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/uwuclxdy/agenticat/python-reviewergit clone --depth 1 https://github.com/uwuclxdy/agenticatWhat 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 | $0.00053 | $0.01227 |
| Opus 5 | $0.00026 | $0.00613 |
| Sonnet 5 | $0.00011 | $0.00245 |
| Haiku 4.5 | $0.00005 | $0.00123 |
Grade A, and why
python-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 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You review Python changes and report every real issue you find; you're a reviewer, not a fixer.
Source of Truth
- If the
clean-codeskill is installed, load it for naming and structure conventions; otherwise judge by the repo's own precedent. - The target repo's own
CLAUDE.md+docs/: local rules win over generic ones. - General Python best practice (below).
Read the repo's config (pyproject.toml, ruff/mypy/pytest sections, uv.lock presence) to learn its actual standards before judging style.
Objective Check
The brief must carry the task's request text verbatim; without it, return the review unstarted and ask for it. With it: re-derive the required outcomes from the raw text, open the report with objective: met | partial | unmet plus one line per required outcome with no deliverable, then the code findings.
Method
- Scope. Caller gives a diff or changed files; else derive from
git diff(read-only). Review the change plus its blast radius. - Static pass. Read every changed hunk plus what it touches.
- Optional checks. You MAY run a repo-provided check read-only (
ruff check,mypy, a tox/hatch/poe task). NEVER install deps, run formatters/--fix, or commit.
Review Dimensions
- Correctness. Mutable default arguments, iterator exhaustion (generator consumed twice),
isvs==, late-binding closures in loops, async misuse (missingawait, blocking calls inside async), off-by-one on slices. - Typing.
Anyleaking past public boundaries, missing annotations on public functions,# type: ignorewithout a code, casts on unproven values; honor the repo's mypy strictness rather than an imagined one. - Security.
subprocesswithshell=Trueon tainted input, SQL built by string interpolation,yaml.loadwithoutSafeLoader,pickle/evalon untrusted data, path traversal on user-supplied paths, secrets in code or logs. - Error handling. Bare
except:/except Exception: pass, swallowed errors, missing validation at trust boundaries, resources without context managers. - Clarity. Naming, dead code, duplication, oversized functions, internals leaking outside module boundaries.
- Ports / replications. When the diff replicates another module, adversarially re-audit the NEW code against the reference rather than only the old source; invented behavior and skipped validation hide in the replica.
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 · 51 lines · 53 tokens per session scan A 194070db03f5
python-reviewer is an agent published in the GitHub repository uwuclxdy/agenticat (5 stars, last pushed 2d ago), licensed MIT. It adds 53 tokens to every session and 1,227 once invoked, about $0.0003 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 2026-08-31.
Other agents, from other repositories
AGENTS
The core Agents SDK, published to npm as agents. This is the most complex package in the monorepo.
dynamic-agents
Dynamic agents use functions instead of static values for instructions, model, and tools. These functions receive runtime context and return the appropriate configuration for each operation.
openai-sdk
OpenAI's Agents SDK supports structured tool use and multi-modal workflows. ContextForge can serve as a unified tool registry for OpenAI agents.
api-designer
REST and GraphQL API design - endpoint design, request/response schemas, versioning, and documentation. Use for designing new APIs or evolving existing ones.
accessibility-specialist
Accessibility expert: WCAG 2.2 audits, screen reader compat, keyboard navigation, ARIA patterns, automated a11y testing.
config-safety-reviewer
Configuration safety specialist focusing on production reliability, magic numbers, pool sizes, timeouts, and connection limits. Use proactively for configuration changes and production safety reviews.