Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/jhlee0409/claude-pluginsnpx agentmods add skills/jhlee0409/claude-plugins/code-generatorWrote 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/skills/jhlee0409/claude-plugins/code-generator)<a href="https://agentmods.dev/skills/jhlee0409/claude-plugins/code-generator"><img src="https://agentmods.dev/badge/skills/jhlee0409/claude-plugins/code-generator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jhlee0409/claude-plugins/code-generator"><img src="https://agentmods.dev/badge/skills/jhlee0409/claude-plugins/code-generator.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00013 | $0.02208 |
| Opus 5.5 | $0.00005 | $0.00883 |
| Sonnet 5.5 | $0.00003 | $0.00442 |
| Haiku 4.5 | $0.00001 | $0.00221 |
Grade A, and why
code-generator scanned grade A with 0 findings 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 1mo ago.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Generator
Generate API code that perfectly matches your project's existing patterns.
Core Principle: Your code style is the template.
EXECUTION INSTRUCTIONS
When this skill is invoked, Claude MUST perform these steps in order:
Step 1: Receive Inputs
This skill requires:
- Detected patterns from
pattern-detectorskill - Parsed endpoints from
openapi-parserskill - Target tag or list of endpoints to generate
Verify all inputs are present before proceeding.
Step 2: Load Sample Code
-
Read the sample files from detected patterns:
samples.api→ API function patternssamples.types→ Type definition patternssamples.hooks→ Hook patterns (if applicable)
-
For each sample, extract:
- Import statements
- Export patterns
- Function/type structure
- Naming conventions
- Code style (quotes, semicolons, indentation)
Step 3: Generate File Paths
Based on detected structure pattern, generate target file paths:
FSD Pattern:
src/entities/{tag}/
├── api/
│ ├── {tag}-api.ts
│ ├── {tag}-api-paths.ts
│ └── {tag}-queries.ts
└── model/
└── {tag}-types.ts
Feature-based Pattern:
src/features/{tag}/
├── api.ts
├── hooks.ts
└── types.ts
Flat Pattern:
src/api/{tag}/
├── api.ts
├── hooks.ts
└── types.ts
Step 4: Generate Types
For each schema used by target endpoints:
- Determine type style from sample (interface vs type)
- Generate TypeScript definition:
// If sample uses interface:
export interface User {
id: string
name: string
email?: string // optional if not in required array
}
// If sample uses type:
export type User = {
id: string
name: string
email?: string
}
- Generate Request/Response types:
- Follow naming convention from sample
- Examples:
GetUserRequest,CreateUserRequest,UserResponse
Step 5: Generate Path Constants
Clone the path constant pattern from sample:
// Sample pattern: function-based
export const USER_PATHS = {
list: () => '/api/v1/users',
detail: (id: string) => `/api/v1/users/${id}`,
create: () => '/api/v1/users',
} as const
// Generated: same pattern for new tag
export const PROJECT_PATHS = {
list: () => '/api/v1/projects',
detail: (id: string) => `/api/v1/projects/${id}`,
create: () => '/api/v1/projects',
} as const
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.
- 1mo ago First seen · 352 lines · 13 tokens per session scan A 7c9824868059
code-generator is a skill published in the GitHub repository jhlee0409/claude-plugins (4 stars, last pushed 9mo ago), licensed MIT. It adds 13 tokens to every session and 2,208 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
fastapi
FastAPI best practices and conventions. Use when working with FastAPI APIs, Pydantic models, dependencies, streaming responses including Server-Sent Events (SSE), and serving frontend apps. Keeps FastAPI code clean and up to date with the latest features and patterns.
printing-press-amend
Amend a published CLI from one of two input sources: (1) dogfood mode mines the active Claude Code session transcript for friction (missing flags, hand- rolled API payloads, silent-null returns); (2) direct-input mode accepts user-supplied asks (rename a command, add commands or feeds, fix a named bug, optionally…
datamodel-code-generator
Use this skill when the user wants Python data models, Pydantic models, dataclasses, TypedDicts, msgspec structs, or type-safe Python classes generated from OpenAPI, AsyncAPI, JSON Schema, GraphQL, JSON/YAML/CSV sample data, MCP tool schemas, Protocol Buffers, XML Schema, Apache Avro, or existing Python model objects.…
printing-press
Set up a new integration, connector, or CLI binding for any API. Wrap or generate a ship-ready Go CLI from an OpenAPI, HAR, or Postman spec via the lean research -> generate -> build -> shipcheck loop. Use when the user says build a CLI, wrap this API, set up a new integration, add a connector, integrate with a…
implementing-api-schema-validation-security
Implement API schema validation using OpenAPI specifications and JSON Schema to enforce input/output contracts and prevent injection, data exposure, and mass assignment attacks.
emulate-seed
Generate emulate seed configs for stateful API emulation. Wraps Vercel's emulate tool for GitHub, Vercel, Google OAuth, Slack, Apple Auth, Microsoft Entra, AWS, Okta, Clerk, Resend, Stripe, and MongoDB Atlas APIs — full state machines, not mocks. Use when setting up test environments, CI pipelines, integration tests…