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 skills add autohandai/community-skills --skill authentication-setupgit clone --depth 1 https://github.com/autohandai/community-skillsWrote 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/autohandai/community-skills/authentication-setup)<a href="https://agentmods.dev/skills/autohandai/community-skills/authentication-setup"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/authentication-setup/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/autohandai/community-skills/authentication-setup"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/authentication-setup.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 | $0.00044 | $0.04645 |
| Opus 5 | $0.00022 | $0.02322 |
| Sonnet 5 | $0.00009 | $0.00929 |
| Haiku 4.5 | $0.00004 | $0.00464 |
Grade A, and why
authentication-setup 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 5d 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 — 667 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Authentication Setup
When to use this skill
Lists specific situations where this skill should be triggered:
- User Login System: When adding user authentication to a new application
- API Security: When adding an authentication layer to a REST or GraphQL API
- Permission Management: When role-based access control is needed
- Authentication Migration: When migrating an existing auth system to JWT or OAuth
- SSO Integration: When integrating social login with Google, GitHub, Microsoft, etc.
Input Format
The required and optional input information to collect from the user:
Required Information
- Authentication Method: Choose from JWT, Session, or OAuth 2.0
- Backend Framework: Express, Django, FastAPI, Spring Boot, etc.
- Database: PostgreSQL, MySQL, MongoDB, etc.
- Security Requirements: Password policy, token expiry times, etc.
Optional Information
- MFA Support: Whether to enable 2FA/MFA (default: false)
- Social Login: OAuth providers (Google, GitHub, etc.)
- Session Storage: Redis, in-memory, etc. (if using sessions)
- Refresh Token: Whether to use (default: true)
Input Example
Build a user authentication system:
- Auth method: JWT
- Framework: Express.js + TypeScript
- Database: PostgreSQL
- MFA: Google Authenticator support
- Social login: Google, GitHub
- Refresh Token: enabled
Instructions
Specifies the step-by-step task sequence to follow precisely.
Step 1: Design the Data Model
Design the database schema for users and authentication.
Tasks:
- Design the User table (id, email, password_hash, role, created_at, updated_at)
- RefreshToken table (optional)
- OAuthProvider table (if using social login)
- Never store passwords in plaintext (bcrypt/argon2 hashing is mandatory)
Example (PostgreSQL):
CREATE TABLE users (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
email VARCHAR(255) UNIQUE NOT NULL,
password_hash VARCHAR(255), -- NULL if OAuth only
role VARCHAR(50) DEFAULT 'user',
is_verified BOOLEAN DEFAULT false,
mfa_secret VARCHAR(255),
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
CREATE TABLE refresh_tokens (
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
user_id UUID REFERENCES users(id) ON DELETE CASCADE,
token VARCHAR(500) UNIQUE NOT NULL,
expires_at TIMESTAMP NOT NULL,
created_at TIMESTAMP DEFAULT NOW()
);
CREATE INDEX idx_users_email ON users(email);
CREATE INDEX idx_refresh_tokens_user_id ON refresh_tokens(user_id);
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.
- 5d ago First seen · 667 lines · 44 tokens per session scan A 3c612477d65b
authentication-setup is a skill published in the GitHub repository autohandai/community-skills (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 44 tokens to every session and 4,645 once invoked, about $0.0002 per session on Opus 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 2026-09-03.
Other skills, from other repositories
amplify-workflow
Build and deploy full-stack web and mobile apps with AWS Amplify Gen2 (TypeScript code-first). Covers auth (Cognito), data (AppSync/DynamoDB including schema modeling, enum types, relationships, authorization rules), storage (S3), functions, APIs, and AI (Amplify AI Kit with Bedrock). Supports React, Next.js, Vue…
aws-lambda-durable-functions
Build resilient, long-running, multi-step applications with AWS Lambda durable functions with automatic state persistence, retry logic, and orchestration for long-running executions. Covers the critical replay model, step operations, wait/callback patterns, error handling with saga pattern, testing with…
api-gateway
Build, manage, and operate APIs with Amazon API Gateway (REST, HTTP, and WebSocket). Triggers on phrases like: API Gateway, REST API, HTTP API, WebSocket API, custom domain, Lambda authorizer, usage plan, throttling, CORS, VPC link, private API. Also covers troubleshooting API Gateway errors (4xx, 5xx, timeout, CORS…
amazon-location-service
Integrates Amazon Location Service APIs for AWS applications. Use this skill when users want to add maps (interactive MapLibre or static images); geocode addresses to coordinates or reverse geocode coordinates to addresses; calculate routes, travel times, or service areas; find places and businesses through text…
aws-lambda
Design, build, deploy, test, and debug serverless applications with AWS Lambda. Triggers on phrases like: Lambda function, event source, serverless application, API Gateway, EventBridge, Step Functions, serverless API, event-driven architecture, Lambda trigger. For deploying non-serverless apps to AWS, use…
agentsop-http-tool-wrapping
Decision protocol for wrapping a REST / GraphQL / RPC API as a tool an LLM agent can call. The load-bearing premise: the tool surface is an LM-friendly subset of the API surface — one tool per user intent, not one per endpoint. Activates when a coder agent must expose an external HTTP API to a model (function calling…