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.
git clone --depth 1 https://github.com/mlunato47/claude-grc-pluginWrote 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/commands/mlunato47/claude-grc-plugin/tabletop-scenario)<a href="https://agentmods.dev/commands/mlunato47/claude-grc-plugin/tabletop-scenario"><img src="https://agentmods.dev/badge/commands/mlunato47/claude-grc-plugin/tabletop-scenario.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00007 | $0.01398 |
| Opus 5 | $0.00003 | $0.00699 |
| Sonnet 5 | $0.00001 | $0.00280 |
| Haiku 4.5 | $0.00001 | $0.00140 |
Grade A, and why
tabletop-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 7d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- tabletop-scenario — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/grc:tabletop-scenario
Generate incident response or contingency plan tabletop exercise scenarios.
Usage
/grc:tabletop-scenario [type] [scenario?] [system-type?]
Arguments
- type:
ir(incident response) orcp(contingency plan) - scenario (optional): Scenario theme —
credentials,ransomware,breach,supply-chain,insider,ddos,outage,corruption,custom - system-type (optional): System context —
saas,paas,iaas,web-app,api,data-platform
Examples
/grc:tabletop-scenario ir credentials
/grc:tabletop-scenario ir ransomware saas
/grc:tabletop-scenario cp outage
/grc:tabletop-scenario ir supply-chain api
/grc:tabletop-scenario cp corruption data-platform
/grc:tabletop-scenario ir custom
Behavior
When invoked:
-
No redaction reminder needed — this is a pure reference command generating generic exercise scenarios. No user content is processed.
-
Read the appropriate reference files:
skills/grc-knowledge/audits/tabletop-scenarios.mdfor scenario templates, exercise structure, and report requirementsskills/grc-knowledge/audits/document-section-requirements.mdfor IRP/CP section requirements (to ensure the exercise tests relevant procedures)
-
Generate a complete tabletop exercise package:
a. Scenario Narrative A realistic scenario appropriate to the type and system context. Written in present tense, progressively revealing information through injects.
b. Injects 4-6 progressive injects that escalate the scenario and force new decisions. Each inject should:
- Introduce new information
- Require a decision or action
- Test a different aspect of the IRP/CP
c. Discussion Questions 8-12 questions that probe:
- Detection and initial response
- Escalation and communication
- Containment/recovery decisions
- External reporting obligations (CISA, FedRAMP PMO, agencies)
- Evidence preservation
- Lessons learned
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.
- 7d ago First seen · 180 lines · 7 tokens per session scan A 5caa31cdf05e
tabletop-scenario is a command published in the GitHub repository mlunato47/claude-grc-plugin (181 stars, last pushed 1mo ago), licensed MIT. It adds 7 tokens to every session and 1,398 once invoked, about $0.0000 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
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.