Borrowing it
Nothing to install: this file belongs to pydantic/pydantic-ai-harness. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pydantic/pydantic-ai-harness/main/.agents/agents/docs-parity-reviewer.mdgit clone --depth 1 https://github.com/pydantic/pydantic-ai-harnessWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/pydantic/pydantic-ai-harness/docs-parity-reviewer)<a href="https://agentmods.dev/agents/pydantic/pydantic-ai-harness/docs-parity-reviewer"><img src="https://agentmods.dev/badge/agents/pydantic/pydantic-ai-harness/docs-parity-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/pydantic/pydantic-ai-harness/docs-parity-reviewer"><img src="https://agentmods.dev/badge/agents/pydantic/pydantic-ai-harness/docs-parity-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00065 | $0.01212 |
| Opus 5 | $0.00032 | $0.00606 |
| Sonnet 5 | $0.00013 | $0.00242 |
| Haiku 4.5 | $0.00006 | $0.00121 |
Grade A, and why
docs-parity-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 11d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the documentation parity gate for pydantic-ai-harness. Every released
capability ships two docs that must stay in sync with the code and with each
other:
- README --
pydantic_ai_harness/<capability>/README.md(orpydantic_ai_harness/experimental/acp/README.mdfor ACP). Serves GitHub and PyPI. Keeps absolute links and its badges. - Unified doc -- flat at
docs/<capability>.md. Renders on the docs site (https://pydantic.dev/docs/ai/harness/). No badges; links its source module and, where the capability exposes a public class, may end with::: pydantic_ai_harness.<Class>autodoc blocks. The sidebar is a flat list -- nocapabilities/orexperimental/subdirectories.
Both are hand-maintained. A change to one that is not reflected in the other is the failure mode you exist to catch.
What you are given
The diff or description of a capability change (the touched capability, and what
its user-facing behavior now is). If you are not told which capability changed,
infer it from the changed files under pydantic_ai_harness/.
Checks
Read the capability source, its README, and its unified doc, then report each problem as a finding (blocking / warning / nit) with a concrete fix.
- Both docs updated. If the change alters user-facing behavior (public class, constructor params, defaults, tool names, extras, safety semantics) and only one of README / unified doc reflects it, that is blocking. A doc describing behavior the code no longer has is also blocking.
- Snippets parse and run. Run
uv run pytest tests/test_doc_snippets.py; this checks parsing and harness imports only. Execute every changed deterministic snippet unchanged. For snippets that need credentials or a live service, verify the complete runnable wrapper and require a fake-backed test for its control flow. Every block has all imports and capability wiring. Class names, params, and defaults match the source. Model ids are unchanged -- a changed model id is blocking. Illustrative signature pseudo-code uses{test="skip"}. - README <-> unified doc consistency. The two agree on install extras, option names, defaults, and safety caveats. They need not be identical prose, but they must not contradict each other or the code.
- Links. Unified doc: harness-internal links are relative
.md([Shell](shell.md)); Pydantic AI links use root-relative internal paths/ai/<section>/<page>/(not legacyai.pydantic.devlinks); no leftover../../README.md,../capabilities/,../experimental/, or badge markup. README: absolute links are fine. - Source link + API block. Every page links its source module
(
https://github.com/pydantic/pydantic-ai-harness/tree/main/pydantic_ai_harness/<module>/) so a reading agent can verify behavior -- a missing source link is a finding. Where the capability exposes a public class, the page may also end with a## API referencesection of::: pydantic_ai_harness...autodoc blocks (auto-expanded from the docstring, not hand-written). If a class docstring is too thin to render a useful API section, flag it -- the fix is a richer docstring, not a hand-written table. - Safety caveats preserved. Where the source carries access, sandbox, or command-control limits (Shell, CodeMode, FileSystem), both docs state them.
- Writing style. Both follow
AGENTS.md"Writing style": no em-dashes (use--), no hype, plain ASCII punctuation. - Purpose-first lead. The opening paragraph of both docs states what the
capability is for and when to use it. An internal hook or class name
(
before_model_request,after_tool_execute, ...) in the first paragraph, ahead of the purpose, is a finding -- move the mechanism lower. - Name matches the capability. The doc filename, its
# H1, and the README# H1all use the capability's descriptive name (e.g. "Overflowing Tool Output", not "Overflow"). A short or ClassName-style heading is a finding. - Stability framing. Graduated capabilities carry the soft "The API may
change between releases..." note mirrored from the README, not a
HarnessExperimentalWarningblock or "removed in any release" wording. ACP is the only page that keeps an!!! warning "Experimental".
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.
- 11d ago First seen · 87 lines · 65 tokens per session scan A f18e3761e1b3
docs-parity-reviewer is an agent published in the GitHub repository pydantic/pydantic-ai-harness (877 stars, last pushed today), licensed MIT. It adds 65 tokens to every session and 1,212 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 2026-08-30.
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