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-pre-deployment-check/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-pre-deployment-check)<a href="https://agentmods.dev/skills/gsa-tts/agentic-coding-playbook/federal-pre-deployment-check"><img src="https://agentmods.dev/badge/skills/gsa-tts/agentic-coding-playbook/federal-pre-deployment-check/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-pre-deployment-check"><img src="https://agentmods.dev/badge/skills/gsa-tts/agentic-coding-playbook/federal-pre-deployment-check.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 | $0.00023 | $0.01622 |
| Opus 5 | $0.00012 | $0.00811 |
| Sonnet 5 | $0.00005 | $0.00324 |
| Haiku 4.5 | $0.00002 | $0.00162 |
Grade A, and why
federal-pre-deployment-check 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Federal Pre-Deployment Check
This skill executes the 62-item pre-deployment security checklist from
checklists/pre-deployment.md, combining automated tool checks with
human-verified items to produce a completed checklist report.
When to Use
- Before deploying code that was generated or modified with AI agent assistance
- As part of a PR review process for AI-assisted changes
- When a user asks "is this code ready to deploy?"
- When performing a security review of the codebase
Check Classification
See references/CHECK_AUTOMATION.md for the full classification of every checklist item. Summary:
| Type | Count | How It Works |
|---|---|---|
| Automated | 18 | Agent runs a tool, checks exit code or output |
| Semi-automated | 24 | Agent reads files or config, reports findings |
| Manual | 18 | Agent asks the human to verify |
Execution Procedure
Step 1: Collect Deployment Information
Ask the user for:
- System name
- Release/version being deployed
- AI agent that was used
- Files that were agent-authored (or PR number)
Step 2: Run Automated Checks
Run make pre-deploy to execute all automatable checks:
make pre-deploy
The script checks for:
- Secrets in source code (gitleaks or grep-based fallback)
- Pre-commit hooks installed and configured
- .gitignore with required patterns
- Lock file present
- Dependency vulnerabilities (language-specific audit tool)
- Test suite passes
- SAST scan (if tool available)
Output is structured JSON with pass/fail for each automated check.
Step 3: Run Semi-Automated Checks
For each semi-automated check, read the relevant files and report findings:
Category 1 — Code Review and Provenance:
- Check for AI attribution (PR description, AGENTS.md, or git log for
Co-Authored-By) (item 1.2) - Check branch protection config or recent commit history (item 1.4)
Category 3 — Input Validation:
- Search for
eval(,innerHTML, string concatenation in SQL (items 3.2, 3.5) - Search for path handling without validation (item 3.4)
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.
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 · 173 lines · 23 tokens per session scan A dc990ab7c972
federal-pre-deployment-check is a skill published in the GitHub repository GSA-TTS/agentic-coding-playbook (25 stars, last pushed today), licensed CC0-1.0. It adds 23 tokens to every session and 1,622 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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