Anomalib Benchmarking
open-edge-platform/anomalib
Runs the anomalib benchmarking pipeline to train/evaluate a grid of model + dataset (+ category) combinations and collect metrics into a results CSV.
Run clinical and biomedical named-entity recognition on medical text with OpenMed's analyzetext.
Install Extracting Clinical Entities
$ npx skills add maziyarpanahi/openmed --skill extracting-clinical-entities -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install maziyarpanahi/openmed extracting-clinical-entities --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/maziyarpanahi/openmed.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/extracting-clinical-entities .claude/skills/extracting-clinical-entities && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "extracting-clinical-entities" agent skill from https://github.com/maziyarpanahi/openmed/tree/master/skills/extracting-clinical-entities into .claude/skills/extracting-clinical-entities/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-clinical-entities", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/maziyarpanahi/openmed/tree/master/skills/extracting-clinical-entitiesType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add maziyarpanahi/openmed --skill extracting-clinical-entities -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install maziyarpanahi/openmed extracting-clinical-entities --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/maziyarpanahi/openmed.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/extracting-clinical-entities .agents/skills/extracting-clinical-entities && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "extracting-clinical-entities" agent skill from https://github.com/maziyarpanahi/openmed/tree/master/skills/extracting-clinical-entities into .agents/skills/extracting-clinical-entities/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-clinical-entities", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add maziyarpanahi/openmed --skill extracting-clinical-entities -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install maziyarpanahi/openmed extracting-clinical-entities --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/maziyarpanahi/openmed.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/extracting-clinical-entities .cursor/skills/extracting-clinical-entities && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "extracting-clinical-entities" agent skill from https://github.com/maziyarpanahi/openmed/tree/master/skills/extracting-clinical-entities into .cursor/skills/extracting-clinical-entities/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-clinical-entities", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/maziyarpanahi/openmed.git --path skills/extracting-clinical-entities--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add maziyarpanahi/openmed --skill extracting-clinical-entities -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install maziyarpanahi/openmed extracting-clinical-entities --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/maziyarpanahi/openmed.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/extracting-clinical-entities .gemini/skills/extracting-clinical-entities && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "extracting-clinical-entities" agent skill from https://github.com/maziyarpanahi/openmed/tree/master/skills/extracting-clinical-entities into .gemini/skills/extracting-clinical-entities/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-clinical-entities", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install maziyarpanahi/openmed extracting-clinical-entitiesInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add maziyarpanahi/openmed --skill extracting-clinical-entities -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/maziyarpanahi/openmed.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/extracting-clinical-entities .github/skills/extracting-clinical-entities && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "extracting-clinical-entities" agent skill from https://github.com/maziyarpanahi/openmed/tree/master/skills/extracting-clinical-entities into .github/skills/extracting-clinical-entities/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-clinical-entities", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add maziyarpanahi/openmed --skill extracting-clinical-entities -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install maziyarpanahi/openmed extracting-clinical-entities --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/maziyarpanahi/openmed.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/extracting-clinical-entities .opencode/skills/extracting-clinical-entities && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "extracting-clinical-entities" agent skill from https://github.com/maziyarpanahi/openmed/tree/master/skills/extracting-clinical-entities into .opencode/skills/extracting-clinical-entities/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-clinical-entities", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
extracting-clinical-entities- GitHub stars
- 5.5k
- Token cost
- ~1.9k tokens
- SKILL.md length
- 457 words
- Files
- 1
- Skills in repo
- 74
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Run clinical and biomedical named-entity recognition on medical text with OpenMed's analyzetext.
- The user wants to extract diseases
- SKILL.md covers When to use, Install, Quick start and Output formats, plus 6 more sections
- Calls pip
- Other biomedical entities from notes
What it does
Extracting Clinical Entities is an agent skill from maziyarpanahi/openmed. Run clinical and biomedical named-entity recognition on medical text with OpenMed's analyzetext. Use when the user wants to extract diseases, drugs, anatomy, genes, or other biomedical entities from notes; needs NER output as dict/json/html/csv; wants to filter by confidence, group entities, toggle sentence detection, or save spans to JSONL; or wants the openmed analyze CLI. Pairs with loading-openmed-models and choosing-openmed-models, and runs after deidentifying-clinical-text in a privacy-first pipeline.
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Natural language processing and CSV and tabular files. The repository describes itself as: Local-first healthcare AI: clinical NER and HIPAA PII de-identification on hardware you control. 2,200+ medical models, 35 model-backed PII languages, and Python, MLX, Android… The licence is Apache-2.0.
When your agent uses it
- The user wants to extract diseases
- Other biomedical entities from notes
- Needs NER output as dict/json/html/csv
- Wants to filter by confidence
Example prompts
- “/extracting-clinical-entities”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 34d7b8c. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
huggingface.cogithub.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Extracting Clinical Entities loads about 1.9k tokens when it runs. Until then it costs about 136 tokens; SKILL.md has 457 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from maziyarpanahi/openmed at commit 34d7b8c, republished under its Apache-2.0 licence (© maziyarpanahi). 457 words, ~1,926 tokens.
