一键导入
implementation-planning
Use when you have specifications or requirements for multi-step implementation tasks requiring comprehensive documentation for handoff
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Use when you have specifications or requirements for multi-step implementation tasks requiring comprehensive documentation for handoff
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Use when working on a Python task and unsure which specialist to load - routes to type-system, mypy-resolution, project-tooling, delinting, testing, async, scientific-computing, ML-workflows, debugging-and-profiling, or Textual-TUI specialists based on symptoms
Use when starting any UX/UI design task — visual design, IA, interaction, accessibility (WCAG 2.2), user research across mobile, web, desktop, game, and AI surfaces, plus first-principles audience-derived needs analysis. Routes to 11 specialist sheets.
Use when a Claude is taking **standing ownership** of a software product across many sessions — discovery, strategy, specs, and value validation — deciding *what to build, why, for whom,* and *whether it worked*, with continuity, decision provenance, and an authority boundary that escalates irreversible or outward-facing actions to the human owner. Owns the product disciplines: opportunity assessment (JTBD, problem validation, business case), vision/strategy/positioning and intent-roadmapping, PRDs with falsifiable acceptance criteria, delivery orchestration and acceptance, product-value metrics and experimentation, and the product anti-pattern catalog. Orchestrates rather than reimplements: routes sequencing/flow/WSJF to `/axiom-program-management`, plans to `/axiom-planning`, solution/architecture to `/axiom-solution-architect`, research method to `/lyra-ux-designer`. Do **not** load to build one feature, choose an architecture, or manage delivery flow for already-decided work.
This skill should be used when the user asks to "track work", "create an issue", "find something to work on", "what should I work on next", "triage bugs", "close an issue", "check what's blocked", "plan a milestone", "review sprint progress", "coordinate agents", or when working in a project that uses filigree for issue tracking. Provides workflow patterns, team coordination protocols, and operational guidance for the filigree issue tracker.
Use when designing a system that spans more than one process, machine, or failure domain and a partition, crash, or duplicate must not silently corrupt state. Use when you see split-brain, lost writes under partition, duplicate processing, stuck or half-applied sagas, retry storms, dual writes drifting apart, or "works in staging, melts under load." Architecture-level — how to design correctness under partial failure, not how to deploy it. Single-service API internals → `axiom-web-backend`; CI/CD and ops → `axiom-devops-engineering`; replay/seed determinism → `axiom-determinism-and-replay`; broker/event-sourcing mechanics → the event-driven pack.
Use when designing a system whose past behaviour must be recoverable as a fact — RL training substrates, multi-agent simulations, deterministic game engines, replay-debuggable services, multiplayer lockstep, or any pipeline where "I cannot reproduce that bug" is unacceptable. Use when teams disagree about what "snapshot", "seed", or "replay" mean across modules. Use when cross-machine or cross-process determinism is required, or a regulator will ask "what was the input at tick T?". Architecture-level — how to design a deterministic system. For verifying an existing simulation against known patterns, use `/check-determinism` from yzmir-simulation-foundations instead.
| name | implementation-planning |
| description | Use when you have specifications or requirements for multi-step implementation tasks requiring comprehensive documentation for handoff |
Write comprehensive implementation plans assuming the engineer is skilled but unfamiliar with the codebase and project conventions. Document everything they need: which files to touch for each task, complete code examples, testing commands, documentation to reference, and verification steps. Break work into atomic, committable units following DRY, YAGNI, and TDD principles.
Announce at start: "I'm using the implementation-planning skill to create the implementation plan."
Context: Plans should be created in a dedicated worktree for isolation.
Save plans to: docs/plans/YYYY-MM-DD-<feature-name>.md
Use implementation-planning when:
Don't use for:
Each step is one atomic action:
Examples of atomic steps:
NOT atomic:
Every plan MUST start with this header:
# [Feature Name] Implementation Plan
> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task.
**Goal:** [One sentence describing what this builds]
**Architecture:** [2-3 sentences about approach]
**Tech Stack:** [Key technologies/libraries]
**Prerequisites:**
- [Any setup needed before starting]
- [Dependencies that must be installed]
---
Each task follows this template:
### Task N: [Component Name]
**Files:**
- Create: `exact/path/to/file.py`
- Modify: `exact/path/to/existing.py:123-145` (lines if known)
- Test: `tests/exact/path/to/test.py`
- Docs: `docs/path/to/reference.md` (if relevant)
**Step 1: Write the failing test**
```python
def test_specific_behavior():
# Arrange
input_data = create_test_input()
# Act
result = function(input_data)
# Assert
assert result == expected_output
Why this test: [Explain what behavior this verifies]
Step 2: Run test to verify it fails
Run: pytest tests/path/test.py::test_specific_behavior -v
Expected output:
FAILED - NameError: name 'function' is not defined
Step 3: Write minimal implementation
def function(input_data):
"""Brief docstring explaining purpose."""
