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 agents/codingagentsystem/cas/rule-reviewergit clone --depth 1 https://github.com/codingagentsystem/casWhat 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 | $0.00034 | $0.00634 |
| Opus 5 | $0.00017 | $0.00317 |
| Sonnet 5 | $0.00007 | $0.00127 |
| Haiku 4.5 | $0.00003 | $0.00063 |
Grade A, and why
rule-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 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.
How it starts
The opening of the file, as written. The whole thing — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review draft rules: promote, merge, or archive. Keep the rule set lean and high-signal.
Process
- List all rules:
mcp__cas__rule action=list_all— focus on Draft and Stale status. - For each draft rule, assess quality:
- Is it specific and actionable? ("Set busy_timeout on SQLite connections" = good, "Be careful with databases" = bad)
- Is it testable? Could you verify compliance by reading code?
- Does it apply broadly or is it a one-off fix disguised as a rule?
- Check for overlap:
mcp__cas__rule action=check_similar content="<rule content>"— find near-duplicates before deciding. - Decide:
- Promote if clear, specific, actionable, marked helpful, no conflicts with proven rules
- Merge if two rules say the same thing or overlap significantly — keep the more specific one, incorporate unique details from the other
- Rewrite if the rule has a good idea but bad phrasing — update content to be specific and actionable before promoting
- Archive if too vague, unused 30+ days, conflicts with proven rules, or project is done
- Check for conflicts — contradictory rules ("Always X" vs "Never X"), overlapping scope with different guidance.
- Execute:
- Promote:
mcp__cas__rule action=helpful id=<id> - Merge: update the better rule
mcp__cas__rule action=update id=<keep> content="<merged>", then deletemcp__cas__rule action=delete id=<dup> - Rewrite:
mcp__cas__rule action=update id=<id> content="<improved>" - Archive:
mcp__cas__rule action=delete id=<id>
- Promote:
Quality Bar for Promotion
A rule deserves proven status when it:
- States a clear constraint or pattern (not just advice)
- Would catch a real issue if checked during code review
- Doesn't duplicate an existing proven rule
- Has been marked helpful at least once, OR describes a pattern that caused a real rejection
Guidelines
- Be conservative with promotion — rules should earn proven status
- One clear rule > two similar ones
- Rewrite vague rules before promoting — don't promote bad phrasing just because the idea is good
- Archive aggressively — unused rules add noise, and they cost context tokens
- Flag conflicts for human review, don't auto-resolve
- Check
helpful_count— helpful rules deserve promotion - Rules from verification rejections (
from_verificationtag) are high-signal — they caught real issues
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 · 47 lines · 34 tokens per session scan A dc57b68f2065
rule-reviewer is an agent published in the GitHub repository codingagentsystem/cas (151 stars, last pushed 5mo ago), licensed MIT. It adds 34 tokens to every session and 634 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.
Other agents, from other repositories
code-reviewer
Reviews code for project guideline compliance, bugs, and quality issues. Use after writing code, before commits, or before PRs. Specify files to review or defaults to unstaged git changes. High-confidence issues only (80+) to minimize noise.
codebase-analyst
Use proactively to understand HOW code works. Analyzes implementation details, traces data flow, and documents technical workings with precise file:line references. The more specific your request, the better the analysis.
code-reviewer
Code reviewer. Delegate only when the user explicitly starts an Octopus workflow.
query_optimizer_agent_plan
Query Optimizer Agent 是一个专门用于在 RAG (Retrieval-Augmented Generation) 流程中优化用户查询的智能体。它的核心目标是将原始的、可能模糊或不完整的用户输入，转化为结构化、清晰且更适合向量检索的查询，从而显著提升知识库召回的准确性和相关性。.
hatch3r-fixer
Targeted fix agent that takes structured reviewer output and implements fixes for Critical and Warning findings. Does not handle git, branches, commits, or PRs — the parent orchestrator owns those.
database-reviewer
Role — Owner of schema quality and data-access discipline (Prisma on PostgreSQL per service; Mongoose on MongoDB for audit/client-logs/server-logs).