Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add matematicsolutions/awesome-matematic-skills-pl --skill doc-intel-llm-tier-plgit clone --depth 1 https://github.com/matematicsolutions/awesome-matematic-skills-plWrote 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/matematicsolutions/awesome-matematic-skills-pl/doc-intel-llm-tier-pl)<a href="https://agentmods.dev/skills/matematicsolutions/awesome-matematic-skills-pl/doc-intel-llm-tier-pl"><img src="https://agentmods.dev/badge/skills/matematicsolutions/awesome-matematic-skills-pl/doc-intel-llm-tier-pl/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/matematicsolutions/awesome-matematic-skills-pl/doc-intel-llm-tier-pl"><img src="https://agentmods.dev/badge/skills/matematicsolutions/awesome-matematic-skills-pl/doc-intel-llm-tier-pl.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 | $0.00291 | $0.02108 |
| Opus 5 | $0.00146 | $0.01054 |
| Sonnet 5 | $0.00058 | $0.00422 |
| Haiku 4.5 | $0.00029 | $0.00211 |
Grade A, and why
doc-intel-llm-tier-pl 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
doc-intel-llm-tier-pl - grounded extraction (warstwa LLM)
WRAP MateMatic na contextgem. Wyciaga z polskiego dokumentu prawnego inferowane koncepty
zakotwiczone do zrodla + uzasadnienie, i mapuje na nasz kontrakt dokumentowy.
Miejsce w ukladance (nie duplikuj)
| Warstwa | Skill | Technika | Co robi |
|---|---|---|---|
| Struktura + PII | doc-intel-contract-pl |
stdlib, zero-LLM | block_type, bbox, confidence, redaction_candidates |
| Koncepty prawne | ten skill | LLM + grounding | inferowane pola (kara, termin, ryzyko) + refs zdaniowe + justification |
| Weryfikacja cytatu | citation-grounding-pl |
string-match | czy cytat/ref istnieje w zrodle (anti-halucynacja) |
Przeplyw: stdlib doc-intel (struktura/PII) -> ten skill (koncepty + grounding) -> citation-grounding (weryfikacja refs). Nie ruszamy czystosci stdlib core - to osobna, opcjonalna warstwa.
Granica governance (WBUDOWANA W TOOL)
- Domyslny backend = LOKALNY (Ollama). Dla danych KLIENTA (tajemnica adwokacka + RODO) - TYLKO taki.
- Model chmurowy (OpenRouter/OpenAI/DeepSeek...) wymaga jawnej flagi
--allow-cloud; tool odmawia (exit 2) bez niej i ostrzega, ze cloud = wylacznie dane SYNTETYCZNE / nie-klienckie (transfer poza EOG). To nie dokumentacja - to zachowanie kodu. - Tool przygotowuje ekstrakcje; decyzja co z nia (pismo, redakcja) zostaje u czlowieka.
Zaleznosc (swiadomy wyjatek od stdlib)
pip install contextgem>=0.25.1
Grounded-extraction LLM nie da sie zrobic w samym stdlib - dlatego ten skill (w odroznieniu od wiekszosci skilli MateMatic) ma jedna zaleznosc. Rdzen deterministyczny zostaje w stdlib doc-intel.
Quick start
# LOKALNIE (RODO-safe, domyslnie) - wymaga dzialajacego Ollama z modelem 7-14B:
python scripts/ekstrakcja_llm.py --text umowa.txt --concepts koncepty.json \
--model ollama_chat/llama3.1:8b --api-base http://localhost:11434
# z .docx (przez DocxConverter contextgem):
python scripts/ekstrakcja_llm.py --docx pismo.docx --concepts koncepty.json --output text
# CHMURA - TYLKO dane syntetyczne / nie-klienckie (transfer poza EOG):
python scripts/ekstrakcja_llm.py --sample --model openrouter/deepseek/deepseek-chat --allow-cloud
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 · 139 lines · 291 tokens per session scan A 0c3037597fc0
doc-intel-llm-tier-pl is a skill published in the GitHub repository matematicsolutions/awesome-matematic-skills-pl (6 stars, last pushed 19d ago), licensed MIT. It adds 291 tokens to every session and 2,108 once invoked, about $0.0015 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 2026-08-31.
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