Atlas / Skills / addyosmani / Documentation And Adrs

Documentation And AdrsSAFE

skills/addyosmani/documentation-and-adrs

Production-grade engineering skills for AI coding agents.

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
MIT
Stars
99,537
01

Overview

Production-grade engineering skills for AI coding agents.

Read from source at commit 9be8f7674e77OBSERVED · 2026-09-28
02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: documentation-and-adrs
description: Records decisions and documentation. Use when you need to document an architecture decision (ADR) or the reasoning behind a design choice, when changing public APIs, shipping features, or when you need to record context that future engineers and agents will need to understand the codebase.
---

# 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 an `adr-tools` setup. 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.

If the available evidence conflicts, surface the conflict rather than silently introducing another scheme. Only when no convention can be established do you apply the default below.

### ADR Template

Store ADRs in `docs/decisions/` with sequential numbering (unless the project already uses another location — see above):

```markdown
# ADR-001: Use PostgreSQL for primary database

## Status
Accepted | Superseded by ADR-XXX | Deprecated

## Date
2025-01-15

## Context
We need a primary database for the task management application. Key requirements:
- Relational data model (users, tasks, teams with relationships)
- ACID transactions for task state changes
- Support for full-text search on task content
- Managed hosting available (for small team, limited ops capacity)

## Decision
Use PostgreSQL with Prisma ORM.

## Alternatives Considered

### MongoDB
- Pros: Flexible schema, easy to start with
- Cons: Our data is inherently relational; would need to manage relationships manually
- Rejected: Relational data in a document store leads to complex joins or data duplication

### SQLite
- Pros: Zero configuration, embedded, fast for reads
- Cons: Limited concurrent write support, no managed hosting for production
- Rejected: Not suitable for multi-user web application in production

### MySQL
- Pros: Mature, widely supported
- Cons: PostgreSQL has better JSON support, full-text search, and ecosystem tooling
- Rejected: PostgreSQL is the better fit for our feature requirements

## Consequences
- Prisma provides type-safe database access and migration management
- We can use PostgreSQL's full-text search instead of adding Elasticsearch
- Team needs PostgreSQL knowledge (standard skill, low risk)
- Hosting on managed service (Supabase, Neon, or RDS)
```

### ADR Lifecycle

```
PROPOSED → ACCEPTED → (SUPERSEDED or DEPRECATED)
```

- **Don't delete old ADRs.** They capture historical context.
- When a decision changes, write a new ADR that references and supersedes the old one.

## Inline Documentation

### When to Comment

Comment the *why*, not the *what*:

```typescript
// BAD: Restates the code
// Increment counter by 1
counter += 1;

// GOOD: Explains non-obvious intent
// Rate limit uses a sliding window — reset counter at window boundary,
// not on a fixed schedule, to prevent burst attacks at window edges
if (now - windowStart > WINDOW_SIZE_MS) {
  counter = 0;
  windowStart = now;
}
```

### When NOT to Comment

```typescript
// Don't comment self-explanatory code
function calculateTotal(items: CartItem[]): number {
  return items.reduce((sum, item) => sum + item.price * item.quantity, 0);
}

// Don't leave TODO comments for things you should just do now
// TODO: add error handling  ← Just add it

// Don't leave commented-out code
// const oldImplementation = () => { ... }  ← Delete it, git has history
```

### Document Known Gotchas

```typescript
/**
 * IMPORTANT: This function must be called before the first render.
 * If called after hydration, it causes a flash of unstyled content
 * because the theme context isn't available during SSR.
 *
 * See ADR-003 for the full design rationale.
 */
export function initializeTheme(theme: Theme): void {
  // ...
}
```

## API Documentation

For public APIs (REST, GraphQL, library interfaces):

### Inline with Types (Preferred for TypeScript)

```typescript
/**
 * Creates a new task.
 *
 * @param input - Task creation data (title required, description optional)
 * @returns The created task with se
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryWARN
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (1)

MEDIUMInventory / provenance · inv.symlink · CWE-1104
.opencode/skills
.opencode/skills
Why it matters. link not followed

Gates applied: no_behavioural_pass.

Audited 2026-09-28 · audit v0.4.1 · source sha 9be8f7674e77full audit observations/trust-audit/skill/addyosmani__documentation-and-adrs.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-09-289be8f7674e77SAFEB89first audit
05

Questions

What does the Documentation And Adrs skill do?

Production-grade engineering skills for AI coding agents.

Is Documentation And Adrs safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Documentation And Adrs access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (9be8f7674e77), read on 2026-09-28. The repository is watched, and a new audit runs when it changes — this is the first audit.

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