addyosmani/agent-skills is a collection of reusable workflows, quality checks, commands, and other instructions that guide AI coding agents through software development. It is for developers who want agents to follow consistent engineering practices, and the catalogue entries are its packaged skills, commands, agents, plugins, instructions, and hooks.
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 addyosmani/agent-skills --skill documentation-and-adrsgit clone --depth 1 https://github.com/addyosmani/agent-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/addyosmani/agent-skills/documentation-and-adrs)<a href="https://agentmods.dev/skills/addyosmani/agent-skills/documentation-and-adrs"><img src="https://agentmods.dev/badge/skills/addyosmani/agent-skills/documentation-and-adrs/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/addyosmani/agent-skills/documentation-and-adrs"><img src="https://agentmods.dev/badge/skills/addyosmani/agent-skills/documentation-and-adrs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00058 | $0.02154 |
| Opus 5 | $0.00029 | $0.01077 |
| Sonnet 5 | $0.00012 | $0.00431 |
| Haiku 4.5 | $0.00006 | $0.00215 |
Grade A, and why
documentation-and-adrs 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 3d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- documentation-and-adrs — 100% identical, 2 lines differ
- documentation-and-adrs — 100% identical, 2 lines differ
- documentation-and-adrs — 100% identical, 2 lines differ
- documentation-and-adrs — 100% identical, 2 lines differ
- documentation-and-adrs — 100% identical, 2 lines differ
- documentation-and-adrs — 97% identical, 1 lines differ
- documentation-and-adrs — 97% identical, 3 lines differ
- documentation-and-adrs — 94% identical, 7 lines differ
How it starts
The opening of the file, as written. The whole thing — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Documentation and ADRs
Overview
Document decisions, not just code. The most valuable documentation captures the why — the context, constraints, and trade-offs that led to a decision. Code shows what was built; documentation explains why it was built this way and what alternatives were considered. This context is essential for future humans and agents working in the codebase.
When to Use
- Making a significant architectural decision
- Choosing between competing approaches
- Adding or changing a public API
- Shipping a feature that changes user-facing behavior
- Onboarding new team members (or agents) to the project
- When you find yourself explaining the same thing repeatedly
When NOT to use: Don't document obvious code. Don't add comments that restate what the code already says. Don't write docs for throwaway prototypes.
Architecture Decision Records (ADRs)
ADRs capture the reasoning behind significant technical decisions. They're the highest-value documentation you can write.
When to Write an ADR
- Choosing a framework, library, or major dependency
- Designing a data model or database schema
- Selecting an authentication strategy
- Deciding on an API architecture (REST vs. GraphQL vs. tRPC)
- Choosing between build tools, hosting platforms, or infrastructure
- Any decision that would be expensive to reverse
Match the existing convention first
Before creating an ADR, inspect the available repository context for an established convention — existing ADRs, project instructions, and ADR-related configuration or tooling (e.g. an .adr-dir file). An established convention overrides the defaults below. Match:
- Location and format — e.g.
docs/adr/*.md,Documentation/Decisions/*.rst, a MADR layout, or anadr-toolssetup. Match the existing directory, file extension, and markup (Markdown vs reStructuredText). - Numbering and naming — continue the existing sequence and filename pattern (
ADR-004-Title.rst,0004-title.md, …); don't restart at 001 or introduce a second scheme. - Section headings — reuse the project's heading set rather than imposing this template's.
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.
- 3d ago Changed · +15 tokens per session 87ae44a0c7bb
- 13d ago First seen · 289 lines · 43 tokens per session scan A b867bb80fb68
documentation-and-adrs is a skill published in the GitHub repository addyosmani/agent-skills (93,568 stars, last pushed 4d ago), licensed MIT. It adds 58 tokens to every session and 2,154 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
ai-output-validation
Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and fallback handling.
test-driven-development
Drives development with tests via Red-Green-Refactor and the Prove-It pattern, with hard rules against weakening assertions or faking green suites. Use when implementing any logic, fixing any bug, or changing any behavior. Triggers on "add a feature", "fix this bug", "write tests", or any task where done must be…
error-handling
Graceful degradation and meaningful error messages. Errors are first-class citizens, not afterthoughts. Every error path is designed, not discovered.
goal-driven-execution
Transforms imperative instructions into declarative goals with verifiable success criteria. Enables autonomous looping until verified completion.
think-before-coding
Forces explicit reasoning before writing any code. Surfaces assumptions, manages confusion, and prevents hallucination by demanding clarity upfront.
ai-ops
Guides operational excellence for AI/ML systems in production. Use when deploying models, managing inference infrastructure, monitoring model drift, or maintaining AI-powered features. Use when you need reliable, observable, and governable machine learning systems.