以 AI 机器人身份加入视频会议,提供语音、虚拟形象与屏幕共享四种模式。
编程
project-execution
试用Executes implementation plans with progress tracking, checkpoint validation, and quality gates. Use after planning is complete and tasks are ready to implement
它能做什么
**Night Market Skill** — ported from claude-night-market/attune. For the full experience with agents, hooks, and commands, install the Claude Code plugin.
技能文档
Night Market Skill — ported from claude-night-market/attune. For the full experience with agents, hooks, and commands, install the Claude Code plugin.
When To Use
- After planning phase completes
- Ready to implement tasks
- Need systematic execution with tracking
- Want checkpoint-based validation
- Executing task lists with dependencies
- Monitoring progress and velocity
When NOT To Use
- No implementation plan exists (use
Skill(attune:project-planning)first) - Still planning or designing (complete planning phase before execution)
- Single isolated task (execute directly without framework overhead)
- Exploratory coding or prototyping (use focused development instead)
Integration
With superpowers:
- Uses
Skill(superpowers:executing-plans)for systematic execution - Uses
Skill(superpowers:systematic-debugging)for issue resolution - Uses
Skill(superpowers:verification-before-completion)for validation - Uses
Skill(superpowers:test-driven-development)for TDD workflow
Without superpowers:
- Standalone execution framework
- Built-in checkpoint validation
- Progress tracking patterns
Execution Framework
Pre-Execution Phase
Actions:
- Load implementation plan
- Validate project initialized
- Check dependencies installed
- Review task dependency graph
- Identify starting tasks (no dependencies)
Validation:
- ✅ Plan file exists and is valid
- ✅ Project structure initialized
- ✅ Git repository configured
- ✅ Development environment ready
Task Execution Loop
For each task in dependency order:
1. PRE-TASK
- Verify dependencies complete
- Review acceptance criteria
- Create feature branch (optional)
- Set up task context
2. IMPLEMENT (TDD Cycle)
- Write failing test (RED)
- Implement minimal code (GREEN)
- Refactor for quality (REFACTOR)
- Repeat until all criteria met
3. VALIDATE
- All tests passing?
- All acceptance criteria met?
- Code quality checks pass?
- Documentation updated?
4. CHECKPOINT
- Mark task complete IMMEDIATELY (do NOT batch)
- Update execution state
- Report progress
- Identify blockers
Task Completion Discipline: Always call TaskUpdate(taskId: "X", status: "completed") right after finishing each task—never defer completions to end of session.
Verification: Run pytest -v to verify tests pass.
Post-Execution Phase
Actions:
- Verify all tasks complete
- Run full test suite
- Check code quality metrics
- Generate completion report
- Prepare for deployment/release
Terminal Phase Notice
This is the final phase of the attune workflow. No auto-continuation occurs after execution completes. The workflow terminates here. Unlike brainstorming, specification, and planning phases, execution does NOT auto-invoke any subsequent phase.
Task Execution Pattern
TDD Workflow
RED Phase:
# Write test that fails
def test_user_authentication():
user = authenticate("user@example.com", "password")
assert user.is_authenticated
# Run test → FAILS (feature not implemented)
Verification: Run pytest -v to verify tests pass.
GREEN Phase:
# Implement minimal code to pass
def authenticate(email, password):
# Simplest implementation
user = User.find_by_email(email)
if user and user.check_password(password):
user.is_authenticated = True
return user
return None
# Run test → PASSES
Verification: Run pytest -v to verify tests pass.
REFACTOR Phase:
# Improve code quality
def authenticate(email: str, password: str) -> Optional[User]:
"""Authenticate user with email and password."""
user = User.find_by_email(email)
if user is None:
return None
if not user.check_password(password):
return None
user.mark_authenticated()
return user
# Run test → STILL PASSES
Verification: Run pytest -v to verify tests pass.
Checkpoint Validation
Quality Gates:
- [ ] All acceptance criteria met
- [ ] All tests passing (unit + integration)
- [ ] Code linted (no warnings)
- [ ] Type checking passes (if applicable)
- [ ] Documentation updated
- [ ] No regression in other components
Verification: Run pytest -v to verify tests pass.
Automated Checks:
# Run quality gates
make lint # Linting passes
make typecheck # Type checking passes
make test # All tests pass
make coverage # Coverage threshold met
Verification: Run pytest -v to verify tests pass.
Progress Tracking
Execution State
Save to .attune/execution-state.json:
{
"plan_file": "docs/implementation-plan.md",
"started_at": "2026-01-02T10:00:00Z",
"last_checkpoint": "2026-01-02T14:30:22Z",
"current_sprint": "Sprint 1",
"current_phase": "Phase 1",
"tasks": {
"TASK-001": {
"status": "complete",
"started_at": "2026-01-02T10:05:00Z",
"completed_at": "2026-01-02T10:50:00Z",
"duration_minutes": 45,
"acceptance_criteria_met": true,
"tests_passing": true
},
"TASK-002": {
"status": "in_progress",
"started_at": "2026-01-02T14:00:00Z",
"progress_percent": 60,
"blocker": null
}
},
"metrics": {
"tasks_complete": 15,
"tasks_total": 40,
"completion_percent": 37.5,
"velocity_tasks_per_day": 3.2,
"estimated_completion_date": "2026-02-15"
},
"blockers": []
}
Verification: Run pytest -v to verify tests pass.
