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/deep-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 | $0.00008 | $0.01101 |
| Opus 5 | $0.00004 | $0.00550 |
| Sonnet 5 | $0.00002 | $0.00220 |
| Haiku 4.5 | $0.00001 | $0.00110 |
Grade A, and why
deep-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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Comprehensive code review with full context analysis. Includes architecture review, test coverage, and impact analysis.
Phase 1: Gather Context (3 Parallel Haiku Agents)
Agent 1A - Change Summary:
1. Run `git status`, `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)
Agent 1B - Project Context:
1. Find CLAUDE.md (root + directories with changes)
2. Read dependency files:
- package.json / requirements.txt / go.mod / Cargo.toml
3. Identify project type and framework
Agent 1C - Related Files:
For each modified file, find:
- Files that import the modified file
- Files that the modified file imports
- Test files (foo.ts → foo.test.ts)
Phase 2: Load Agents (Progressive)
Only load agents relevant to detected context:
| Condition | Load Agent |
|---|---|
| Always | @agents/bugs.md |
| Always | @agents/security.md |
| Always (deep) | @agents/architecture.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.
Phase 3: Deep Analysis (Parallel Sonnet Agents)
Launch loaded agents in parallel. Each agent:
- Reads full file context + related files from Phase 1C
- Analyzes only the diff (not pre-existing code)
- Returns structured issues with diff-style fixes
Core Agents (always):
@agents/bugs.md- Logic errors, null access, race conditions@agents/security.md- OWASP Top 10, injection, XSS, secrets@agents/architecture.md- Patterns, coupling, dependencies
Conditional Agents:
@agents/compliance.md- If CLAUDE.md exists@agents/language-typescript.md- If TS/JS files@agents/language-python.md- If Python files
Additional Deep Analysis:
- Tests & Documentation Agent:
- Do test files exist for modified code?
- Do tests need updating for this change?
- Are new public APIs missing tests?
- Do README/docs need updates?
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 · 147 lines · 8 tokens per session scan A f90158604e39
deep-review is a command published in the GitHub repository turingmindai/turingmind-code-review (49 stars, last pushed 7mo ago), licensed MIT. It adds 8 tokens to every session and 1,101 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
git
Git operations with intelligent commit messages and workflow optimization.
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.
constitution
Create or update the project constitution from interactive or provided principle inputs.