentflow finish-workInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Before submitting or committing, use this checklist to ensure work completeness.
Timing: After code is written and tested, before commit
# Must pass
pnpm lint
pnpm type-check
pnpm test
pnpm lint passes with 0 errors?pnpm type-check passes with no type errors?console.log statements (use logger)?x! operator)?any types?Code-Spec Docs:
.trellis/spec/backend/ need updates?
.trellis/spec/frontend/ need updates?
.trellis/spec/guides/ need updates?
Key Question:
"If I fixed a bug or discovered something non-obvious, should I document it so future me (or others) won't hit the same issue?"
If YES -> Update the relevant code-spec doc.
If this change touches infra or cross-layer contracts, this is a blocking checklist:
Block Rule:
If infra/cross-layer changed but the related spec is still abstract, do NOT finish. Run $update-spec manually first.
If you modified API endpoints:
If you modified database schema:
If the change spans multiple layers:
# 1. Code checks
pnpm lint && pnpm type-check
# 2. View changes
git status
git diff --name-only
# 3. Based on changed files, check relevant items above
| Oversight | Consequence | Check | |-----------|-------------|-------| | Code-spec docs not updated | Others don't know the change | Check .trellis/spec/ | | Spec text is abstract only | Easy regressions in infra/cross-layer changes | Require signature/contract/matrix/cases/tests | | Migration not created | Schema out of sync | Check db/migrations/ | | Types not synced | Runtime errors | Check shared types | | Tests not updated | False confidence | Run full test suite | | Console.log left in | Noisy production logs | Search for console.log |
Development Flow:
Write code -> Test -> $finish-work -> git commit -> $record-session
| |
Ensure completeness Record progress
Debug Flow:
Hit bug -> Fix -> $break-loop -> Knowledge capture
|
Deep analysis
$finish-work - Check work completeness (this skill)$record-session - Record session and commits$break-loop - Deep analysis after debuggingDelivery includes not just code, but also documentation, verification, and knowledge capture.
Complete work = Code + Docs + Tests + Verification
development
Multi-agent pipeline orchestrator that plans and dispatches parallel development tasks to worktree agents. Reads project context, configures task directories with PRDs and jsonl context files, and launches isolated coding agents. Use when multiple independent features need parallel development, orchestrating worktree agents, or managing multi-agent coding pipelines.
development
Captures executable contracts and coding knowledge into .trellis/spec/ documents after implementation, debugging, or design decisions. Enforces code-spec depth for infra and cross-layer changes with mandatory sections for signatures, contracts, validation matrices, and test points. Use when a feature is implemented, a bug is fixed, a design decision is made, a new pattern is discovered, or cross-layer contracts change.
development
Initializes an AI development session by reading workflow guides, developer identity, git status, active tasks, and project guidelines from .trellis/. Classifies incoming tasks and routes to brainstorm, direct edit, or task workflow. Use when beginning a new coding session, resuming work, starting a new task, or re-establishing project context.
development
Records completed work progress to .trellis/workspace/ journal files after human testing and commit. Captures session summaries, commit hashes, and updates developer index files for future session context. Use when a coding session is complete, after the human has committed code, or to persist session knowledge for future AI sessions.