skills/design/SKILL.md
Reads SPEC.md, validates technical feasibility through web research, finds quality reference implementations, confirms tech stack compatibility, then produces DESIGN.md (architecture design, external dependencies), CHECKLIST.md (verification strategy), Architecture Diff, and design-time refactoring plan. Not for use without a SPEC.md.
npx skillsauth add laitszkin/apollo-toolkit designInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Transform SPEC.md business requirements into a technical design grounded in evidence — verify feasibility, find reference implementations, confirm tech stack compatibility before designing. Identify refactoring opportunities during code survey and classify by module boundary scope (T1–T3).
docs/plans/<YYYY-MM-DD>/<spec_name>/DESIGN.md covering all required sectionsreferences/ folder populated with external method/API reference documentsreferences/ folderreferences/ at the batch root covering all constituent specs. Distill the common goal from all SPEC.md files during ingestion.Read all SPEC.md files. Understand: business goal, BDD requirements, error/edge cases, uncertainty markers, clarification questions.
Complete three passes before designing. Do deep research to obtain required information.
2a. Technical Feasibility — For each requirement, verify feasibility under the current tech stack. Flag high-risk points. Record key limitations.
⚠️ Decision gate — STOP if blocking issues found:
| Assessment | Action | |---|---| | ✅ All feasible | Continue | | ⚠️ Partial validation needed | Continue; mark uncertain items as Exploratory; suggest spike/prototype where warranted | | 🛑 Blocking issues found | STOP. Document infeasibility with evidence (API limits, licensing, platform gaps). Request SPEC.md revision. |
This gate is critical: the spec phase validates against existing code only — it does not check external feasibility. This is the pipeline's only chance to catch real-world constraints before design.
2b. Existing Quality References — Search for mature open-source solutions, community best practices, officially recommended approaches for similar functionality. Record reusable design patterns.
2c. Tech Stack Compatibility — Verify external dependency compatibility: version conflict risks, alternatives comparison, license compatibility.
3a. Discover CodeGraph commands — run apltk codegraph --help and apltk codegraph <subcommand> --help before selecting commands. Use the live help output instead of memorized command names.
3b. Explore affected code — use CodeGraph to inspect real files, symbols, callers/callees, contextual flows, and change impact for affected modules. Record only findings that influence design decisions.
See references/codegraph.md for all flags and subcommands.
3c. Code Health Assessment — While reading code, identify smells, dead code, legacy patterns. Classify using the T1–T3 framework (see references/code-smells.md for patterns):
| Tier | Scope | Validate with | |------|-------|---------------| | T1 | Single function/file; no API change | Existing unit tests | | T2 | Crosses files within same module | Existing integration tests | | T3 | Crosses module boundaries | New test coverage (define in CHECKLIST.md) |
T1 items are safe to refactor inline. T2 and T3 feed into the design and task plan.
Use assets/templates/DESIGN.md. Transfer research outputs from Step 2 into the Research Summary section. Cover all template sections. Use Req 1, Req 2 numbering matching SPEC.md.
Scale awareness — Adapt depth to the change, not every section requires full treatment:
None if change is confined to a single module with no new cross-module coupling. Do not fabricate INT-### entries.None with justification if no architectural constraint changes.None if no code health issues found. Do not fabricate findings.Design self-challenge — Before finalizing:
Use assets/templates/CHECKLIST.md. Use test-case-strategy skill to guide test level choices. Cover all template sections.
Mandatory test coverage (unless SPEC.md describes only documentation or test changes):
Use apltk architecture CLI. Before invoking it, run apltk architecture --help and the relevant subcommand help, then follow the live CLI guidance. See references/architecture.md only as supporting reference.
references/definition.md): System Context → Container (features) → Component (submodules) → Code (selective).apltk architecture commands. Use CodeGraph only as source-code evidence while deciding what to add or change.Create reference documents for every external method and API used in the design:
This reduces hallucinated API shapes during implementation — workers consult these files instead of guessing.
Run two passes before delivering:
Completeness: Research recorded as evidence in DESIGN.md. Every architecture decision has a trade-off record. External API facts traceable to official docs. CHECKLIST.md covers all BDD requirements. Architecture Diff covers full scope. T1–T3 findings dispositioned. references/ populated. References sections cite code file paths.
Design quality:
assets/templates/DESIGN.md — DESIGN.md templateassets/templates/CHECKLIST.md — CHECKLIST.md templatereferences/architecture.md — apltk architecture CLI referencereferences/codegraph.md — apltk codegraph CLI referencereferences/definition.md — C4 model level definitionsreferences/code-smells.md — Code smell patterns to spot during surveyreferences/module-internal-simplification.md — T1 patternsreferences/module-internal-restructuring.md — T2 patternsreferences/module-boundary-adjustment.md — T3 patternsdevelopment
Review a pull request — interactive PR selection via `gh`, 4-dimension code review (hallucinated code, architecture, performance, test validity), then post severity-graded comments with fix suggestions on the PR. Not for spec-based review — use `review` instead.
development
Read a user-specified PDF that marks the week's key financial events, deeply research each marked event with current sources, capture any additional breaking financial developments, and produce a concise Chinese-capable PDF briefing that explains what happened and why it matters.
documentation
Generate long-form videos (more than 10 minutes) by following user instructions and invoking related skills only when needed (`openai-text-to-image-storyboard`, `docs-to-voice`, `remotion-best-practices`). For text inputs, extract a complete long-form story arc, generate fresh storyboard images (no reuse of previously generated pictures), and render a 16:9 animated long-form video.
tools
協助完成自動化版本發佈。同步文檔、更新版本號、推送 tag 並建立 GitHub Release。