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。
直接命令不会经过审查 Prompt;运行前请先检查来源。
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想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
依 ticket/issue 產出初版實作 — 解析需求、從最新 develop 切出符合命名規範的分支、寫出實作、跑既有測試與 lint、conventional commit,然後交棒給 /simplify。Use when the user types /develop, or asks to start implementing a ticket, issue, or feature request end-to-end from requirement to first commit.
Put a website behind a Cloudflare Access (Zero Trust) login gate, or remove one, entirely from the CLI — no dashboard GUI. Use when the user wants to password/email-protect a hostname, gate a Cloudflare Pages or Workers site, restrict a site to specific emails, set up Zero Trust Access, or asks about "cf-gate". Manages Access applications and allow-email policies via the Cloudflare API using a token in the skill's .env. Note: wrangler does NOT manage Access — this uses the Cloudflare REST API directly.
Curate a GitHub repository's wiki (the separate repo.wiki.git) into a coherent, tightly written set of pages: Home, Introduction (project + features), Roadmap, Gotchas/Lessons, Tech Debt, and an Architecture page taught through the book *Head First Software Architecture* — with mermaid diagrams, in the repo's own language and style, then commit and push. Use when the user wants to create, update, curate, or document a GitHub repo's wiki; write or refresh wiki pages; add an architecture / design page to a wiki; enable a wiki; or asks for "/wiki-git". Handles both first-time wikis and updates to existing ones, and can fan out across many repos.
基于 SOC 职业分类
正在显示 SKILL.md
| name | research-plan-implement |
| description | Three-phase RPI workflow (Research, Plan, Implement). Use for non-trivial code changes. |
A disciplined three-phase workflow that trades speed for clarity, predictability, and correctness. Each phase has a validation gate that must pass before proceeding.
Core principle: Never let AI write code without first researching the codebase and producing a validated plan. A 1-page plan gives 10x leverage over reviewing 1,000 lines of AI code.
Copy this checklist and track progress:
- [ ] Phase 1: Research (FAR validated)
- [ ] Phase 2: Plan (FACTS validated)
- [ ] Phase 3: Implement (verified)
Goal: Explore the codebase, find the right files. Make ZERO modifications.
Use sub-agents (Task tool with Explore type) to read broadly without polluting the main context. Sub-agents return only compressed facts.
For each finding, record:
Every finding must pass all three:
| Check | Question | Fail Example |
|---|---|---|
| Factual | Is this from actual code, not assumption? | "There's probably a config file somewhere" |
| Actionable | Does it include specific file path + line number? | "The auth module handles this" |
| Relevant | Is it directly related to the task? | Documenting unrelated utility functions |
Template for research summary:
## Research Findings for: {task description}
### Finding 1: {short title}
- **File**: `src/auth/middleware.ts:42-67`
- **What**: `validateToken()` checks JWT expiry and role claims
- **Why**: We need to extend this to support API key auth
### Finding 2: {short title}
- **File**: `src/types/auth.ts:15-23`
- **What**: `AuthContext` interface defines `user`, `token`, `roles`
- **Why**: Must add `apiKey` field here
### FAR Check
- [x] All findings reference actual code with paths and line numbers
- [x] No assumptions or guesses included
- [x] Every finding directly relates to the task
Do NOT proceed to Phase 2 until FAR passes.
Goal: Produce a step-by-step plan where each step has code-level specificity.
A plan without code snippets is just a "feeling" -- it has no execution power.
For each step:
### Step N: {action verb} + {what}
**File**: `path/to/file.ts:line-range`
**Change**: {precise description of what to add/modify/remove}
**Code sketch**:
```typescript
// Before (current):
function validateToken(token: string): AuthContext { ... }
// After (planned):
function validateAuth(credential: string | ApiKey): AuthContext { ... }
Verify: {how to confirm this step succeeded}
Scope: Changes ONLY validateToken signature and body. Does NOT touch callers yet.
### FACTS Validation Gate
Every step must pass all five:
| Check | Question | Fail Example |
|-------|----------|-------------|
| **F**easible | Can this be done in the current environment? | "Migrate to a new framework" mid-task |
| **A**tomic | Does it do exactly one thing? | "Update auth and refactor tests" |
| **C**lear | Are file names, line numbers, and code snippets present? | "Update the relevant files" |
| **T**estable | Is there a concrete verification step? | "Make sure it works" |
| **S**coped | Is it clear what changes and what doesn't? | No mention of boundaries |
**FACTS checklist**:
```markdown
### FACTS Validation
- [ ] F: Every step is achievable in current environment
- [ ] A: Each step has exactly one objective
- [ ] C: Every step has file path, line numbers, and code sketch
- [ ] T: Every step has a specific verification command or check
- [ ] S: Every step states what it changes AND what it leaves untouched
Do NOT proceed to Phase 3 until FACTS passes.
After FACTS validation, present the plan for human review. The plan is the primary review artifact -- it's far more efficient to review a plan than to review generated code.
Goal: Execute the validated plan with minimal context pressure.
Step N: {title}
- Status: DONE / BLOCKED / MODIFIED
- Verification: {result of the verification step}
- Deviation: {none, or explain why plan was adjusted}
When the conversation becomes long or starts going off track (roughly 20+ turns):
## Compressed Context for: {task}
### Validated Facts
- {fact 1 with file path and line number}
- {fact 2 with file path and line number}
### Plan Status
- Steps completed: {list}
- Current step: {N} - {status}
- Remaining: {list}
### Blockers
- {any unresolved issues}
Better input produces better output. A fresh context with compressed facts outperforms a bloated context with noise.
Sub-agents are sharding tools, not personas. Use them to:
| Task | Agent Type | Returns |
|---|---|---|
| Broad codebase reading | Explore | Compressed file/function map |
| Targeted search | Explore | Specific paths and line numbers |
| Independent implementation steps | Bash or general-purpose | Step completion status |
| Test execution | Bash | Pass/fail with output |
Rules:
When RPI plans repeatedly fail (3+ consecutive step failures), this signals that the task complexity exceeds what AI can handle with current context.
Action: Stop. Return to the whiteboard. The human must re-think the approach and break the problem down differently before re-engaging AI.
Do not brute-force through repeated failures -- that wastes context and produces bad code.