Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/grcengineering/companion/scenariogit clone --depth 1 https://github.com/grcengineering/companionWhat 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 | $0.00013 | $0.00139 |
| Opus 5 | $0.00006 | $0.00069 |
| Sonnet 5 | $0.00003 | $0.00028 |
| Haiku 4.5 | $0.00001 | $0.00014 |
Grade A, and why
scenario 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 2d 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.
What it actually says
Practice Scenario Command
Use when the learner needs judgment practice.
Flow
- Generate a fictional but realistic GRC situation.
- Ask the learner what they would do first and why.
- Grade reasoning, assumptions, and tradeoffs.
- Show a stronger answer.
- Ask what principle transfers to their work.
Boundary
Never ask for sensitive live details. Keep the case fictional.
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.
- 2d ago First seen · 27 lines · 13 tokens per session scan A ad30b4f3951d
scenario is a command published in the GitHub repository grcengineering/companion (32 stars, last pushed 3mo ago), licensed MIT. It adds 13 tokens to every session and 139 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.
Other commands, from other repositories
Ingest Legacy SSP
Run the end-to-end legacy SSP import process: extract source material, create source traceability, bootstrap a Trestle workspace, map content, validate OSCAL, and report unmapped items.
Fetch OSCAL Baseline
Download the NIST SP 800-53 Rev 5 catalog and a FedRAMP Rev 5 baseline profile, then import both into a Compliance Trestle workspace for real-baseline SSP drafting.
KSI Coverage Report
Compare an OSCAL SSP to the FedRAMP 20x Key Security Indicators from the 2026 Consolidated Rules and report documentation coverage.
Validate OSCAL Package
Validate an OSCAL file or Trestle package with available validators and write a validation summary.
health
Check FedRAMP MCP server health status.
evidence-examples
Get evidence examples for FedRAMP compliance.