PyGraphistry is a Python library for loading, preparing, analyzing, and exploring large graphs through Graphistry’s GPU-accelerated visual graph analyzer. Data scientists and developers use it with dataframes, graph databases, and tools such as Pandas, Spark, RAPIDS, and Apache Arrow. Catalogue add-ons support workflows around using the library.
Borrowing it
Nothing to install: this file belongs to graphistry/pygraphistry. 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/graphistry/pygraphistry/master/.agents/skills/docs-review/SKILL.mdgit clone --depth 1 https://github.com/graphistry/pygraphistryWrote 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/skills/graphistry/pygraphistry/docs-review)<a href="https://agentmods.dev/skills/graphistry/pygraphistry/docs-review"><img src="https://agentmods.dev/badge/skills/graphistry/pygraphistry/docs-review/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/skills/graphistry/pygraphistry/docs-review"><img src="https://agentmods.dev/badge/skills/graphistry/pygraphistry/docs-review.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 | $0.00053 | $0.00851 |
| Opus 5.5 | $0.00021 | $0.00340 |
| Sonnet 5.5 | $0.00011 | $0.00170 |
| Haiku 4.5 | $0.00005 | $0.00085 |
Grade A, and why
docs-review 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 26d 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Docs editorial policy
Audience: a pandas, Polars, or Cypher user who has never seen this codebase. They read once, forwards, and stop at the first sentence that does not pay off.
Rules (ASD-STE100 in spirit)
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One idea per sentence, about 20 words, active voice, present tense.
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Verbs over nouns: "the index reads only those neighborhoods", not "the index enables neighborhood-scoped reads".
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Say the benefit to the reader, then the mechanism, then the evidence link. Never the mechanism alone.
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Define or replace jargon on first use. Prefer the plain phrase (right column):
avoid write shape (of a query) kind of query, query pattern, workload seeded / seed set / seeded lookup a query that starts from a few known nodes lane, route, fast path, hot path (omit; say what runs faster and when) parity, oracle the same result on every engine decline, typed decline raises an error before the query runs materialize (intermediate) build an intermediate result frontier the nodes reached at this hop point lookup a query for one node by id receipt, artifact, committed artifact (omit; link the provenance section once) attenuation, engagement (omit) release gate, CI, sweep, lever, step N (omit; internal process) -
No parentheticals inside sentences. Split them into sentences or delete them.
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No unnecessary contrast flourishes: "not X but Y", "no GPU, same results", "X — and Y". State Y.
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No mannerist titles ("The one-line speedup"). Title = what the reader gets to do ("Switch engines with one keyword").
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No footnote-style citations in body text ("[F1] Polars leads"). Put one fact inline with its link, or move the block to a provenance section.
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No competitor ammunition. Say where GFQL is good and link the full board with the losses shown. Do not editorialize a competitor's strengths.
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No internal process in user docs: release gates, CI lanes, what we refuse to publish, how numbers were audited. One provenance line with a link is enough.
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Every number comes from a vendored benchmark cell (
:bench-*:roles). Never type a measured number as a literal. -
Keep examples and tables; cut prose. A rewrite that shortens prose by a third with no loss of meaning is the normal outcome of a review.
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.
- 26d ago First seen · 67 lines · 53 tokens per session scan A 811bb9c3df17
docs-review is a skill published in the GitHub repository graphistry/pygraphistry (2,576 stars, last pushed yesterday), licensed BSD-3-Clause. It adds 53 tokens to every session and 851 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-03.
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