agentic-coding-playbook: Skill for Claude Code

.agents/skills/federal-decision-records/SKILL.md

federal-decision-records is a skill for Claude Code, Codex from GSA-TTS/agentic-coding-playbook. It costs 27 tokens per session (1,758 once invoked), scanned A, original, CC0-1.0.

A record of an architecture or security choice, written in MADR (Markdown Any Decision Records) format and extended for federal compliance. It can create, check, and list these records.

In plain words
What is it for?
Documenting decisions about AI-agent permissions, data classification, infrastructure, authentication, or cryptography. It also helps explain how identified risks were treated.
Why use it?
It keeps the reason for a technical choice, its risks, and related NIST security controls in one audit trail. This helps prepare documentation for an Authority to Operate (ATO) review.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents); mentions AGENTS.md.

This is GSA-TTS/agentic-coding-playbook's own configuration. It tells Claude Code and Codex how to work on agentic-coding-playbook itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything agentic-coding-playbook configures →

Reuse

Borrowing it

Nothing to install: this file belongs to GSA-TTS/agentic-coding-playbook. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/GSA-TTS/agentic-coding-playbook/main/.agents/skills/federal-decision-records/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/GSA-TTS/agentic-coding-playbook

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for federal-decision-records

README.md
[![agentmods](https://agentmods.dev/badge/skills/gsa-tts/agentic-coding-playbook/federal-decision-records/github.svg)](https://agentmods.dev/skills/gsa-tts/agentic-coding-playbook/federal-decision-records)
Your own site
<a href="https://agentmods.dev/skills/gsa-tts/agentic-coding-playbook/federal-decision-records"><img src="https://agentmods.dev/badge/skills/gsa-tts/agentic-coding-playbook/federal-decision-records/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.

agentmods 80×15 button for federal-decision-records

Your own site · 80×15
<a href="https://agentmods.dev/skills/gsa-tts/agentic-coding-playbook/federal-decision-records"><img src="https://agentmods.dev/badge/skills/gsa-tts/agentic-coding-playbook/federal-decision-records.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,758 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00027 $0.01758
Opus 5 $0.00014 $0.00879
Sonnet 5 $0.00005 $0.00352
Haiku 4.5 $0.00003 $0.00176

Measured 10d ago against content hash 9e7872ed09b2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

federal-decision-records 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 10d 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.

.agents/skills/federal-decision-records/SKILL.md · 212 lines

How it starts

The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Federal Decision Records

This skill helps users create, validate, and index architectural and security decision records using MADR (Markdown Any Decision Records) format with federal compliance extensions.

Decision records provide an audit trail linking design choices to NIST controls and risk treatment rationale — required for ATO documentation.

When to Use

  • Documenting a decision about AI agent authorization or capabilities
  • Recording data handling or classification decisions
  • Capturing deployment or infrastructure security choices
  • Documenting cryptographic or authentication design decisions
  • Preparing audit trail for ISSO or ATO reviewer
  • When the federal-risk-assessment skill identifies risks that require treatment decisions — document the treatment rationale here

How It Works

This skill has three modes:

  1. Create — Guide the user through creating a new decision record
  2. Validate — Check existing ADRs for format and completeness
  3. Index — Generate a decision record index from frontmatter

Ask the user which mode they need, or infer from context.

Mode 1: Create a Decision Record

Step 1: Determine Decision Category

Ask the user what kind of decision they are documenting. Present the categories from references/DECISION_CATEGORIES.md:

"What category does this decision fall into?"

Read references/DECISION_CATEGORIES.md to present the current category list with examples. Each category maps to relevant NIST controls.

Step 2: Collect Decision Metadata

Ask for the required fields:

"Let's document this decision. I need:

  1. Title — What is being decided? (Use format: 'Use X for Y')
  2. Status — proposed, accepted, deprecated, or superseded?
  3. Decision makers — Who is involved in this decision?
  4. Date — When was this decided? (Default: today)"

Step 3: Collect Federal Compliance Fields

These extend standard MADR with federal context:

Read the full file on GitHub · 212 lines

Files

What ships with it

2 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.

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. 10d ago First seen · 212 lines · 27 tokens per session scan A 9e7872ed09b2

Subscribe to this mod's changes

federal-decision-records is a skill published in the GitHub repository GSA-TTS/agentic-coding-playbook (25 stars, last pushed today), licensed CC0-1.0. It adds 27 tokens to every session and 1,758 once invoked, about $0.0001 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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