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/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/review)<a href="https://agentmods.dev/skills/graphistry/pygraphistry/review"><img src="https://agentmods.dev/badge/skills/graphistry/pygraphistry/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/review"><img src="https://agentmods.dev/badge/skills/graphistry/pygraphistry/review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Data Exfiltration · line 71 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
- medium Agent Snooping · line 44 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 129 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 266 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00070 | $0.05165 |
| Opus 5.5 | $0.00028 | $0.02066 |
| Sonnet 5.5 | $0.00014 | $0.01033 |
| Haiku 4.5 | $0.00007 | $0.00517 |
Grade A, and why
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 4d 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 — 411 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR Review (pygraphistry)
Invocation
/review [<PR-number-or-branch>] [mode=findings|pr-comments|both] [fixes=deferred|inline]
Defaults:
- Target: current branch PR (
gh pr view --json number,headRefName,baseRefName,url) mode=findingsfixes=deferred
mode:
findings: local artifacts only underplans/<task>/pr-comments: draft locally, post only after explicit user confirmation in the same sessionboth: run findings then comment flow
fixes:
deferred: read-only reviewinline: after each converged wave, apply confirmedBLOCKER/IMPORTANTfixes in separate commits
Runtime Assumptions
- Run from repo root.
ghis authenticated (gh auth status).- Local branch reflects PR head (
origin/<base>...HEADmatches intended review scope). plans/is local working memory and normally gitignored.
Plan-First Requirement
Always use the plan skill flow:
- If
plans/<task>/plan.mdexists, reuse it and append a review section. - Else reload
.agents/skills/plan/SKILL.mdand createplans/<task>/plan.md. - Record PR metadata,
mode,fixes, and timestamp. - Reload plan before every step; update plan immediately after every step.
<task>: prefer review-pr-<N> or <branch>-review.
Phase 0: Resolve Scope + Stack Context
- Resolve PR context (number/title/url/head/base).
- Record stack context:
gh pr view <PR> --json baseRefName,headRefName,title,body
gh pr list --base <headRefName>
- If stacked, explicitly mark out-of-scope upstream/downstream work in
plan.md. - Set diff range reference:
origin/<base>...HEAD.
Phase 1: Research Criteria Before Findings
Create plans/<task>/research/ with:
context.mdpolicies.mdcredentials.mdcanvas-<dimension>.md(only for dimensions that apply)
1a) Collect context + changed files
gh pr view <PR> --json number,title,headRefName,baseRefName,url,body
git fetch origin <baseRefName> <headRefName>
git diff --name-only origin/<base>...HEAD
git log --oneline origin/<base>..HEAD
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.
- 4d ago Changed · +11 lines 7b677945394e
- 15d ago Changed 9d4546a4d3ee
- 19d ago Changed · +17 lines · +1 tokens per session 4c58213d4bb2
- 24d ago Changed · +37 lines b93de33bf482
- 1mo ago Changed 700d652b205a
- 2mo ago First seen · 346 lines · 69 tokens per session scan A c24bd7054c82
review is a skill published in the GitHub repository graphistry/pygraphistry (2,576 stars, last pushed yesterday), licensed BSD-3-Clause. It adds 70 tokens to every session and 5,165 once invoked, about $0.0003 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 2026-08-30.
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