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-risk-assessment/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-risk-assessment)<a href="https://agentmods.dev/skills/gsa-tts/agentic-coding-playbook/federal-risk-assessment"><img src="https://agentmods.dev/badge/skills/gsa-tts/agentic-coding-playbook/federal-risk-assessment/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-risk-assessment"><img src="https://agentmods.dev/badge/skills/gsa-tts/agentic-coding-playbook/federal-risk-assessment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00028 | $0.01543 |
| Opus 5 | $0.00014 | $0.00772 |
| Sonnet 5 | $0.00006 | $0.00309 |
| Haiku 4.5 | $0.00003 | $0.00154 |
Grade A, and why
federal-risk-assessment 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 12d 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Federal Risk Assessment
This skill walks users through the risk assessment template from
docs/risk-assessment.md interactively, helping them complete each
section with context-appropriate guidance.
When to Use
- Preparing for Authority to Operate (ATO) review
- Evaluating risk before deploying an AI coding agent
- Updating a risk assessment after system or agent changes
- When an ISSO asks for an AI agent risk assessment
How It Works
Read docs/risk-assessment.md first to discover the current worksheet structure
(sections, capabilities, data types, threats, control areas). Then guide the user
through each section, explain what's needed, ask questions, and fill in the template.
Assessment Procedure
Section 1: System Identification
Ask the user for basic system information:
"Let's start with system identification. I need the following:
- System name
- System owner (name, title)
- ISSO (name, title)
- FIPS impact level (Low / Moderate / High)
- ATO status (Active / In process / Pre-ATO)
- Today's date as the assessment date
- Your name and title as assessor
- When should this be reviewed next? (Default: 1 year from now)"
Fill in the System Identification table.
Section 2: AI Agent Identification
Ask about the AI agent being assessed:
"Now let's identify the AI agent:
- Agent name and product (e.g., GitHub Copilot, Cursor, Codex)
- Agent version
- Agent vendor (e.g., Anthropic, GitHub/Microsoft)
- Deployment model — Local (runs on dev machine), Cloud SaaS, or Self-hosted?
- FedRAMP status — Authorized, In process, Not applicable, Unknown?
- Data residency — US only, International, Unknown?
- Training data opt-out — Confirmed, Not available, Unknown?"
Then walk through the capabilities checklist:
"Which of these capabilities will the agent use in this project? (Yes/No for each)"
Present the capabilities from docs/risk-assessment.md Section 2
(Agent Capabilities Inventory). Read the template to discover the current list.
For each capability, ask Yes/No.
What ships with it
1 file 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.
- 12d ago First seen · 167 lines · 28 tokens per session scan A be02caa9c21f
federal-risk-assessment is a skill published in the GitHub repository GSA-TTS/agentic-coding-playbook (25 stars, last pushed 2d ago), licensed CC0-1.0. It adds 28 tokens to every session and 1,543 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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