MatrAIx is an infrastructure for evaluating AI systems and interactive products with simulated users represented by language-model agents, each based on a distinct persona. It helps researchers test products across surveys, chatbots, websites, and native apps while studying results for individual groups and whole populations. The catalogue entry contains skills for working with this simulation system.
Borrowing it
Nothing to install: this file belongs to MatrAIx-ai/MatrAIx-Persona-8B. 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/MatrAIx-ai/MatrAIx-Persona-8B/main/.github/skills/persona-extraction-quality-check/SKILL.mdgit clone --depth 1 https://github.com/MatrAIx-ai/MatrAIx-Persona-8BWrote 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/matraix-ai/matraix-persona-8b/persona-extraction-quality-check)<a href="https://agentmods.dev/skills/matraix-ai/matraix-persona-8b/persona-extraction-quality-check"><img src="https://agentmods.dev/badge/skills/matraix-ai/matraix-persona-8b/persona-extraction-quality-check/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/matraix-ai/matraix-persona-8b/persona-extraction-quality-check"><img src="https://agentmods.dev/badge/skills/matraix-ai/matraix-persona-8b/persona-extraction-quality-check.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 | $0.00108 | $0.02234 |
| Opus 5 | $0.00054 | $0.01117 |
| Sonnet 5 | $0.00022 | $0.00447 |
| Haiku 4.5 | $0.00011 | $0.00223 |
Grade A, and why
persona-extraction-quality-check 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 12d 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Persona Extraction Quality Check
Evaluate extraction quality one complete persona at a time. The source profile is ground truth. Never score an extraction without pairing it to its exact source record.
Canonical rubric
Before judging anything, read the complete canonical rubric at:
persona/human_extraction/docs/EXTRACTION_QUALITY_RUBRIC.md
Use its M1–M7 definitions and 1–5 anchors verbatim. Do not invent a replacement scale or silently reinterpret a metric. Read the judge protocol before dispatching reviewers.
Non-negotiable rules
- One judge task receives exactly one persona packet. Never ask one reviewer call to score several personas.
- Judge the full persona. Do not split one persona's fields across workers; M4–M7 require whole-record context.
- Inspect fields before record-level scoring. Check every emitted field for M1 value, M2 evidence, and M3 description, then score M4–M7.
- Parallelize across independent reviews, not within a persona. Multiple personas and/or independent model reviews may run concurrently.
- Keep reviewers independent. A reviewer must not see another review before producing its own result.
- Fail closed on identity mismatch. Never pair records by guessed position if
ResponseId,response_id,row_index, or another stable ID conflicts. - Do not treat confidence as correctness. Verify the value and evidence against the source even when confidence is 1.0.
- Resume safely. Skip an existing valid
(persona_id, actual_model)review unless the user explicitly requests overwrite. - Do not claim a model was used unless the runtime actually selected it. Record both requested and actual model names.
- Never expose one person's full source profile in aggregate reports. Keep source text in per-persona packets; aggregate only scores and concise issue summaries.
Inputs
Require these logical inputs:
- Source profiles: CSV, JSONL, SQLite, or a directory containing them. For Stack Overflow 2025, prefer the exact filtered CSV used by extraction, normally
results_2025_completeness_60.csv, not the broader raw CSV. - Extracted personas: one or more JSONL files, a ZIP containing JSONL shards, or a directory of shards.
- Output directory: packets, independent reviews, consensus, and summaries go here.
- Selection: explicit IDs, a deterministic sample, or all records.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 172 lines · 108 tokens per session scan A 876ef6b247de
persona-extraction-quality-check is a skill published in the GitHub repository MatrAIx-ai/MatrAIx-Persona-8B (1,881 stars, last pushed 3d ago), licensed MIT. It adds 108 tokens to every session and 2,234 once invoked, about $0.0005 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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