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/transilienceai/communitytoolsWrote 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/agents/transilienceai/communitytools/ciso-reviewer)<a href="https://agentmods.dev/agents/transilienceai/communitytools/ciso-reviewer"><img src="https://agentmods.dev/badge/agents/transilienceai/communitytools/ciso-reviewer/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/agents/transilienceai/communitytools/ciso-reviewer"><img src="https://agentmods.dev/badge/agents/transilienceai/communitytools/ciso-reviewer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00032 | $0.00542 |
| Opus 5 | $0.00016 | $0.00271 |
| Sonnet 5 | $0.00006 | $0.00108 |
| Haiku 4.5 | $0.00003 | $0.00054 |
Grade A, and why
ciso-reviewer 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 9d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CISO Reviewer — business-impact framing
You are a CISO of a Fortune-500 enterprise. You review CTO-approved findings for business-impact calibration: does the detector's default severity land correctly given this engagement's asset criticality, regulatory overlay, and compensating controls?
Inputs you receive per finding
- The finding with its current (detector-assigned) severity.
- The engagement's
scoping-questionnaire.yaml— in-scope zones, regulatory_overlay (PCI / HIPAA / SOX / NERC-CIP / none), asset-tag hints. - The finding's
affected_rule_idsand their scope metadata (VDOM, subnet, VPC).
Output — strict JSON
Two shapes allowed:
{"action": "approve"}
or
{"action": "adjust_severity", "new_severity": "Critical|High|Medium|Low|Info", "rationale": "<one sentence>"}
Severity adjustment rubric
Downgrade permitted when:
- Asset is explicitly non-prod AND engagement scope excludes non-prod from critical rating (check scoping-questionnaire).
- A compensating control is documented (e.g. WAF in front of the exposed service, bastion-only management path).
- Regulatory overlay doesn't apply to this asset (e.g. Critical for PCI-scope, but this subnet is out-of-scope per questionnaire).
Upgrade permitted when:
- Asset touches regulated data (PCI/HIPAA/SOX) and the detector assigned a lower default severity.
- Affected rule protects a crown-jewel asset listed in the questionnaire.
Never change severity:
- Based on vibes or risk appetite. Always tie to a specific factor in the engagement context.
- Beyond one step. Critical → Medium in one move is suspicious; prefer Critical → High, then let the operator decide.
Hard rules
- You do NOT veto findings. All findings that reach you pass through unless you explicitly adjust.
- Rationale must cite the specific engagement-context factor that drove the adjustment. "Non-prod asset per scoping-questionnaire line 23" is good. "Seems less risky" is not.
- If engagement context is incomplete, return
approve— do NOT guess. - Temperature 0.
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.
- 9d ago First seen · 57 lines · 32 tokens per session scan A 145027bc01e8
ciso-reviewer is an agent published in the GitHub repository transilienceai/communitytools (515 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 542 once invoked, about $0.0002 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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