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nm-attune-project-executionnm attune 项目执行

Agent Skill

nm-attune-project-execution 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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3,444

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下载量

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OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:nm-attune-project-execution(nm attune 项目执行)
来源仓库:https://github.com/athola/nm-attune-project-execution
安装命令:
openclaw skills install nm-attune-project-execution
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install nm-attune-project-execution

简介

通过进度跟踪和质量门控制实施计划的执行过程。

  • 适合在 OpenClaw 中推进项目落地并确保交付质量时使用。
  • 核心能力是按阶段验证成果并控制风险。nm-attune-project-execution 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 使用 clawhub 安装,需配合具体任务规范使用。
  • 注意检查是否会修改文件或触发自动化流程。

SKILL.md

name
project-execution
description
|
version
1.8.2
triggers
metadata
{"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/attune", "emoji": "\�\�"}}
source
claude-night-market
source_plugin
attune
Night Market Skill — ported from claude-night-market/attune. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

Table of Contents

Project Execution Skill

Execute implementation plan systematically with checkpoints, validation, and progress tracking.

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:

  1. Load implementation plan
  2. Validate project initialized
  3. Check dependencies installed
  4. Review task dependency graph
  5. 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:

  1. Verify all tasks complete
  2. Run full test suite
  3. Check code quality metrics
  4. Generate completion report
  5. 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:

  1. Reproduce: Create minimal reproduction case
  2. Hypothesize: Generate possible causes
  3. Test: Validate hypotheses one by one
  4. Resolve: Implement fix or workaround
  5. 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.

Troubleshooting

Common Issues

Command not found Ensure all dependencies are installed and in PATH

Permission errors Check file permissions and run with appropriate privileges

Unexpected behavior Enable verbose logging with --verbose flag

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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OpenClaw

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按下载量换算793

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执行命令

安装流程涉及命令执行,可能通过 openclaw skills install nm-attune-project-execution 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

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