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/turingmindai/turingmind-code-reviewWrote 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/turingmindai/turingmind-code-review/review)<a href="https://agentmods.dev/commands/turingmindai/turingmind-code-review/review"><img src="https://agentmods.dev/badge/commands/turingmindai/turingmind-code-review/review/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/commands/turingmindai/turingmind-code-review/review"><img src="https://agentmods.dev/badge/commands/turingmindai/turingmind-code-review/review.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 | $0.00009 | $0.00706 |
| Opus 5.5 | $0.00004 | $0.00282 |
| Sonnet 5.5 | $0.00002 | $0.00141 |
| Haiku 4.5 | $0.00001 | $0.00071 |
Grade A, and why
review 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 1mo 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quick code review for uncommitted changes. Fast, focused on critical issues.
Step 1: Gather Context (Haiku Agent)
Detect what needs to be reviewed:
1. Run `git status` and `git diff` / `git diff --staged`
2. If no changes → inform user and stop
3. Extract:
- Files changed (list)
- Languages detected (from extensions)
- Line counts (additions/deletions)
- Has CLAUDE.md? (root or in changed directories)
Step 2: Load Agents (Progressive)
Only load agents relevant to detected context:
| Condition | Load Agent |
|---|---|
| Always | @agents/bugs.md |
| Always | @agents/security.md |
| CLAUDE.md exists | @agents/compliance.md |
.ts/.tsx/.js/.jsx files |
@agents/language-typescript.md |
.py files |
@agents/language-python.md |
See @agents/index.md for full routing logic.
Step 3: Run Review (Parallel Sonnet Agents)
Launch loaded agents in parallel. Each agent:
- Reads full file context for changed files
- Analyzes only the diff (not pre-existing code)
- Returns structured issues with diff-style fixes
Output format per agent (see @agents/bugs.md for example):
### 🐛 {{issue_title}}
**Location:** `{{file}}:{{line}}`
**Confidence:** {{score}}/100
**Problem:** {{reason}}
**Suggested Fix:**
```diff
- {{old_code}}
+ {{new_code}}
## Step 4: Score & Filter (Haiku Agents)
For each issue, score confidence 0-100:
| Factor | Points |
|--------|--------|
| In the diff (new code) | +20 |
| Would cause failure | +30 |
| In CLAUDE.md rules | +20 |
| Senior engineer would flag | +20 |
| Has ignore comment | -50 |
Apply filters from `@templates/false-positive-rules.md`:
- Filter issues with score < 80
- Track filtered count by reason
## Step 5: Present Results
Format output using `@templates/output-format.md`:
Code Review
Summary: Reviewed X files, Y lines changed
| Found | Reported | Filtered |
|---|---|---|
| total | ≥80 score | <80 score |
Critical (95-100) 🔴
[Issues with diff-style fixes]
Warning (80-94) 🟠
[Issues with diff-style fixes]
Filtered Issues 🔇
[Count by reason, expandable details]
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.
- 1mo ago First seen · 104 lines · 9 tokens per session scan A de6695c2ed26
review is a command published in the GitHub repository turingmindai/turingmind-code-review (50 stars, last pushed 9mo ago), licensed MIT. It adds 9 tokens to every session and 706 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
speckit.spex.submit
Push and create PR for team review, with optional watch mode for CI monitoring.
git
The pre-finish status: branch, hygiene findings, message checks, workflow lint, template state.
merge-conflict-analysis
You are analyzing merge conflicts for PR #${{ pr-number }}.
release-tag
Create and push a version tag after a release PR has been merged.
factory
Linha de montagem multi-agente (Software Factory) — spec-writer → builder em worktree isolado → 4 revisores paralelos (Security, Code, UI, UX) → approver com gate de risco e dashboard visual.
repo-audit
Audit a codebase (local or remote GitHub/GitLab) against architecture principles and requirements, surfacing drift, risk, and missing decisions.