Cabloy Spec Generation
cabloy/cabloy
A skill your agent uses to create or maintain Cabloy suite specifications under repo-specs, including PRD, SRS, PDP/WBS, acceptance planning, progress, and suite ADRs.
Generate PRD and Design Docs from existing codebase through discovery, generation, verification, and review workflow
$ npx skills add shinpr/claude-code-workflows --skill recipe-reverse-engineer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install shinpr/claude-code-workflows recipe-reverse-engineer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/shinpr/claude-code-workflows.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/recipe-reverse-engineer .claude/skills/recipe-reverse-engineer && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "recipe-reverse-engineer" agent skill from https://github.com/shinpr/claude-code-workflows/tree/main/skills/recipe-reverse-engineer into .claude/skills/recipe-reverse-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recipe-reverse-engineer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/shinpr/claude-code-workflows/tree/main/skills/recipe-reverse-engineerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add shinpr/claude-code-workflows --skill recipe-reverse-engineer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install shinpr/claude-code-workflows recipe-reverse-engineer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shinpr/claude-code-workflows.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/recipe-reverse-engineer .agents/skills/recipe-reverse-engineer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "recipe-reverse-engineer" agent skill from https://github.com/shinpr/claude-code-workflows/tree/main/skills/recipe-reverse-engineer into .agents/skills/recipe-reverse-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recipe-reverse-engineer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add shinpr/claude-code-workflows --skill recipe-reverse-engineer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install shinpr/claude-code-workflows recipe-reverse-engineer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shinpr/claude-code-workflows.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/recipe-reverse-engineer .cursor/skills/recipe-reverse-engineer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "recipe-reverse-engineer" agent skill from https://github.com/shinpr/claude-code-workflows/tree/main/skills/recipe-reverse-engineer into .cursor/skills/recipe-reverse-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recipe-reverse-engineer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/shinpr/claude-code-workflows.git --path skills/recipe-reverse-engineer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add shinpr/claude-code-workflows --skill recipe-reverse-engineer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install shinpr/claude-code-workflows recipe-reverse-engineer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shinpr/claude-code-workflows.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/recipe-reverse-engineer .gemini/skills/recipe-reverse-engineer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "recipe-reverse-engineer" agent skill from https://github.com/shinpr/claude-code-workflows/tree/main/skills/recipe-reverse-engineer into .gemini/skills/recipe-reverse-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recipe-reverse-engineer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install shinpr/claude-code-workflows recipe-reverse-engineerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add shinpr/claude-code-workflows --skill recipe-reverse-engineer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/shinpr/claude-code-workflows.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/recipe-reverse-engineer .github/skills/recipe-reverse-engineer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "recipe-reverse-engineer" agent skill from https://github.com/shinpr/claude-code-workflows/tree/main/skills/recipe-reverse-engineer into .github/skills/recipe-reverse-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recipe-reverse-engineer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add shinpr/claude-code-workflows --skill recipe-reverse-engineer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install shinpr/claude-code-workflows recipe-reverse-engineer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shinpr/claude-code-workflows.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/recipe-reverse-engineer .opencode/skills/recipe-reverse-engineer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "recipe-reverse-engineer" agent skill from https://github.com/shinpr/claude-code-workflows/tree/main/skills/recipe-reverse-engineer into .opencode/skills/recipe-reverse-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recipe-reverse-engineer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
recipe-reverse-engineerGenerate PRD and Design Docs from existing codebase through discovery, generation, verification, and review workflow
Recipe Reverse Engineer is an agent skill from shinpr/claude-code-workflows. Generate PRD and Design Docs from existing codebase through discovery, generation, verification, and review workflow
Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Product & Project Management, covering PRD writing and Architecture decision records. The repository describes itself as: Development workflows for Claude Code that keep broad exploration focused on the outcome you approved. The licence is MIT.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit a4ecd62. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Recipe Reverse Engineer loads about 4k tokens when it runs. Until then it costs about 35 tokens; SKILL.md has 1,221 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from shinpr/claude-code-workflows at commit a4ecd62, republished under its MIT licence (© shinpr). 1,221 words, ~4,008 tokens.
.claude/skills/recipe-reverse-engineer/SKILL.md (or your agent's skills folder).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: llm-friendly-context before writing Agent prompts, handoffs, or generated artifacts. Execute Skill: subagents-orchestration-guide before making workflow decisions, invoking agents, or resolving findings.
Context: Reverse engineering workflow to create documentation from existing code
Target: $ARGUMENTS
Core Identity: "I am an orchestrator."
Local authority gate: Make this recipe's workflow decisions and validate each returned result directly; delegate semantic deliverable production to the named specialist.
Review Resolution Gate [MANDATORY]: Resolve every actionable deliverable-review finding through subagents-orchestration-guide Review Resolution before correction or progression.
Before the first finding disposition, read references/review-resolution.md from the loaded subagents-orchestration-guide skill.
Execution Protocol:
At each Agent invocation below, build the prompt as a mechanical extraction: copy the named source values into the exact fields, apply only the declared serialization, then invoke immediately.
Execution Gate: Complete Phase 1 before Phase 2. Within each phase, complete one unit's generation, verification, review, and required revision to convergence before starting the next unit. Advance only when the current step's stated output and quality gate are satisfied. At each loop boundary, select the first unit in the current phase's declared order whose Unit Completion conditions are unsatisfied and that is not logged as a generation failure. A document path proves generation only.
Use AskUserQuestion to confirm:
docs/prd/ or existing PRD directorydocs/design/ or existing design directoryPhase 1: PRD Generation
Step 1: Scope Discovery (unified, single pass → group into PRD units → human review)
Step 2-5: Per-unit loop (Generation → Verification → Review → Revision)
Phase 2: Design Doc Generation (if requested)
Step 6: Design Doc Scope Mapping (reuse Step 1 results, no re-discovery)
Step 7-10: Per-unit loop (Generation → Verification → Review → Revision)
※ fullstack=Yes: each unit produces backend + frontend Design DocsAgent tool invocation:
subagent_type: dev-workflows:scope-discoverer
description: "Discover functional scope"
prompt: |
Discover functional scope targets in the codebase.
target_path: $USER_TARGET_PATH
reference_architecture: $USER_RA_CHOICE
focus_area: [user-confirmed focus area verbatim, if specified]Store output as: $STEP_1_OUTPUT
Quality Gate:
$STEP_1_OUTPUT.prdUnits existssourceUnits across prdUnits (flattened, deduplicated) match the set of discoveredUnits IDs — no unit missing, no unit duplicatedunitInventory has at least one non-empty category (routes, testFiles, or publicExports). Units with all three empty indicate incomplete discovery — re-run scope-discoverer with focus on that unit's relatedFilesHuman Review Point (if enabled): Present $STEP_1_OUTPUT.prdUnits with their source unit mapping. The user confirms, adjusts grouping, or excludes units from scope. This is the most important review point — incorrect grouping cascades into all downstream documents.
FOR each unit in $STEP_1_OUTPUT.prdUnits (sequential, one unit at a time):
Agent tool invocation:
subagent_type: dev-workflows:prd-creator
description: "Generate PRD"
prompt: |
Create reverse-engineered PRD for the following feature.
Operation Mode: reverse-engineer
External Scope Provided: true
Feature: $PRD_UNIT_NAME (current Step 1 PRD unit name unchanged)
Description: $PRD_UNIT_DESCRIPTION (current Step 1 PRD unit description unchanged)
Related Files: $PRD_UNIT_COMBINED_RELATED_FILES
Entry Points: $PRD_UNIT_COMBINED_ENTRY_POINTS
Use provided scope as investigation starting point.
If tracing entry points reveals files outside this scope, include them.
Create final version PRD based on thorough code investigation.Store output as: $STEP_2_OUTPUT (PRD path)
Prerequisite: $STEP_2_OUTPUT (PRD path from Step 2)
Agent tool invocation:
subagent_type: dev-workflows:code-verifier
description: "Verify PRD consistency"
prompt: |
Verify consistency between PRD and code implementation.
doc_type: prd
document_path: $STEP_2_OUTPUT
unit_inventory: [the current unit's Step 1 unitInventory]unit_inventory supplies the completeness baseline while repository evidence supplies the search scope.
Store output as: $STEP_3_OUTPUT
Quality Gate:
summary.status is blocked, inventory coverage is missing, or counts do not balance → re-run or escalate with the exact unusable input or evidenceneeds_review / inconsistent and any unaccounted items become explicit review evidenceRequired Input: $STEP_3_OUTPUT (verification JSON from Step 3)
Agent tool invocation:
subagent_type: dev-workflows:document-reviewer
description: "Review PRD"
prompt: |
Review the following PRD considering code verification findings.
doc_type: PRD
target: $STEP_2_OUTPUT
review_context: reverse-engineer
verification_evidence: $STEP_3_OUTPUTStore output as: $STEP_4_OUTPUT
Pass $STEP_3_OUTPUT to document-reviewer as verification evidence, then apply the Review Resolution Gate to $STEP_4_OUTPUT. Run revision only when at least one finding is apply; a decline-only result completes the review.
Agent tool invocation:
subagent_type: dev-workflows:prd-creator
description: "Revise PRD"
prompt: |
Update PRD based on review feedback and code verification results.
Operation Mode: update
Existing PRD: $STEP_2_OUTPUT
## Adjudicated Findings
[complete reviewer finding objects verbatim, with only their orchestrator dispositions added]
Treat these findings as the complete revision scope and preserve adjacent content.Re-validation: After each revision, re-run code-verifier on the revised document with the original unit_inventory, then re-run document-reviewer with the latest verification_evidence and prior_feedback.
apply findings remainNext: Proceed to next unit. After all units → Phase 2.
Execute only if Design Docs were requested in Step 0
No additional discovery required. Use $STEP_1_OUTPUT.discoveredUnits (implementation-granularity units) for technical profiles. Use $STEP_1_OUTPUT.prdUnits[].sourceUnits to trace which discovered units belong to each PRD unit.
Each PRD unit from Phase 1 maps to Design Doc unit(s):
Map $STEP_1_OUTPUT units to Design Doc generation targets, carrying forward:
technicalProfile.primaryModules → Primary FilestechnicalProfile.publicInterfaces → Public Interfacesdependencies → DependenciesrelatedFiles → Scope boundaryunitInventory → Unit Inventory (routes, test files, public exports)In fullstack mode, partition each unit inventory by the owning path into backend and frontend target inventories. Assign a shared entry to each Design Doc whose public contract must account for it and record that shared reason; otherwise assign it once. Each Step 7 and Step 8 invocation receives its target's inventory, not the unpartitioned combined unit.
Store output as: $STEP_6_OUTPUT
FOR each unit in $STEP_6_OUTPUT (sequential, one unit at a time):
Scope: Document the current architecture exactly as implemented in code.
Standard mode (fullstack=No):
Agent tool invocation:
subagent_type: dev-workflows:technical-designer
description: "Generate Design Doc"
prompt: |
Create Design Doc for the following feature based on existing code.
Operation Mode: reverse-engineer
Feature: $UNIT_NAME (current Step 6 target name unchanged)
Description: $UNIT_DESCRIPTION (current Step 6 target description unchanged)
Primary Files: $UNIT_PRIMARY_MODULES
Public Interfaces: $UNIT_PUBLIC_INTERFACES
Dependencies: $UNIT_DEPENDENCIES
Unit Inventory: $DESIGN_DOC_UNIT_INVENTORY
Parent PRD: $APPROVED_PRD_PATH
Document current architecture as-is. Use Unit Inventory as a completeness baseline — all routes and exports should be accounted for in the Design Doc.Store output as: $STEP_7_OUTPUT
Fullstack mode (fullstack=Yes):
For each unit, invoke 7a then 7b sequentially (7b depends on 7a output):
7a. Backend Design Doc:
subagent_type: dev-workflows:technical-designer
description: "Generate backend Design Doc"
prompt: |
Create a backend Design Doc for the following feature based on existing code.
Operation Mode: reverse-engineer
Feature: $UNIT_NAME (current Step 6 target name unchanged)
Description: $UNIT_DESCRIPTION (current Step 6 target description unchanged)
Primary Files: $UNIT_PRIMARY_MODULES
Public Interfaces: $UNIT_PUBLIC_INTERFACES
Dependencies: $UNIT_DEPENDENCIES
Unit Inventory: $BACKEND_UNIT_INVENTORY
Parent PRD: $APPROVED_PRD_PATH
Focus on: API contracts, data layer, business logic, service architecture.
Document current architecture as-is. Use Unit Inventory as completeness baseline.Store output as: $STEP_7a_OUTPUT
7b. Frontend Design Doc:
subagent_type: dev-workflows-frontend:technical-designer-frontend
description: "Generate frontend Design Doc"
prompt: |
Create a frontend Design Doc for the following feature based on existing code.
Operation Mode: reverse-engineer
Feature: $UNIT_NAME (current Step 6 target name unchanged)
Description: $UNIT_DESCRIPTION (current Step 6 target description unchanged)
Primary Files: $UNIT_PRIMARY_MODULES
Public Interfaces: $UNIT_PUBLIC_INTERFACES
Dependencies: $UNIT_DEPENDENCIES
Unit Inventory: $FRONTEND_UNIT_INVENTORY
Parent PRD: $APPROVED_PRD_PATH
Backend Design Doc: $STEP_7a_OUTPUT
Reference backend Design Doc for API contracts.
Focus on: component hierarchy, state management, UI interactions, data fetching.
Document current architecture as-is. Use Unit Inventory as completeness baseline.Store output as: $STEP_7b_OUTPUT
Standard mode: Verify $STEP_7_OUTPUT.
Fullstack mode: Verify each Design Doc separately.
Agent tool invocation (per Design Doc):
subagent_type: dev-workflows:code-verifier
description: "Verify Design Doc consistency"
prompt: |
Verify consistency between Design Doc and code implementation.
doc_type: design-doc
document_path: $STEP_7_OUTPUT (or $STEP_7a_OUTPUT / $STEP_7b_OUTPUT)
unit_inventory: [the current Design Doc target's Step 6 unitInventory]Store output as: $STEP_8_OUTPUT
Verification gate (per Design Doc):
blocked, missing inventoryCoverage, or unbalanced category counts → correct the invocation/input and rerun; stop for the user only when repository evidence cannot resolve the input defect.needs_review, inconsistent, and every unaccounted item as explicit verifier evidence.Required Input: $STEP_8_OUTPUT (verification JSON from Step 8)
Agent tool invocation (per Design Doc):
subagent_type: dev-workflows:document-reviewer
description: "Review Design Doc"
prompt: |
Review the following Design Doc considering code verification findings.
doc_type: DesignDoc
target: $STEP_7_OUTPUT (or $STEP_7a_OUTPUT / $STEP_7b_OUTPUT)
review_context: reverse-engineer
verification_evidence: $STEP_8_OUTPUT
## Parent PRD
$APPROVED_PRD_PATH
## Additional Review Focus
- Technical accuracy of documented interfaces
- Consistency with parent PRD scope
- Completeness of unit boundary definitionsStore output as: $STEP_9_OUTPUT
Pass $STEP_8_OUTPUT to document-reviewer as verification evidence, then apply the Review Resolution Gate to $STEP_9_OUTPUT. Run revision only when at least one finding is apply; a decline-only result completes the review.
Agent tool invocation (per Design Doc):
subagent_type: dev-workflows:technical-designer (or dev-workflows-frontend:technical-designer-frontend for frontend Design Docs)
description: "Revise Design Doc"
prompt: |
Update Design Doc based on review feedback and code verification results.
Operation Mode: update
Existing Design Doc: $STEP_7_OUTPUT (or $STEP_7a_OUTPUT / $STEP_7b_OUTPUT)
## Adjudicated Findings
[complete reviewer finding objects verbatim, with only their orchestrator dispositions added]
Treat these findings as the complete revision scope and preserve adjacent content.Re-validation: After each revision, re-run code-verifier on the revised document with the original unit_inventory, then re-run document-reviewer with the latest verification_evidence and prior_feedback.
apply findings remainNext: Proceed to next unit. After all units → Final Report.
Output summary including:
| Error | Action |
|---|---|
| Discovery finds nothing | Ask user for project structure hints |
| Generation fails | Log failure, continue with other units, report in summary |
Verification is inconsistent or inventory remains unaccounted after correction | Flag for mandatory human review — require explicit human approval |
© shinpr, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/recipe-reverse-engineer of shinpr/claude-code-workflows.
