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Api Docs (Grade A)Security-tested development skill for Claude AI. Grade A. Workflow for creating comprehensive API documentation. Use when the user needs to document API endpoints.

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Daniel NikulshynReviewed by Daniel Nikulshyn·Updated July 2026

Overview

Api Docs (Grade A) is a workflow for creating comprehensive API documentation. It uses when the user needs to document API endpoints. The process involves inventorying endpoints, documenting each endpoint, creating an OpenAPI spec, and generating documentation using Swagger UI or Redoc. The user must have API endpoints available and access to OpenAPI/Swagger tooling. Steps to document API endpoints include listing all endpoints, grouping by resource/entity, and noting HTTP method and path. This skill also covers validation and auto-updating of documentation in CI/CD, ensuring that all endpoints are accurately documented with correct examples and error responses.

Key features

  • Endpoint inventory and grouping
  • Detailed endpoint documentation
  • OpenAPI specification creation
  • Interactive documentation generation
  • Validation and review checklist

Pricing

Model
Free
Category
Skills
Rating
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Use cases

API Endpoint Documentation

Documenting API endpoints for a new e-commerce platform, including listing all endpoints, detailing HTTP methods and paths, and generating interactive documentation.

OpenAPI Specification Creation

Creating an OpenAPI specification for an existing API, including defining info, servers, security schemes, and validating the specification.

Pros & Cons

Pros

  • Comprehensive documentation process
  • Supports OpenAPI/Swagger tooling
  • Interactive documentation generation

Cons

  • Requires existing API endpoints or design
  • Optional OpenAPI/Swagger tooling may add complexity

Battle record

Across 1 battle in the Pantheon.

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Q&A

Why You Should Use This?

| Audience | Key Benefits | | -------------------- | ------------------------------------------------------------------------------------------------- | | Individual | 30%+ token savings, stop repeating standards, security guardrails, instant scaffolding | | Teams | Consistent AI behavior, day‑one onboarding, codified patterns, compounding cost savings | | Organizations | Always‑on compliance, same standards across 1,000 projects, central governance, measurable ROI |

Asked by Chioma Nwosu · Jul 12, 2026

What it detects?

| Category | Signals | | ------------------ | ------------------------------------------------------------------------------------------ | | Language | tsconfig.json, go.mod, Cargo.toml, requirements.txt, pom.xml, file extensions | | Framework | Dependencies in package.json / requirements.txt (React, Express, Django, Spring, etc.) | | Database | ORM configs (prisma/, sequelize, typeorm), .sql files, migration folders | | Testing | jest.config., vitest, pytest, cypress/, playwright.config. | | Infrastructure | Dockerfile, terraform/, cdk.json, serverless.yml, cloud SDK deps | | CI/CD | .github/workflows/, .gitlab-ci.yml, Jenkinsfile |

Asked by Vikram Rao · Jul 10, 2026

What You Can Customize?

| Section | What It Controls | Example | | ------------- | -------------------------- | ---------------------------------------------- | | project | Project identity | Name, description, repo URL | | techStack | Language, framework, tools | TypeScript + Express or Python + FastAPI | | paths | Directory structure | Where handlers, services, and common code live | | domain | Business entities | Order, Product, Customer + lifecycle states | | patterns | Code patterns | 7‑step handler flow, error handling strategy | | testing | Quality gates | 90% coverage, test/lint/type‑check commands | | database | DB conventions | Soft delete field, timestamp columns, naming | | packages | Internal packages | @your‑org scope, registry URL | | conventions | Git and workflow | Branch prefixes, commit format, PR templates |

Asked by Rina Desai · Jul 11, 2026

What's Inside?

| Layer | Count | What It Does | How It's Triggered | | ------------- | ----- | ------------------------------------------------- | ----------------------------------- | | Rules | 47 | Enforces coding standards on every AI interaction | Automatically — always on | | Agents | 62 | Specialized assistants for complex tasks | On demand — /agent-name | | Skills | 50 | Step‑by‑step guided workflows with checklists | Contextually — when patterns match | | Commands | 37 | Lightweight, token‑efficient quick actions | On demand — /command | | Hooks | 12 | Automation scripts in the AI loop | Event‑driven — before/after actions | | Templates | 9 | Scaffolding for handlers, components, tests, etc. | Referenced by skills and agents |

Asked by Gabriel Duarte · Jun 1, 2026

How It Works?

Layer 1 — Pre-Processing: Hooks inject project context and block dangerous commands before your prompt reaches the AI. Layer 2 — Rules Engine: 47 always‑on rules enforce token efficiency, security, architecture, code standards, database conventions, and testing thresholds. Layer 3 — Specialized Processing: The right component activates — an agent, skill, or command — based on your prompt. Layer 4 — Post-Processing: Hooks validate output, auto‑format, scan for secrets, and verify coverage.

Asked by Daniel Schmidt · May 24, 2026

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