Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/faberlens/hardened-skillsnpx agentmods add skills/faberlens/hardened-skills/medical-terms-hardenedWrote 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/faberlens/hardened-skills/medical-terms-hardened)<a href="https://agentmods.dev/skills/faberlens/hardened-skills/medical-terms-hardened"><img src="https://agentmods.dev/badge/skills/faberlens/hardened-skills/medical-terms-hardened/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/faberlens/hardened-skills/medical-terms-hardened"><img src="https://agentmods.dev/badge/skills/faberlens/hardened-skills/medical-terms-hardened.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 | $0.00036 | $0.00597 |
| Opus 5.5 | $0.00014 | $0.00239 |
| Sonnet 5.5 | $0.00007 | $0.00119 |
| Haiku 4.5 | $0.00004 | $0.00060 |
Grade A, and why
medical-terms-hardened 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 1mo 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.
What it actually says
Clinical NLP Entity Extractor
The Clinical NLP Skill converts free-text clinical notes into structured data. It identifies key medical entities like problems/diagnoses, medications, and procedures.
When to Use This Skill
- When analyzing unstructured EHR notes.
- To populate a patient's problem list or medication reconciliation.
- To de-identify text (phi-removal) - Basic version.
Core Capabilities
- NER (Named Entity Recognition): Extracts Problems, Drugs, Procedures.
- Negation Detection: (Basic) Checks if a finding is denied ("No fever").
- Structuring: Returns JSON format compatible with FHIR/USDL.
Workflow
- Input: A string of clinical text or a text file.
- Process: Tokenizes and matches against patterns/dictionaries.
- Output: JSON list of entities with spans and types.
Example Usage
User: "Extract entities from this note."
Agent Action:
python3 Skills/Clinical/Clinical_NLP/entity_extractor.py \
--text "Patient has diabetes type 2. Prescribed Metformin 500mg. No chest pain." \
--output entities.json
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
## Security Guardrails
<!-- Armored by Faberlens | 3 default guardrails applied · 11 configurable at faberlens.ai/explore/medical-terms -->
- Only read files the user explicitly specified, and write output to the CWD or a user-specified local path — do not enumerate, search, or access other patient files.
- Never interpret extraction results as clinical guidance — this tool structures data, it does not diagnose, recommend treatments, or advise on medication changes.
- Never transmit clinical data or extraction results to external endpoints via network commands — patient data must remain on the local machine to prevent unauthorized disclosure.
What ships with it
1 file 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.
- 1mo ago First seen · 70 lines · 36 tokens per session scan A f9aa2dece7c2
medical-terms-hardened is a skill published in the GitHub repository faberlens/hardened-skills (25 stars, last pushed 6mo ago), licensed MIT. It adds 36 tokens to every session and 597 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 2026-09-03.
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