# Minimal code to make test pass
return expected_output
Why minimal: [Explain what's deliberately omitted for now]
Step 4: Run test to verify it passes
Run: pytest tests/path/test.py::test_specific_behavior -v
Expected output:
PASSED
Step 5: Commit
git add tests/path/test.py src/path/file.py
git commit -m "feat: add specific feature
- Implements [specific behavior]
- Tests verify [specific condition]
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>"
Definition of Done:
## Quality Standards
**Every task MUST include:**
1. **Exact file paths** - No "update the config file" without path
2. **Complete code** - No "add validation logic" without showing the code
3. **Exact commands** - Full command with flags, not "run tests"
4. **Expected output** - What success/failure looks like
5. **Definition of Done** - Checklist for task completion
**Code examples must be:**
- Complete and runnable
- Include necessary imports
- Show actual logic, not pseudocode
- Have minimal comments (code should be self-explanatory)
## Cross-Skill References
**Reference skills using requirement markers, NOT @ syntax:**
```markdown
# ✅ GOOD: Explicit requirement marker
**REQUIRED SUB-SKILL:** Use superpowers:test-driven-development
**BACKGROUND:** Understanding superpowers:systematic-debugging helps with troubleshooting
# ❌ BAD: @ syntax force-loads and burns context
@skills/test-driven-development/SKILL.md
| Mistake | Impact | Fix |
|---|---|---|
| Vague tasks ("add validation") | Engineer doesn't know what to implement | Include complete validation code in plan |
| Missing file paths | Time wasted searching codebase | Exact path for every file touched |
| "Run tests" without specifics | Wrong tests run, or none at all | Exact command with file::function and expected output |
| Generic commit messages | Hard to review, poor history | Follow conventional commits with context |
| Skipping test failure verification | False confidence (test might not work) | Always verify RED before implementing GREEN |
| Combining multiple actions in one step | Unclear what to commit, harder to debug | Each step = one atomic action |
| Assuming context knowledge | Engineer unfamiliar with codebase stuck | Document which files define types, where utilities live |
| Incomplete code examples | "Figure it out" wastes time | Complete, runnable code in plan |
These thoughts mean incomplete plan:
| Excuse | Reality |
|---|---|
| "They'll figure out the details" | Details ARE the plan. Document them. |
| "File path is obvious from context" | Not obvious to someone unfamiliar. State it explicitly. |
| "Standard validation, doesn't need code" | What's standard to you is unclear to others. Show the code. |
| "Test command is straightforward" | Exact command eliminates ambiguity. Specify it. |
| "This step is quick, combine with next" | Atomic steps = atomic commits. Keep separate. |
If you catch yourself using these rationalizations, STOP and add the missing details.
For high-risk or high-complexity work, validate the plan against codebase reality before any execution:
RECOMMENDED SUB-SKILL: Run /review-plan docs/plans/<filename>.md (plan-review skill).
It spawns reality/architecture/quality/systems reviewers and returns one of three verdicts:
Skip review only for low-risk, low-complexity plans; reserve it for work where a wrong assumption is expensive.
When /review-plan returns CHANGES_REQUESTED, treat it as a revision loop, not a one-shot gate:
Severity × Likelihood × Reversibility)./review-plan on the revised plan. Repeat until the verdict is APPROVED or APPROVED_WITH_WARNINGS.Only then proceed to Execution Handoff.
After the plan is saved (and reviewed, for high-risk work), offer execution choice:
"Plan complete and saved to docs/plans/<filename>.md. Two execution options:
1. Subagent-Driven (this session) - I dispatch fresh subagent per task with code review between tasks for fast iteration
2. Parallel Session (separate) - Open new session with executing-plans for batch execution with checkpoints
Which approach?"
If Subagent-Driven chosen:
If Parallel Session chosen:
DRY (Don't Repeat Yourself):
YAGNI (You Aren't Gonna Need It):
TDD (Test-Driven Development):
Frequent Commits: