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/test-amplification/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/test-amplification)<a href="https://agentmods.dev/skills/graphistry/pygraphistry/test-amplification"><img src="https://agentmods.dev/badge/skills/graphistry/pygraphistry/test-amplification/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/test-amplification"><img src="https://agentmods.dev/badge/skills/graphistry/pygraphistry/test-amplification.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00056 | $0.02852 |
| Opus 5.5 | $0.00022 | $0.01141 |
| Sonnet 5.5 | $0.00011 | $0.00570 |
| Haiku 4.5 | $0.00006 | $0.00285 |
Grade A, and why
test-amplification 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 2mo 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 — 433 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Amplification Skill
When to Use
- A pygraphistry feature area is stable enough to use but still yields recurring bugs.
- You need to grow tests from evidence, not random additions.
- You want reusable patterns that apply beyond one subsystem (for example, beyond GFQL).
Goal
Turn historical and newly found bugs into:
- A reusable bug taxonomy.
- Prioritized concrete test tasks.
- Implemented tests (plus minimal fixes when needed).
- Clear validation evidence and checkpoint commits.
Required Inputs
- Current branch context.
- Target feature area(s).
- Existing test locations for those areas.
- Safety constraints (for example: pure vectorized path, pandas+cudf compatibility, remote protocol constraints).
Multi-Round Artifact Rules (Required)
This workflow is usually iterative. Keep artifacts cleanly separated by round, plus cumulative rollups.
Round IDs
- Use zero-padded round IDs:
round-001,round-002, ... - Keep a stable task root:
plans/<task>/
Round-Scoped Artifacts
Per round, write under:
plans/<task>/rounds/round-00N/
Recommended files:
user_testing_playbook.mdfeature-risk-analysis.mddiscovery-matrix.mdbug-retro-5whys.mdtaxonomy-and-task-plan.mdexecution-report.mdmetrics.md
Cumulative Artifacts
Maintain cumulative rollups under:
plans/<task>/cumulative/
Recommended files:
feature-risk-analysis-cumulative.mdbug-retro-5whys-cumulative.mdtaxonomy-cumulative.mdround-metrics-ledger.mdcontinue-or-halt-log.md
Naming Conventions
- Prefer deterministic names inside round folders (no random suffixes).
- If date is needed, use ISO format:
YYYY-MM-DD. - Every round must update:
- one round-scoped
metrics.md - cumulative
round-metrics-ledger.md - cumulative
continue-or-halt-log.md
- one round-scoped
Parallel Subagent Protocol (Recommended When Available)
If parallel subagents are available (for example, Claude workers), use them with fixed artifact locations so work is crash-resumable.
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.
- 2mo ago First seen · 433 lines · 56 tokens per session scan A acde435bf5b9
test-amplification is a skill published in the GitHub repository graphistry/pygraphistry (2,576 stars, last pushed yesterday), licensed BSD-3-Clause. It adds 56 tokens to every session and 2,852 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 2026-08-30.
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