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/hybrid-agent-worker/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/hybrid-agent-worker)<a href="https://agentmods.dev/skills/graphistry/pygraphistry/hybrid-agent-worker"><img src="https://agentmods.dev/badge/skills/graphistry/pygraphistry/hybrid-agent-worker/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/hybrid-agent-worker"><img src="https://agentmods.dev/badge/skills/graphistry/pygraphistry/hybrid-agent-worker.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 | $0.00033 | $0.00572 |
| Opus 5.5 | $0.00013 | $0.00229 |
| Sonnet 5.5 | $0.00007 | $0.00114 |
| Haiku 4.5 | $0.00003 | $0.00057 |
Grade A, and why
hybrid-agent-worker 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 3d 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 — 30 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hybrid Agent Worker
Own implementation, your plan.md, verification, and evidence. Follow plan, review when applicable, and the shared protocol.
Keep progressing
- Recover current plans, authority, unread messages, and actual execution state. Keep the plan’s next-action pointer accurate.
- Incorporate planner guidance or explain an evidence-backed alternative. Update your plan and continue within authority; notification need not become a permission wait.
- Execute successive authorized steps, including routine debugging and tests, without new assignments or ACKs. Ask when a consequential decision is needed; continue unaffected work.
- Inspect real APIs, response shapes, entrypoints, CI discovery, and side effects before building adapters or harnesses. Establish target/version prerequisites early.
Verify and report
Test actual behavior with meaningful positive/failure cases; do not mock away the claimed boundary. Run required checks for changed scope, confirm test discovery, and fix underlying defects. Reuse unchanged accepted evidence with a reason; broaden testing only for a concrete risk or required gate.
During active work, send a brief heartbeat every 2–3 minutes by default: plan step, progress/handle, and blocker/change. Substantive updates replace heartbeats. Continue without waiting for ACK; never ACK an ACK.
After each long tool wait, read new planner messages before the next external side effect; do not issue another side-effecting command from inside an uninterruptible shell call.
While awaiting a live planner review, clearance, or CI gate, keep the goal active in a bounded poll loop: check the incoming mailbox and known gate every 2–3 minutes, with no single wait over 60 seconds. Read only deltas, ACK new messages, and continue authorized independent plan work. A timeout or quiet mailbox is not a stopping condition; do not repeat tests or send status essays on unchanged state. If the reviewer or execution is confirmed unavailable and no independent work remains, record the next actor/resume event and follow the platform's blocked-state rules. A file write cannot wake a stopped session.
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.
- 3d ago Changed · +4 lines e58f46e7b781
- 9d ago First seen · 26 lines · 33 tokens per session scan A 8d17e8e9bf12
hybrid-agent-worker is a skill published in the GitHub repository graphistry/pygraphistry (2,576 stars, last pushed yesterday), licensed BSD-3-Clause. It adds 33 tokens to every session and 572 once invoked, about $0.0001 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 2026-10-20.
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