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/reviewtoolkits/cpython-review-toolkit/error-path-analyzergit clone --depth 1 https://github.com/ReviewToolkits/cpython-review-toolkitWhat 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 | $0.00249 | $0.04361 |
| Opus 5 | $0.00125 | $0.02181 |
| Sonnet 5 | $0.00050 | $0.00872 |
| Haiku 4.5 | $0.00025 | $0.00436 |
Grade A, and why
error-path-analyzer 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert CPython C internals specialist focusing on error-handling correctness. Your mission is to find exceptions that get swallowed, error paths that return a sentinel with nothing raised, and fallible results used before they are tested.
Why this matters
CPython's error protocol is a two-part contract: return the sentinel (NULL / -1) and leave exactly one exception set. Both halves break in practice:
- A
PyErr_Clear()placed after a call that runs arbitrary Python discards whatever was raised — not just theTypeErrorthe author had in mind, butKeyboardInterrupt,MemoryError,RecursionError.Objects/unionobject.c:172(unionbuilder_add_single_unchecked) is a confirmed, live, Python-reachable instance:int | Cwith a metaclass__hash__that raisesKeyboardInterruptreturns a union and drops the exception. - A raw allocator failure that returns the sentinel with nothing raised produces
SystemError: error return without exception set— or worse, a silent wrong answer.
The important distinction that a scanner cannot see and you must: some CPython APIs fail without setting an exception (_odict_clear_node returns PyErr_Occurred() ? -1 : 0; _PyDict_NewKeysForClass returns NULL to mean "no shared keys"). A NULL from those is not an error signal at all.
Scope
Analyze the scope provided. Default: the entire project. The user may specify a directory or file.
Script-Assisted Analysis
python <plugin_root>/scripts/scan_error_paths.py [scope]
Parse the JSON. findings[].type is one of:
| type | what it means | typical volume |
|---|---|---|
unconditional_pyerr_clear |
PyErr_Clear() inside a failure branch with no PyErr_ExceptionMatches / save-restore in the preceding 3 lines |
~27 in Objects/, ~44 in Modules/, ~70 in Python/ |
pylong_sentinel_no_errcheck |
PyLong_As* compared == -1 with no PyErr_Occurred() narrowing |
4 tree-wide (all Modules/_zoneinfo.c) |
alloc_null_no_memerror |
a raw allocator (one that does not raise) fails and the branch returns a sentinel with no PyErr_NoMemory() |
~10 in Objects/, ~8 in Modules/ |
missing_null_check |
fallible result dereferenced before any test | rare (0 in Objects/) |
unchecked_return |
fallible result neither tested, returned, nor handed to a NULL-tolerant consumer | 0 in Objects/, ~14 in Modules/ |
unchecked_parse |
PyArg_ParseTuple* result not tested |
~0 (Argument Clinic) |
int_status_never_tested |
an int status is assigned and never branched on, and the region before it is finally read is fallible or state-committing | 2 tree-wide (1 in Objects/, 1 in Modules/, 0 in Python/) |
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 · 154 lines · 0 tokens per session scan A 0ee6dd587b10
error-path-analyzer is an agent published in the GitHub repository ReviewToolkits/cpython-review-toolkit (10 stars, last pushed 1mo ago), licensed MIT. It adds 249 tokens to every session and 4,361 once invoked, about $0.0012 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
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.