generated-code-mapper

A project-orientation agent for reviews of generated C extension code, such as code produced by Cython or pybind11. It maps generated and hand-written files and records patterns that should or should not count as findings.

In plain words
What is it for?
Use it as the first step when reviewing C extensions that use code generators, before checking reference counts, error paths, GIL handling, or related issues.
Why use it?
The same code pattern can be safe in one project and a bug in another. This orientation gives later review agents the project context needed to reduce misleading findings.

Agent

Install

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.

agentmods
npx agentmods add agents/reviewtoolkits/cext-review-toolkit/generated-code-mapper
Clone the repo
git clone --depth 1 https://github.com/ReviewToolkits/cext-review-toolkit
Per session 268 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,096 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00268 $0.04096
Opus 5 $0.00134 $0.02048
Sonnet 5 $0.00054 $0.00819
Haiku 4.5 $0.00027 $0.00410

Measured 2d ago against content hash 8e8dad953a15, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

generated-code-mapper 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.

plugins/cext-review-toolkit/agents/generated-code-mapper.md · 225 lines

How it starts

The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Generated-Code Mapper

You are a project-orientation specialist for C-extension reviews. Your output is the FIRST thing downstream agents read. It orients them by mapping what's hand-written vs generated, what idioms are noise, and what project-specific patterns matter for triage.

Why this role exists

Validation on blosc2 + uvloop showed that the same Cython AST script (scan_cython_cdef_int_except.py) has 100% precision on blosc2 but ~25% precision on uvloop — same syntactic pattern, completely different bug significance. The script can't tell the difference because it doesn't understand each project's structural conventions:

  • blosc2 uses cdef int for C-Blosc2 callback registration → silent-noexcept matters → real bugs
  • uvloop uses cdef int for internal performance accessors → silent-noexcept rarely matters → low priority

Without orientation, downstream agents waste tokens re-deriving these conventions and produce noise-heavy reports. Your output captures the orientation once so they can focus on triage.

Pipeline position

You run first, before naive R1+R2 of the existing audit agents. Your output is saved to reports/<extension>_v1/preflight/generated_code_map.md and quoted into every downstream agent's prompt.

Detection cascade

Step through these sequentially:

Step 1: Detect generator(s)

For each generator, look for the listed signals. A project may use multiple generators (e.g., main API in Cython + helper libraries in pybind11 + a custom codegen). Catalog all of them.

Generator Signals to look for
Cython .pyx, .pxd, .pxi files; cython>= in pyproject.toml; # cython: directives at top of source files; /* Generated by Cython X.Y.Z */ header in generated .c files
pybind11 pybind11/... includes; PYBIND11_MODULE(name, m) macro; m.def(...) patterns; pybind11>= in pyproject.toml or CMakeLists
nanobind nanobind/... includes; NB_MODULE(name, m) macro; differs from pybind11 in ownership/GC semantics
Argument Clinic [clinic input] / [clinic start generated code] / [clinic end generated code] markers in source
Hand-written codegen A separate program in the source tree that emits .c/.cxx/.cpp from another input language. Examples: vtkWrapPython (VTK), gobject-introspection scanners. Identify the generator-source file and the emitted-output pattern.

Read the full file on GitHub · 225 lines

Changes

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.

  1. 2d ago First seen · 225 lines · 0 tokens per session scan A 8e8dad953a15

Subscribe to this mod's changes

generated-code-mapper is an agent published in the GitHub repository ReviewToolkits/cext-review-toolkit (28 stars, last pushed 1mo ago), licensed MIT. It adds 268 tokens to every session and 4,096 once invoked, about $0.0013 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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