Open the folder on GitHubat commit a4ecd62
Recipe Reverse Engineer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Recipe Reverse Engineer this skillshinpr/claude-code-workflows | 691 | — | ~4k | Automated safety check: Pass | MIT | |
| Cabloy Spec Generationcabloy/cabloy | 982 | — | ~3.2k | Automated safety check: Notes | MIT | |
| Schematicblader/schematic | 240 | — | ~2.2k | Automated safety check: Pass | MIT | |
| Shep Workstreamsshep-ai/shep | 264 | — | ~2.5k | Automated safety check: Pass | MIT | |
| Write Update Tidb Docspingcap/docs | 616 | — | ~2.3k | Automated safety check: Pass | Custom licence | |
| App Spec Packagerinstructa/agent-skills | 139 | — | ~1.5k | Automated safety check: Pass | None |
cabloy/cabloy
A skill your agent uses to create or maintain Cabloy suite specifications under repo-specs, including PRD, SRS, PDP/WBS, acceptance planning, progress, and suite ADRs.
blader/schematic
Reverse engineer a detailed product and technical specification document from a git branch's implementation.
shep-ai/shep
A skill your agent uses when a large body of work (a version milestone, an epic, a roadmap, a set of PRDs/design docs) needs to be broken into parallel workstreams and executed with the shep CLI.
pingcap/docs
Write new TiDB documentation or update existing TiDB documentation from code changes, PRs, issues, design docs, product specs, rough drafts, existing docs, or short feature descriptions.
instructa/agent-skills
A skill your agent uses when the user wants to turn an application, product, startup idea, SaaS, mobile app, web app, API, AI product, or internal tool into a production-ready Markdown specification…
aj-geddes/claude-code-bmad-skills
Orchestration spine and "what do I do next?" router for the BMAD Planning & Orchestrator plugin.
shinpr/claude-code-workflows
Integration and E2E test design principles, ROI calculation, test skeleton specification, and review criteria.
shinpr/claude-code-workflows
Applies language-agnostic and backend technical decision criteria, anti-pattern detection, debugging, and quality gates.
shinpr/claude-code-workflows
Applies React/TypeScript-specific technical decision criteria, anti-pattern detection, debugging, and frontend quality gates.
shinpr/claude-code-workflows
Implementation strategy selection framework. An agent skill from shinpr/claude-code-workflows.
shinpr/claude-code-workflows
Proposes repository-specific quality policy for implementation and review and, after confirmation, creates or updates docs/project-context/quality.yaml.
shinpr/claude-code-workflows
Guides subagent coordination through implementation workflows.
Categories
Generate PRD and Design Docs from existing codebase through discovery, generation, verification, and review workflow. Recipe Reverse Engineer is an agent skill from shinpr/claude-code-workflows.
Recipe Reverse Engineer fits situations like: tasks that involve PRD writing; tasks that involve Architecture decision records.
Run `npx skills add shinpr/claude-code-workflows --skill recipe-reverse-engineer -a claude-code`. Or copy the skill folder (skills/recipe-reverse-engineer in shinpr/claude-code-workflows) into .claude/skills/recipe-reverse-engineer in your project. Claude Code loads it when a task matches its description.
Run `npx skills add shinpr/claude-code-workflows --skill recipe-reverse-engineer -a codex`. Or copy the skill folder (skills/recipe-reverse-engineer in shinpr/claude-code-workflows) into .agents/skills/recipe-reverse-engineer in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add shinpr/claude-code-workflows --skill recipe-reverse-engineer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/recipe-reverse-engineer, .gemini/skills/recipe-reverse-engineer, .github/skills/recipe-reverse-engineer and .opencode/skills/recipe-reverse-engineer in your project.
SKILL.md names no scripts, command-line tools or credentials: Recipe Reverse Engineer is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Recipe Reverse Engineer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Recipe Reverse Engineer: Cabloy Spec Generation (cabloy/cabloy, 982 stars), Schematic (blader/schematic, 240 stars), Shep Workstreams (shep-ai/shep, 264 stars) and Write Update Tidb Docs (pingcap/docs, 616 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
shinpr (a GitHub user) maintains it in shinpr/claude-code-workflows, which has 691 GitHub stars. The repository holds 30 skills in this directory. The repository was last updated on October 1, 2026.
Source: shinpr/claude-code-workflows on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.