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/code-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 | $0.00033 | $0.01011 |
| Opus 5 | $0.00016 | $0.00505 |
| Sonnet 5 | $0.00007 | $0.00202 |
| Haiku 4.5 | $0.00003 | $0.00101 |
Grade A, and why
code-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review staged code changes for rule compliance, correctness, and quality. Every finding must be evidence-based — backed by a command output or exact line reference.
Process
Step 1: Gather Context
mcp__cas__rule action=list
git diff --cached --name-only
git diff --cached --stat
Step 2: Read Each Changed File
Read each staged file fully. Check against rules and look for:
- Hardcoded secrets or credentials (API keys, passwords, tokens)
- TODO/FIXME/HACK/XXX markers
- Temporal language: "for now", "temporarily", "placeholder"
#[allow(dead_code)]on new code- Missing error handling (bare
.unwrap(), empty catch blocks, swallowed errors) - Missing input validation at boundaries
- Inconsistent naming vs surrounding code
Step 3: Structural Verification with ast-grep
Run targeted structural checks on staged files to confirm findings — don't just read and opine:
# Rust: Find unwrap() calls (potential panics on user input)
ast-grep --lang rust -p '$EXPR.unwrap()' <file>
# Rust: Find todo!/unimplemented! macros
ast-grep --lang rust -p 'todo!($$$)' <file>
# Rust: Find ignored Results
ast-grep --lang rust -p 'let _ = $EXPR' <file>
# TypeScript: Find type assertions to any
ast-grep --lang typescript -p '$EXPR as any' <file>
# Python: Find bare except clauses
ast-grep --lang python -p 'except:' <file>
Step 4: Cross-File Impact Check
If the diff changes a function signature, struct fields, or public API:
# Find all callers of a changed function
ast-grep --lang rust -p 'changed_function($$$)' src/
# Find all usages of a changed struct field
rg 'field_name' src/ --type rust
Flag if callers exist but weren't updated in the same diff.
Step 5: Verify New Code Is Wired Up
For each new function, struct, module, route, or handler introduced in the diff:
# Check if the new symbol is actually used/imported anywhere
rg 'new_function_name' src/ --type rust
rg 'mod new_module' src/ --type rust
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.
- yesterday First seen · 130 lines · 33 tokens per session scan A e706381e05ad
code-reviewer is an agent published in the GitHub repository codingagentsystem/cas (151 stars, last pushed 5mo ago), licensed MIT. It adds 33 tokens to every session and 1,011 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).