一键导入
dev-workflow-planning
Structured dev workflows via /brainstorm, /write-plan, /execute-plan. Use when breaking down complex projects into systematic steps.
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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Structured dev workflows via /brainstorm, /write-plan, /execute-plan. Use when breaking down complex projects into systematic steps.
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
Builds multi-repo context hubs and compiled markdown knowledge maps. Use when profiling repo portfolios or assembling LLM-ready cross-repo knowledge bases.
Builds per-repo code graphs in JSON and markdown-ready derived artifacts. Use when you need blast radius, symbol-level maps, import graphs, inheritance, or test links.
Context-driven AI development with AGENTS.md, repo knowledge bases, Claude Code, Codex, and Copilot. Use when adopting repo-native AI workflows or multi-repo setups.
Technical writing for READMEs, ADRs, API docs, and changelogs. Use when revising or consolidating a repo documentation folder.
Design, implement, and troubleshoot NUKE-based CI/CD pipelines for .NET services with fast local-to-CI feedback loops. Use when creating or refactoring `nuke/Build.cs` target graphs, tuning `DependsOn`/`After`/`Triggers`/`OnlyWhenDynamic` behavior, orchestrating unit/API/DB test categories, merging and publishing coverage and test reports, building and pushing Docker images with traceable tags and digests, producing artifact contracts such as `deploy.env`, and diagnosing flaky or slow pipeline execution. For service code changes use $software-csharp-backend, for NUnit fixture design use $qa-testing-nunit, and for safe logging rewrites use $dev-structured-logs.
Systematic debugging for crashes, regressions, flakes, and production bugs. Use when diagnosing stack traces, logs, traces, or profiling data.
| name | dev-workflow-planning |
| description | Structured dev workflows via /brainstorm, /write-plan, /execute-plan. Use when breaking down complex projects into systematic steps. |
This skill enables structured, systematic development workflows. The assistant should apply these patterns when users need to break down complex projects, create implementation plans, or execute multi-step development tasks with clear checkpoints.
Inspired by: Obra Superpowers patterns for structured agent workflows.
| Command | Purpose | When to Use |
|---|---|---|
/brainstorm | Generate ideas and approaches | Starting new features, exploring solutions |
/write-plan | Create detailed implementation plan | Before coding, after requirements clarification |
/execute-plan | Implement plan step-by-step | When plan is approved, ready to code |
/checkpoint | Review progress, adjust plan | Mid-implementation, after major milestones |
/summarize | Capture learnings, document decisions | End of session, before context reset |
The assistant should invoke this skill when a user requests:
Purpose: Explore the problem space and generate potential solutions.
/brainstorm [topic or problem]
OUTPUT:
1. Problem Understanding
- What are we solving?
- Who is affected?
- What are the constraints?
2. Potential Approaches (3-5)
- Approach A: [description, pros, cons]
- Approach B: [description, pros, cons]
- Approach C: [description, pros, cons]
3. Questions to Resolve
- [List of unknowns needing clarification]
4. Recommended Approach
- [Selected approach with justification]
Purpose: Create a detailed, actionable implementation plan.
/write-plan [feature or task]
OUTPUT:
## Implementation Plan: [Feature Name]
### Goal
[Single sentence describing the outcome]
### Success Criteria
- [ ] Criterion 1
- [ ] Criterion 2
- [ ] Criterion 3
### Steps (with estimates)
#### Step 1: [Name] (~Xh)
- What: [specific actions]
- Files: [files to modify/create]
- Dependencies: [what must exist first]
- Verification: [how to confirm done]
### Risks & Mitigations
| Risk | Likelihood | Impact | Mitigation |
|------|-----------|--------|------------|
| Risk 1 | Medium | High | Plan B if... |
### Open Questions
- [Questions to resolve before starting]
When a plan will be executed with multiple subagents, each task must declare its dependencies explicitly. This enables the orchestrator to determine which tasks can run in parallel.
### Task Dependency Graph
| Task ID | Name | depends_on | Files | Agent Scope |
|---------|------|------------|-------|-------------|
| T1 | Setup database schema | [] | db/schema.sql | db-engineer |
| T2 | Create API routes | [T1] | src/routes/*.ts | backend-dev |
| T3 | Build auth middleware | [T1] | src/middleware/auth.ts | backend-dev |
| T4 | Frontend components | [] | src/components/*.tsx | frontend-dev |
| T5 | Integration tests | [T2, T3, T4] | tests/integration/*.test.ts | qa-agent |
Rules for dependency graphs:
depends_on: [] with explicit task IDs (empty array = no blockers).Swarm Waves (Accuracy-First) — Launch one subagent per unblocked task, in dependency-respecting waves. Wait for each wave to complete before launching the next. Best for production code and complex interdependencies.
Super Swarms (Speed-First) — Launch as many subagents as possible at once, regardless of dependencies. Best for prototypes and greenfield scaffolding. Expect merge conflicts.
