Borrowing it
Nothing to install: this file belongs to diillson/chatcli. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/diillson/chatcli/main/.agent/skills/api-patterns/SKILL.mdgit clone --depth 1 https://github.com/diillson/chatcliWrote 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/diillson/chatcli/api-patterns)<a href="https://agentmods.dev/skills/diillson/chatcli/api-patterns"><img src="https://agentmods.dev/badge/skills/diillson/chatcli/api-patterns/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/diillson/chatcli/api-patterns"><img src="https://agentmods.dev/badge/skills/diillson/chatcli/api-patterns.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 | $0.00028 | $0.00581 |
| Opus 5.5 | $0.00011 | $0.00232 |
| Sonnet 5.5 | $0.00006 | $0.00116 |
| Haiku 4.5 | $0.00003 | $0.00058 |
Grade A, and why
api-patterns 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.
This is a copy
77% identical to api-patterns — 32 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
API Patterns
API design principles and decision-making for 2025. Learn to THINK, not copy fixed patterns.
🎯 Selective Reading Rule
Read ONLY files relevant to the request! Check the content map, find what you need.
📑 Content Map
| File | Description | When to Read |
|---|---|---|
api-style.md |
REST vs GraphQL vs tRPC decision tree | Choosing API type |
rest.md |
Resource naming, HTTP methods, status codes | Designing REST API |
response.md |
Envelope pattern, error format, pagination | Response structure |
graphql.md |
Schema design, when to use, security | Considering GraphQL |
trpc.md |
TypeScript monorepo, type safety | TS fullstack projects |
versioning.md |
URI/Header/Query versioning | API evolution planning |
auth.md |
JWT, OAuth, Passkey, API Keys | Auth pattern selection |
rate-limiting.md |
Token bucket, sliding window | API protection |
documentation.md |
OpenAPI/Swagger best practices | Documentation |
security-testing.md |
OWASP API Top 10, auth/authz testing | Security audits |
🔗 Related Skills
| Need | Skill |
|---|---|
| API implementation | @[skills/backend-development] |
| Data structure | @[skills/database-design] |
| Security details | @[skills/security-hardening] |
✅ Decision Checklist
Before designing an API:
- Asked user about API consumers?
- Chosen API style for THIS context? (REST/GraphQL/tRPC)
- Defined consistent response format?
- Planned versioning strategy?
- Considered authentication needs?
- Planned rate limiting?
- Documentation approach defined?
❌ Anti-Patterns
DON'T:
- Default to REST for everything
- Use verbs in REST endpoints (/getUsers)
- Return inconsistent response formats
- Expose internal errors to clients
- Skip rate limiting
DO:
- Choose API style based on context
- Ask about client requirements
- Document thoroughly
- Use appropriate status codes
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 82 lines · 28 tokens per session scan A d8c2cbe8a096
api-patterns is a skill published in the GitHub repository diillson/chatcli (93 stars, last pushed yesterday), licensed Apache-2.0. It adds 28 tokens to every session and 581 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 77% identical to api-patterns, differing in 32 lines, and is treated as a copy.
Other skills, from other repositories
01-bootstrap
Design and validate a new SaaS's architecture into an INSTALL.md via Q&A and stack comparison. Use when the user starts a project, chooses a stack, or picks an architecture pattern. Not for editing an existing stack or scaffolding code.
agent-state-bar
A design guide for an agent status bar: a short, automatically updated summary of progress, remaining tasks, tool counts, errors, and system state placed near the latest model input.
reward-design
A guide to defining the scores that an AI agent receives during trial-and-error training. It covers checks based on rules, human preferences, or another model, and distinguishes rewarding the final result from rewarding the steps taken.
bad-case-to-dpo
A guide to turning failed production agent runs into training and evaluation data. It explains how to find the first mistake, create preference examples for DPO (Direct Preference Optimization), and check that a fix does not damage other tasks.
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.
blind-sqli
Blind SQL injection under hostile WAF — manual bypass playbook for when sqlmap fails because common tokens (SUBSTRING, IF, AND, WHERE, single quotes) are filtered. Covers token-fingerprinting probe loops, arithmetic-multiplication boolean evaluation, hex-encoded literals, and exponential-probe binary search. Loaded on…