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 commands/turingmindai/turingmind-code-review/reviewgit clone --depth 1 https://github.com/turingmindai/turingmind-code-reviewWhat 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 | $0.00009 | $0.00706 |
| Opus 5 | $0.00005 | $0.00353 |
| Sonnet 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 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 — 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.
- 2d 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 (49 stars, last pushed 7mo ago), licensed MIT. It adds 9 tokens to every session and 706 once invoked, about $0.0000 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.