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-landscape-update/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-landscape-update)<a href="https://agentmods.dev/skills/gsa-tts/agentic-coding-playbook/federal-landscape-update"><img src="https://agentmods.dev/badge/skills/gsa-tts/agentic-coding-playbook/federal-landscape-update/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-landscape-update"><img src="https://agentmods.dev/badge/skills/gsa-tts/agentic-coding-playbook/federal-landscape-update.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Rogue Agent · line 16 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.01739 |
| Opus 5 | $0.00014 | $0.00870 |
| Sonnet 5 | $0.00006 | $0.00348 |
| Haiku 4.5 | $0.00003 | $0.00174 |
Grade A, and why
federal-landscape-update 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 — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Federal AI Landscape Update
This skill automates the discovery of new federal AI guidance and generates diff reports for human review. It implements a hybrid (human-curated automation) approach: 70% automated discovery + 30% human judgment.
When to Use
- Monthly scheduled review (first Monday of each month)
- When a new Executive Order mentioning "AI" is signed
- When NIST publishes a new AI-series document
- When OWASP releases a new LLM/Agentic version
- When a compliance deadline is <30 days away
- When the user asks "what's new in federal AI guidance?"
Prerequisites
- Python 3.12+ with
feedparserinstalled - Access to
data/federal-ai-landscape.yaml(structured registry) - Network access to RSS feeds (whitehouse.gov, nist.gov)
Execution Procedure
Step 1: Run the Landscape Monitor
Execute the monitoring script to fetch RSS feeds and compare against the registry:
PYTHONPATH=scripts python3 -m playbook_validator landscape-check
Or run the standalone script:
python3 scripts/landscape_monitor.py --output reports/landscape-diff-$(date +%Y-%m-%d).md
The script will:
- Fetch RSS feeds from configured sources
- Parse entries for AI-related publications
- Compare against
data/federal-ai-landscape.yaml - Identify new, updated, or missing entries
- Flag compliance deadlines within 30 days
- Generate a structured diff report
Step 2: Review the Diff Report
The generated report at reports/landscape-diff-YYYY-MM-DD.md includes:
| Section | Description |
|---|---|
| New Publications | Items in RSS not in registry |
| Updated Publications | Items with newer versions |
| Approaching Deadlines | Compliance dates within 30 days |
| Staleness Warnings | Registry entries not seen in RSS for >90 days |
Step 3: Human Review Checklist
For each flagged item, the human reviewer must determine:
- Relevance: Is this publication applicable to AI-assisted development?
- Category: Which category does it belong to?
- Urgency: Does this require immediate action?
- Narrative: What is the "Relevance" annotation for the landscape doc?
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 · 233 lines · 28 tokens per session scan A 6fe80a5e38de
federal-landscape-update is a skill published in the GitHub repository GSA-TTS/agentic-coding-playbook (25 stars, last pushed today), licensed CC0-1.0. It adds 28 tokens to every session and 1,739 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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