Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/kumaran-is/claude-code-onboarding/doc-generategit clone --depth 1 https://github.com/kumaran-is/claude-code-onboardingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/kumaran-is/claude-code-onboarding/doc-generate)<a href="https://agentmods.dev/commands/kumaran-is/claude-code-onboarding/doc-generate"><img src="https://agentmods.dev/badge/commands/kumaran-is/claude-code-onboarding/doc-generate.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00032 | $0.01044 |
| Opus 5 | $0.00016 | $0.00522 |
| Sonnet 5 | $0.00006 | $0.00209 |
| Haiku 4.5 | $0.00003 | $0.00104 |
Grade A, and why
doc-generate scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s http://localhost:8000/openapi.json > docs/api/openapi.json 2>/dev/null || echo "[NOTE] Start the service first to export live spec" How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Documentation Generation
Generate or refresh all documentation for the target project or path.
Target: $ARGUMENTS (default: current directory).
Step 1: Detect project type
TARGET="${ARGUMENTS:-.}"
HAS_JAVA=false; HAS_NODE=false; HAS_PYTHON=false; HAS_FLUTTER=false
[ -f "$TARGET/pom.xml" ] && HAS_JAVA=true && echo "Java/Spring detected"
[ -f "$TARGET/package.json" ] && HAS_NODE=true && echo "NestJS/Angular detected"
[ -f "$TARGET/pyproject.toml" ] || [ -f "$TARGET/requirements.txt" ] && HAS_PYTHON=true && echo "Python detected"
[ -f "$TARGET/pubspec.yaml" ] && HAS_FLUTTER=true && echo "Flutter detected"
Step 2: API documentation
Load the openapi-spec-generation skill, then:
- Java/Spring Boot: Scan controllers for
@RestController,@GetMapping,@PostMapping, etc. Extract route paths, request/response types, and generate or updatedocs/api/openapi.yaml. - NestJS: Scan
*.controller.tsfor@Controller,@Get,@Postdecorators. Extract DTOs and generate or updatedocs/api/openapi.yaml. - Python FastAPI: Confirm
/openapi.jsonis served at runtime (built-in). Export the spec with:curl -s http://localhost:8000/openapi.json > docs/api/openapi.json 2>/dev/null || echo "[NOTE] Start the service first to export live spec" - Flutter: Skip API generation (client-side).
Step 3: Architecture diagram
Dispatch mermaid-expert agent to create a system diagram from the project structure:
- Scan top-level directories and entry points
- Identify key services, controllers, models, and external dependencies
- Produce a
graph TBdiagram showing components and data flows
Output to docs/diagrams/[detected-service-name]-architecture.md.
Step 4: README
Check if README.md exists:
ls $TARGET/README.md 2>/dev/null && echo "README exists" || echo "README missing"
- If missing: Generate from the stack-specific template in
skills/documentation-generation/references/readme-templates.md. - If exists: Check for stale sections:
- Missing environment variables (grep
process.env/os.environ/@Valueagainst README) - Outdated build commands (compare against
package.json scripts/pom.xml/pyproject.toml) - Report stale sections without overwriting (present diff to user for review)
- Missing environment variables (grep
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 108 lines · 32 tokens per session scan A 25ddd7e3e749
doc-generate is a command published in the GitHub repository kumaran-is/claude-code-onboarding (35 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 1,044 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.