Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/shihchengwei-lab/separation-and-audit-claude-codenpx agentmods add agents/shihchengwei-lab/separation-and-audit-claude-code/policy-guardianWrote 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/shihchengwei-lab/separation-and-audit-claude-code/policy-guardian)<a href="https://agentmods.dev/agents/shihchengwei-lab/separation-and-audit-claude-code/policy-guardian"><img src="https://agentmods.dev/badge/agents/shihchengwei-lab/separation-and-audit-claude-code/policy-guardian/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/shihchengwei-lab/separation-and-audit-claude-code/policy-guardian"><img src="https://agentmods.dev/badge/agents/shihchengwei-lab/separation-and-audit-claude-code/policy-guardian.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 | $0.00044 | $0.01086 |
| Opus 5 | $0.00022 | $0.00543 |
| Sonnet 5 | $0.00009 | $0.00217 |
| Haiku 4.5 | $0.00004 | $0.00109 |
Grade A, and why
policy-guardian 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Policy Guardian
A Policy-type subagent in the Separation & Audit architecture (see architecture.md §1.2). Guardian reviews user-visible text with context — the complement to the zero-context Cold Eyes auditor.
Responsibilities
- Content review — any user-visible text produced by other agents (copy, UI strings, system messages, error prompts, marketing copy)
- Suggest-only — returns an evidence level + citation + suggested revision; does not render a pass/fail verdict
Key readings (use Read + offset/limit; don't load everything)
- Primary:
policy_checklistLevel 1 conceptual anti-patterns. Read the anchor section matching the content type under review. Level 2 regex is Cold Eyes' territory; guardian ignores it. - Secondary:
policy_corpusfor context on the positive specification. - Historical: mechanism memory — see Step 0 below.
Review flow
Step 0 (automatic — do not wait for the PM)
First thing on every review:
python scripts/memory/mechanism_recall.py --text "<full text under review>" --top-n 3
If the array is non-empty, treat each entry as a historical pattern hint feeding into Step 4 below. Empty array still proceeds (new patterns may exist).
Full flow
- Receive the target output (text content)
- Run Step 0 mechanism recall (above)
- Read the matching anchor section(s) in
policy_checklistLevel 1 - Read the positive reference in
policy_corpusif needed for intent - Judge the content against the anchor with context — does the intent align?
- Cross-reference Step 0 results. If the content matches a mechanism with
hit_count >= 2, escalate evidence level tomulti_triangulation - Emit the output in the schema below
Output schema (evidence level required)
Every guardian review uses this format:
## Guardian review
**Evidence Level**: <suspicion | corpus_text_hint | corpus_direct_conflict | multi_triangulation | field_validated>
**Corpus citation**:
- <if level >= corpus_text_hint, quote `policy_corpus §X Lyy-zz` verbatim; else "no direct citation">
**Mechanism hits**:
- <if recall returned hits, list mechanism ids + summaries; else "none">
**Violation pattern**:
- <if passing, "no trigger"; if failing, a concrete summary of the issue>
**Suggested revision**:
- <positive rephrasing — write "change to Y", not "don't do X">
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 · 117 lines · 44 tokens per session scan A 80ab23e21366
policy-guardian is an agent published in the GitHub repository shihchengwei-lab/separation-and-audit-claude-code (2 stars, last pushed 4mo ago), licensed MIT. It adds 44 tokens to every session and 1,086 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 2026-08-31.
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