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.
curl -O https://raw.githubusercontent.com/GSA-TTS/agentic-coding-playbook/main/.agents/skills/federal-agents-config/SKILL.mdgit clone --depth 1 https://github.com/GSA-TTS/agentic-coding-playbookWrote 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/gsa-tts/agentic-coding-playbook/federal-agents-config)<a href="https://agentmods.dev/skills/gsa-tts/agentic-coding-playbook/federal-agents-config"><img src="https://agentmods.dev/badge/skills/gsa-tts/agentic-coding-playbook/federal-agents-config/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/gsa-tts/agentic-coding-playbook/federal-agents-config"><img src="https://agentmods.dev/badge/skills/gsa-tts/agentic-coding-playbook/federal-agents-config.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 20 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- medium Excessive Agency · line 174 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00020 | $0.02239 |
| Opus 5 | $0.00010 | $0.01120 |
| Sonnet 5 | $0.00004 | $0.00448 |
| Haiku 4.5 | $0.00002 | $0.00224 |
Grade A, and why
federal-agents-config 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.
How it starts
The opening of the file, as written. The whole thing — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Federal AGENTS.md Configuration
This skill generates a project-specific AGENTS.md by walking the user through compliance-relevant questions and producing a customized behavioral contract.
The generated file is a thin, project-specific layer: it references the
universal Federal AI Agent Behavioral Best Practices (the universal AGENTS.md)
as a prerequisite and does NOT restate the universal rules. This keeps a single
source of truth for the universal contract and avoids copy-paste drift. The
generated file instructs the agent to STOP if it cannot confirm the universal
contract is available, and to require the user's affirmative permission before
proceeding without it.
When to Use
- User needs to create an AGENTS.md for a new federal project
- User wants to update their existing AGENTS.md for compliance
- User asks "how do I configure my AI agent for federal requirements"
- User mentions FIPS, ATO, or federal compliance in the context of agent setup
How It Works
- Ask the user compliance-relevant questions (elicitation sequence below)
- Accumulate answers into a JSON configuration object
- Run
scripts/generate-agents-md.pywith the config to produce AGENTS.md - Present the output for human review
- Iterate if the user wants changes
Configuration Object
Track this JSON structure as you collect answers. Fields marked required must
be filled before generation. Fields marked optional have sensible defaults.
{
"system_name": null,
"agency_name": null,
"system_description": null,
"impact_level": "moderate",
"language": null,
"framework": null,
"data_classification": "internal",
"ato_status": "pre-ato",
"agent_names": [],
"prohibited_actions": [],
"permitted_actions": [],
"approval_required_actions": [],
"sensitive_data_types": [],
"approved_storage": [],
"secrets_backend": null,
"approved_registries": [],
"license_restrictions": [],
"network_allowlist": [],
"test_command": null,
"test_coverage_target": "80",
"ci_checks": ["lint", "test", "sast", "sca", "secrets-scan"],
"branch_protection": "1 review, no force push",
"project_lead": null,
"security_contact": null,
"isso_contact": null,
"reviewed_by": null
}
What ships with it
4 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.
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.
- 10d ago First seen · 240 lines · 20 tokens per session scan A 651a006e4085
federal-agents-config is a skill published in the GitHub repository GSA-TTS/agentic-coding-playbook (23 stars, last pushed 2d ago), licensed CC0-1.0. It adds 20 tokens to every session and 2,239 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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