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/turingmindai/turingmind-code-review/language-pythongit 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 | $0.00002 | $0.00191 |
| Opus 5 | $0.00001 | $0.00096 |
| Sonnet 5 | $0.00000 | $0.00038 |
| Haiku 4.5 | $0.00000 | $0.00019 |
Grade A, and why
Python Issues 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.
What it actually says
Language-specific checks for Python.
Checks
Type Safety
- Missing type hints on public function signatures
Anytype where specific type is known- Incorrect Optional handling
Common Pitfalls
- Mutable default arguments (
def foo(x=[])) - Bare
except:clauses - Using
isfor value comparison - Missing
if __name__ == "__main__"
Resource Management
- Missing context managers for files
- Unclosed connections/cursors
- Missing finally blocks
Performance
- String concatenation in loops (use join)
- Repeated dictionary lookups
- Loading large files into memory
Output
For each issue, return:
file: File pathline: Line numberissue: Brief descriptionfix: Suggested fix with code
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 · 40 lines · 2 tokens per session scan A 193fe24204cf
Python Issues is an agent published in the GitHub repository turingmindai/turingmind-code-review (49 stars, last pushed 7mo ago), licensed MIT. It adds 2 tokens to every session and 191 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.
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