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 DevelopersGlobal/ai-agent-skills --skill documentationgit clone --depth 1 https://github.com/DevelopersGlobal/ai-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/developersglobal/ai-agent-skills/documentation)<a href="https://agentmods.dev/skills/developersglobal/ai-agent-skills/documentation"><img src="https://agentmods.dev/badge/skills/developersglobal/ai-agent-skills/documentation/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/developersglobal/ai-agent-skills/documentation"><img src="https://agentmods.dev/badge/skills/developersglobal/ai-agent-skills/documentation.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 | $0.00030 | $0.00707 |
| Opus 5.5 | $0.00012 | $0.00283 |
| Sonnet 5.5 | $0.00006 | $0.00141 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
documentation 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.
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.
Overview
Code explains what. Documentation explains why. The most valuable documentation records decisions that aren't obvious from reading the code: why this architecture, why this tradeoff, why not the obvious alternative.
When to Use
- After any significant architectural decision
- Before complex code that future maintainers will question
- When an operational procedure isn't self-evident
- When a non-obvious tradeoff was made
Process
Step 1: Architectural Decision Records (ADRs)
For every significant architectural decision:
- Write an ADR with:
- Context: What was the situation requiring a decision?
- Decision: What was decided?
- Alternatives considered: What else was evaluated and why rejected?
- Consequences: What are the positive and negative consequences?
- Status: Proposed | Accepted | Deprecated | Superseded
- Store ADRs in
docs/decisions/as numbered markdown files.
Verify: Every significant decision in the last sprint has an ADR.
Step 2: Code-Level Documentation
- Document the WHY, not the WHAT:
- ✅
// Using exponential backoff here — the payment API has strict rate limits (3 req/sec) - ❌
// Retry the request
- ✅
- Document non-obvious algorithmic choices.
- Document external constraints (rate limits, API quirks, platform limitations).
- Remove comments that state the obvious — they add noise.
Verify: Every non-obvious code block has a "why" comment.
Step 3: Runbooks
- For every production process that humans execute, write a runbook:
- When is this runbook used?
- What steps to execute?
- What does "done" look like?
- What could go wrong and how to recover?
- Runbooks live in
docs/runbooks/.
Verify: Every on-call alert has a linked runbook.
Step 4: README Currency
- README reflects current state (not v1 state).
- Setup instructions work on a fresh machine.
- Architecture diagram updated after significant changes.
Common Rationalizations (and Rebuttals)
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 · 30 tokens per session scan A e271da494001
documentation is a skill published in the GitHub repository DevelopersGlobal/ai-agent-skills (177 stars, last pushed 5mo ago), licensed MIT. It adds 30 tokens to every session and 707 once invoked, about $0.0001 per session on Opus 5.5. 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
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
doubt-driven-development
Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when you want every assumption cross-examined before proceeding, when stress-testing a plan for hidden failure modes, when correctness matters more than speed, when working in unfamiliar code, when stakes are high…
git-workflow-and-versioning
Structures git workflow practices. Use when making any code change. Use when committing, branching, resolving conflicts, splitting uncommitted work in a messy working tree into clean atomic commits, opening or reviewing a pull request (PR), pushing to a remote, or when you need to organize work across multiple…
observability-and-instrumentation
Instruments code so production behavior is visible and diagnosable. Use when adding logging, metrics, tracing, or alerting. Use when shipping any feature that runs in production and you need evidence it works. Use when production issues are reported but you can't tell what happened from the available data.
performance-optimization
Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.
spec-driven-development
Creates specs before coding. Use when starting a new project, feature, or significant change and no specification exists yet. Use when drafting a PRD or requirements document with objectives and scope, or when requirements are unclear, ambiguous, or only exist as a vague idea. Use when a single requirement spans…