See references/planning-templates.md for the full swarm-ready plan template.
Purpose: Implement the plan systematically with checkpoints.
/execute-plan [plan reference]
EXECUTION PATTERN:
1. Load the plan
2. For each step:
a. Announce: "Starting Step X: [name]"
b. Execute actions
c. Verify completion
d. Report: "Step X complete. [brief summary]"
3. After completion:
a. Run all verification criteria
b. Report final status
For production coding sessions, wrap /execute-plan with a delivery guardrail:
npm run test:analytics-gate)../scripts/git/feature-workflow.sh start <feature-slug>
cd .worktrees/<feature-slug>
# implement plan steps
../../scripts/git/feature-workflow.sh gate
../../scripts/git/feature-workflow.sh pr --title "feat: <summary>"
Key rules from production experience (Feb 2026):
See references/session-patterns.md for the full production evidence table and checkpoint protocol for long sessions.
Before broad edits, tests, or reviews — run a 60-second preflight:
pwd / git branch --show-current / ls -latest -e <path> to verify target paths before heavy commandsnpx <tool> --help to validate flags before first use[], *, ?, or spacesSee references/operational-checklists.md for the full git/branch safety preflight, E2E/server preflight, shell safety gate, and SDK type verification.
PATTERN: Test assumptions before committing
Before implementing:
1. State hypothesis: "If we [action], then [expected outcome]"
2. Define experiment: "To test this, we will [minimal test]"
3. Execute experiment
4. Evaluate: "Hypothesis confirmed/rejected because [evidence]"
5. Proceed or pivot based on result
Build in verifiable increments: smallest testable unit → implement and verify → expand scope → verify at each expansion → integrate and verify whole.
See references/planning-templates.md for an authentication feature example with 5 increments.
PATTERN: Maintain visible progress
[X] Step 1: Create database schema
[X] Step 2: Implement API endpoints
[IN PROGRESS] Step 3: Add frontend form
[ ] Step 4: Write tests
Current: Step 3 of 4 (75% complete)
Blockers: None
Next: Complete form validation
Limit concurrent work: individual (2-3 tasks), team stories (team size + 1), in-progress column (3-5 items), code review (2-3 PRs). If limits are never reached, lower them. If constantly blocked, investigate the bottleneck.
See references/planning-templates.md for the full WIP limits reference and setting guidelines.
For multi-step execution, constrain scope and checkpoint progress at milestone boundaries.
1-2 deliverables only.At each milestone, record:
Stop and rescope when any occur:
See references/session-scope-budgeting.md for full scope budgeting model and enforcement rules.
Session initialized.
- Project: [name]
- Goal: [today's objective]
- Context loaded: [files, previous decisions]
- Plan status: [steps remaining]
Ready to continue from: [last checkpoint]
/summarize
OUTPUT:
## Session Summary
### Completed
- [List of completed items]
### In Progress
- [Current state of incomplete work]
### Decisions Made
- [Key decisions with rationale]
### Next Session
- [ ] [First task for next time]
### Context to Preserve
[Critical information for continuity]
When faced with choices:
1. State the decision clearly
2. List options (2-4)
3. For each option: Pros / Cons / Effort / Risk
4. Recommendation with justification
5. Reversibility assessment
Example:
| Option | Pros | Cons | Effort | Risk |
|--------|------|------|--------|------|
| JWT | Stateless, scalable | Token management | 2 days | Low |
| Sessions | Simple, secure | Server state | 1 day | Low |
| OAuth only | No passwords | External dependency | 3 days | Medium |
Recommendation: Sessions for MVP, plan JWT migration for scale.
/write-plan with TDD:
Step 1: Write failing test
Step 2: Implement minimal code
Step 3: Verify test passes
Step 4: Refactor
Step 5: Add edge case tests
/brainstorm system design:
1. Requirements clarification
2. Component identification
3. Interface definition
4. Data flow mapping
5. Implementation plan
assets/template-dor-dod.md - Checklists for work readiness and completion.
assets/template-work-item-ticket.md - Ticket template with DoR/DoD and testable acceptance criteria.
Key sections: Definition of Ready / Done checklists, Acceptance Criteria templates (Gherkin), Estimation Guidelines (story point scale 1-21+), Planning Levels (Roadmap → Sprint → Task), Cross-Functional RACI.
| Anti-Pattern | Problem | Fix |
|---|---|---|
| No DoR | Unclear requirements discovered mid-sprint | Gate sprint entry with DoR |
| Soft DoD | "Done" means different things | Written DoD checklist |
| Mega-stories | Never finish, hard to track | Slice to <8 points |
| Missing AC | Built wrong thing | Gherkin format AC |
| No ownership | Work falls through cracks | RACI for every epic |
| Hope-based estimates | Always late | Use reference scale + buffer |
Note: AI can assist but should not replace human judgment on priorities and acceptance.
AI-generated criteria and estimates require human calibration before committing to them.