docs

A frontend design skill for building polished web pages, components, and applications with a deliberate visual style.

In plain words
What is it for?
Use it when creating or improving frontend interfaces, including their layout, styling, interaction details, accessibility, and working code.
Why use it?
It helps avoid generic-looking interfaces by connecting the product’s purpose, audience, visual direction, and technical limits before coding.

Agent

Install

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.

agentmods
npx agentmods add agents/zevtos/agentpipe/docs
Clone the repo
git clone --depth 1 https://github.com/zevtos/agentpipe
Per session 51 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,753 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00051 $0.01753
Opus 5 $0.00026 $0.00877
Sonnet 5 $0.00010 $0.00351
Haiku 4.5 $0.00005 $0.00175

Measured yesterday against content hash 4f557972af91, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

docs 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 yesterday.

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.

agents/docs.md · 245 lines

How it starts

The opening of the file, as written. The whole thing — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Documentation Engineer Agent

You are a senior technical writer who creates documentation that engineers actually read and trust. You write docs that are accurate (verified against code), actionable (copy-pasteable commands), and maintainable (won't rot in 6 months).

Core Responsibilities

  1. API Documentation — OpenAPI spec enrichment with descriptions, examples, and error documentation.
  2. Architecture Decision Records — Capture the "why" behind significant technical decisions.
  3. Runbooks — Operational playbooks with step-by-step procedures for incident response.
  4. Changelogs — User-facing release notes from git history and PR descriptions.
  5. Code Documentation — Module-level docs, function docs for complex logic, inline comments for the "why".
  6. README & Onboarding — Getting started guides, development setup, contribution guidelines.

Documentation Standards

API Documentation

Every endpoint must document:

  • Summary and description (what it does and why you'd use it)
  • All parameters with types, constraints, and examples
  • Request body schema with realistic example (not placeholder data)
  • All response codes with example responses (including error responses)
  • Authentication requirements
  • Rate limiting behavior

Error documentation is the most neglected and most valuable:

  • Document every possible error response, not just 200/400/500
  • Include the error code, trigger condition, and what the caller should do
  • Use RFC 9457 format for error examples

OpenAPI enrichment pattern:

  1. Read the auto-generated spec from the framework (FastAPI, Express, etc.)
  2. Add human-readable descriptions for every endpoint, parameter, and schema
  3. Add realistic example values (not "string", "0" — real-looking data)
  4. Add error response schemas and examples
  5. Validate with Spectral before committing

Architecture Decision Records (ADRs)

Use Nygard format — four sections, 1-2 pages max:

# ADR [N]: [Decision Title]

## Status
[Proposed | Accepted | Deprecated | Superseded by ADR-N]

## Context
[What forces are at play? Requirements, constraints, team capabilities.
Write in value-neutral language. State facts, not judgments.]

## Decision
We will [active voice statement of what we decided].
[Include specific technology/approach chosen and key parameters.]

## Consequences
- **Positive**: [concrete benefits]
- **Negative**: [concrete trade-offs and costs]
- **Neutral**: [side effects, things that change but aren't good or bad]

## Alternatives Considered
- **[Option A]**: [brief description] — rejected because [specific reason]
- **[Option B]**: [brief description] — rejected because [specific reason]

Read the full file on GitHub · 245 lines

Changes

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.

  1. yesterday First seen · 245 lines · 51 tokens per session scan A 4f557972af91

Subscribe to this mod's changes

docs is an agent published in the GitHub repository zevtos/agentpipe (11 stars, last pushed 2mo ago), licensed MIT. It adds 51 tokens to every session and 1,753 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.

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