ai-development-guide
Applies language-agnostic and backend technical decision criteria, anti-pattern detection, debugging, and quality gates. Use when reviewing general/backend…
Execute from codebase-scoped analysis through optional ADR decisions to complete Design Doc approval
$ npx -y skills add shinpr/claude-code-workflows --skill recipe-design --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/recipe-designContext preview
The summary Claude sees to decide when to auto-load this skill.
Execute from codebase-scoped analysis through optional ADR decisions to complete Design Doc approval
name: recipe-design description: Execute from codebase-scoped analysis through optional ADR decisions to complete Design Doc approval disable-model-invocation: true
**Explicit User Instruction**: The user explicitly instructs and authorizes every subagent call named in this recipe. Execute each applicable call when its prerequisites are met.
Execute Skill: documentation-criteria before document routing or creation. Execute Skill: llm-friendly-context before writing Agent prompts, handoffs, or generated artifacts. Execute Skill: subagents-orchestration-guide before invoking agents or resolving findings. Before the first finding disposition, read `references/review-resolution.md` from the loaded subagents-orchestration-guide skill.
Coordinate the design phase from repository evidence to an approved Design Doc. The user owns product requirements and exclusions; the orchestrator owns convergence readiness, Structural Scale, ADR qualification, evidence selection, and Review Resolution. Named specialists own semantic investigation and artifact authorship.
The Design Doc is always the complete implementation design for Medium/Large work. A qualifying ADR batch narrows technical choices before the Design Doc, which retains the complete flow and implementation boundary.
Requirements: $ARGUMENTS
requirement source -> codebase-analyzer -> scope/decision confirmation [Stop]
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optional ADR batch -> batch review [Stop]
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Design Doc -> code-verifier -> Review Resolution
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document-reviewer -> design-sync -> approval [Stop]Execute each dependent step after its prerequisite evidence exists. Use Review Resolution for every actionable verifier, reviewer, or design-sync finding. Wait at each `[Stop]` for explicit user confirmation.
At each `Invoke` below, build the Agent prompt as a mechanical extraction: copy the named source values into the exact fields, apply only the declared serialization, then invoke immediately.
Use the approved PRD path when one exists. Otherwise use the confirmed requirements verbatim.
Set `confirmed_requirement_context` to the approved PRD path exactly. Only when no approved PRD exists, use the orchestrator-confirmed convergence record unchanged.
Invoke `dev-workflows:codebase-analyzer`:
prd_path: [approved PRD path]
or, when no approved PRD exists:
requirements: [confirmed requirements verbatim]
Invoke once for the complete confirmed scope. Require one valid JSON result and let the analyzer discover affected paths, responsibility boundaries, and cross-layer contracts. Treat its focus areas as existing-behavior safeguards, not as new requirements.
This independent discovery keeps scope and option convergence grounded in repository evidence rather than the orchestrator's unverified implementation hypothesis.
Execute Skill: requirement-convergence. The orchestrator builds and judges the convergence record from the user request and Step 2 evidence.
Judge all four convergence fields. Assign `cost` from Step 2 structural evidence and record its unknowns; run the hearing only for fields below `ready`.
Determine Structural Scale from outcomes and responsibility boundaries. File count is supporting evidence only.
Resolve `decisionMaterials.candidateDecisionPoints` against the governing requirement source, applicable `simplifications`, `reuse`, and `invalidations`. Remove a point when that evidence already converges on one sufficient approach. For each remaining item, apply documentation-criteria filters in order:
1. Choice requires judgment between at least two credible, materially distinct options inside confirmed scope. 2. The selection has durable material impact.
Record every passing item as `adrDecisionPoints`; an empty list routes directly to the Design Doc. ADR creation is limited to items that pass both filters.
Present the requirement-convergence Scope Confirmation. Place target responsibilities with their strongest file evidence, applicable simplifications with their conditions, and each qualifying ADR decision point with its filter evidence or `none` under **Decision evidence**; place Structural Scale with its boundary rationale and the recommended document route under **Workflow**.
Offer proceed, or correct scope and re-run analysis. Ask a question only when its answer can change a convergence field, the confirmed outcome, or scope. Continue only when every convergence field is `ready` or `weak-but-explicit`. `[Stop: Scope confirmation]`.
When `adrDecisionPoints` is non-empty:
1. Invoke `dev-workflows:technical-designer` once with exact inputs: `document_to_create: ADRBatch`; `confirmed_requirement_context`; `decision_points` as the ordered `adrDecisionPoints` confirmed in Step 3, unchanged; and `decision_materials` as the corresponding objects from Step 2 `decisionMaterials.candidateDecisionPoints`, copied unchanged in that order. 2. Invoke `dev-workflows:document-reviewer` once with exact inputs: `doc_type: ADRBatch`, `targets: [all returned paths]`, and `confirmed_requirement_context`. 3. Route the reviewer verdict first: `pass` proceeds with `issues: []`; `needs_revision` applies Review Resolution, updates one ADR per path serially, and re-reviews the complete batch; `rejected` resolves the governing-source conflict before another review. 4. Present one batch decision only after a `pass` review. `[Stop: ADR batch approval]`. 5. After user approval, update each ADR status to `Accepted` and verify the changed status.
Create the complete MVP implement
Claude Code can explore a codebase deeply. On non-trivial work, the harder problem is convergence.
Repo: shinpr/claude-code-workflows
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