Progress Reports
Daily Standup:
# Daily Standup - [Date]
## Yesterday
- ✅ [Task] ([duration])
- ✅ [Task] ([duration])
## Today
- 🔄 [Task] ([progress]%)
- 📋 [Task] (planned)
## Blockers
- [Blocker] or None
## Metrics
- Sprint progress: [X/Y] tasks ([%]%)
- [Status message]
Verification: Run the command with --help flag to verify availability.
Sprint Report:
# Sprint [N] Progress Report
**Dates**: [Start] - [End]
**Goal**: [Sprint objective]
## Completed ([X] tasks)
- [Task list]
## In Progress ([Y] tasks)
- [Task] ([progress]%)
## Blocked ([Z] tasks)
- [Task]: [Blocker description]
## Burndown
- Day 1: [N] tasks remaining
- Day 5: [M] tasks remaining ([status])
- Estimated completion: [Date] ([delta])
## Risks
- [Risk] or None identified
Verification: Run the command with --help flag to verify availability.
Blocker Management
Blocker Detection
Common Blockers:
- Failing tests that can't be fixed quickly
- Missing dependencies or APIs
- Technical unknowns requiring research
- Resource unavailability
- Scope ambiguity
Systematic Debugging
When blocked, apply debugging framework:
- Reproduce: Create minimal reproduction case
- Hypothesize: Generate possible causes
- Test: Validate hypotheses one by one
- Resolve: Implement fix or workaround
- Document: Record solution for future
Escalation
When to escalate:
- Blocker persists > 2 hours
- Requires architecture change
- Impacts critical path
- Needs stakeholder decision
Escalation format:
## Blocker: [TASK-XXX] - [Issue]
**Symptom**: [What's happening]
**Impact**: [Which tasks/timeline affected]
**Attempted Solutions**:
1. [Solution 1] - [Result]
2. [Solution 2] - [Result]
**Recommendation**: [Proposed path forward]
**Decision Needed**: [What needs to be decided]
Verification: Run the command with --help flag to verify availability.
Quality Assurance
Definition of Done
Task is complete when:
- ✅ All acceptance criteria met
- ✅ All tests written and passing
- ✅ Code reviewed (self or peer)
- ✅ Linting passes with no warnings
- ✅ Type checking passes (if applicable)
- ✅ Documentation updated
- ✅ No known regressions
- ✅ Deployed to staging (if applicable)
Testing Strategy
Test Pyramid:
**Verification:** Run `pytest -v` to verify tests pass.
/\
/E2E\ Few, slow, expensive
/------\
/ INT \ Some, moderate speed
/----------\
/ UNIT \ Many, fast, cheap
Verification: Run the command with --help flag to verify availability.
Per Task:
- Unit tests: Test individual functions/classes
- Integration tests: Test component interactions
- E2E tests: Test complete user flows (for user-facing features)
Velocity Tracking
Burndown Metrics
Track daily:
- Tasks remaining
- Story points remaining
- Days left in sprint
- Velocity (tasks or points per day)
Formulas:
**Verification:** Run `pytest -v` to verify tests pass.
Velocity = Tasks completed / Days elapsed
Estimated completion = Tasks remaining / Velocity
On track? = Estimated completion <= Sprint end date
Verification: Run the command with --help flag to verify availability.
Velocity Adjustments
If ahead of schedule:
- Pull in stretch tasks
- Add technical debt reduction
- Improve test coverage
- Enhance documentation
If behind schedule:
- Identify causes (blockers, underestimation)
- Reduce scope (drop low-priority tasks)
- Increase focus (reduce distractions)
- Request help or extend timeline
Related Skills
Skill(superpowers:executing-plans)- Execution framework (if available)Skill(superpowers:systematic-debugging)- Debugging (if available)Skill(superpowers:test-driven-development)- TDD (if available)Skill(superpowers:verification-before-completion)- Validation (if available)Skill(attune:mission-orchestrator)- Full lifecycle orchestration
Related Agents
Agent(attune:project-implementer)- Task execution agent
Related Commands
/attune:execute- Invoke this skill/attune:execute --task [ID]- Execute specific task/attune:execute --resume- Resume from checkpoint
Mission Report
At mission completion, produce a Mission Report using the
template from references/mission-report.md. The report documents:
- Mission identification: Links to brief, spec, plan
- Duration: Start, end, total time
- Outcome: success | partial | failed
- Delivered artifacts: Files created/modified/deleted
- Decisions: Key choices with rationale
- Validation evidence: Tests, reviews, demos
- Follow-ups: Recommended next steps
See references/mission-report.md for the full template and
example reports for successful, partial, and failed missions.
Examples
See /attune:execute command documentation for complete examples.
相关技能
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把自然语言描述转为结构化 JSON,并由 mcp-diagram-generator MCP 服务生成 Draw.io、Mermaid 或 Excalidraw 图表文件。
在本地磁盘以分类纯 Markdown 文件保存需要长期留存的事实,与智能体内置记忆并存。
从 AdMapix API 拉取广告创意、应用、榜单和收入预估等数据,原样返回结构化 JSON。
编写、调试与调优 Playwright 测试,涵盖定位器策略、追踪诊断与 CI 友好的超时配置。
athola 的更多技能
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Applies KISS, YAGNI, and SOLID principles for clean code with reduced complexity
Configures pre-commit hooks for linting, type checking, formatting, and testing
Guide creating Claude Code hooks with security-first design. Use for validation and enforcement
Analyze and improve the improvement process. Use for detecting regressions and meta-optimization
Selects and routes to the right architecture paradigm