Download SKILL.mdSave it as
.claude/skills/extracting-clinical-entities/SKILL.md (or your agent's skills folder).- name
- extracting-clinical-entities
- description
- Run clinical and biomedical named-entity recognition on medical text with OpenMed's analyze_text. Use when the user wants to extract diseases, drugs, anatomy, genes, or other biomedical entities from notes; needs NER output as dict/json/html/csv; wants to filter by confidence, group entities, toggle sentence detection, or save spans to JSONL; or wants the openmed analyze CLI. Pairs with loading-openmed-models and choosing-openmed-models, and runs after deidentifying-clinical-text in a privacy-first pipeline.
- license
- Apache-2.0
- metadata.project
- OpenMed
- metadata.category
- openmed-core
- metadata.pairs
- adjacent
- metadata.version
- 1.0
Extracting Clinical Entities
openmed.analyze_text runs a token-classification model over medical text and
returns structured entities with character offsets and confidence scores. It runs
on-device after a one-time model download.
When to use
- Pull diseases, medications, anatomy, genes, proteins, etc. out of clinical text.
- You need exact character spans (start/end) plus confidence per entity.
- You want output as objects, JSON, an HTML highlight view, or CSV.
- You are building the "extract entities" stage of a clinical NLP pipeline.
To choose a model, see choosing-openmed-models. To load it once and reuse it,
see loading-openmed-models. In a PHI workflow, de-identify first (see
deidentifying-clinical-text), then run NER on the redacted text.
Install
pip install "openmed[hf]"Quick start
import openmed
note = (
"Patient prescribed 500 mg metformin for type 2 diabetes mellitus. "
"Reports intermittent chest pain; ruled out myocardial infarction."
)
result = openmed.analyze_text(
note,
model_name="disease_detection_superclinical", # registry key, HF id, or local path
output_format="dict", # dict | json | html | csv
confidence_threshold=0.5,
)
for ent in result.entities:
print(f"{ent.label:12} {ent.text!r:40} {ent.confidence:.2f} [{ent.start}:{ent.end}]")With output_format="dict" you get a PredictionResult. The fields you use most:
result.text # the original input text
result.entities # list of entity objects
result.model_name # which model produced these
ent.text # the surface string
ent.label # entity type, e.g. "DISEASE"
ent.confidence # model score in [0, 1] (NOTE: .confidence, not .score)
ent.start / ent.end # character offsets into result.textOutput formats
analyze_text(...) returns different types depending on output_format:
output_format | Return type | Use for |
|---|---|---|
"dict" (default) | PredictionResult object | Programmatic access via .entities. |
"json" | str (JSON) | Logging, APIs, writing to disk. |
"html" | str (HTML) | A highlighted preview of the note. |
"csv" | str (CSV) | Spreadsheet / quick review. |
import openmed
note = "Started atorvastatin 40 mg; history of myocardial infarction."
json_str = openmed.analyze_text(note, output_format="json")
html_str = openmed.analyze_text(note, output_format="html") # render in a browser
csv_str = openmed.analyze_text(note, output_format="csv")Key parameters
openmed.analyze_text(
text,
model_name="disease_detection_superclinical",
output_format="dict",
confidence_threshold=0.5, # drop entities below this score; None keeps all
aggregation_strategy="simple", # HF subword aggregation; None for raw tokens
group_entities=False, # merge adjacent same-label spans into one
include_confidence=True, # include scores in formatted output
sentence_detection=True, # pySBD sentence splitting (better long-doc spans)
sentence_language="en",
loader=None, # pass a reused ModelLoader (see loading skill)
)confidence_threshold— the most useful knob. Use the model'srecommended_confidence(fromget_model_info) as a starting point.group_entities=True— merges"type","2","diabetes"fragments into a single"type 2 diabetes"span. Turn on for cleaner output.sentence_detection=True(default) — splits long notes into sentences before inference for more accurate offsets and to respect model max length. Requires pySBD; if unavailable it silently falls back to whole-text inference.
Save results to JSONL
One line per note keeps offsets and labels for downstream grounding or eval:
import json
import openmed
notes = [
"Type 2 diabetes managed with metformin.",
"Acute myocardial infarction; started aspirin and atorvastatin.",
]
with open("entities.jsonl", "w", encoding="utf-8") as fh:
for i, note in enumerate(notes):
result = openmed.analyze_text(note, output_format="dict")
fh.write(json.dumps({
"doc_id": i,
"text": result.text,
"model": result.model_name,
"entities": [
{"label": e.label, "text": e.text,
"start": e.start, "end": e.end,
"confidence": round(e.confidence, 4)}
for e in result.entities
],
}) + "\n")Store offsets and labels, not extra copies of free text, in PHI contexts.
CLI
openmed analyze --text "Type 2 diabetes managed with metformin." \
--model disease_detection_superclinical \
--format json \
--threshold 0.5 \
--group
# Or analyze a file:
openmed analyze --input-file note.txt --model disease_detection_superclinical -o csvFlags: --text/-t, --input-file/-f, --model/-m, --format/-o
(dict|json|html|csv), --threshold/-c, --group, --no-confidence,
--sentence-detection/--no-sentence-detection.
Show full SKILL.md (193 more words)Show less
Hand-off to / from OpenMed
From
loading-openmed-models: pass your reusedloader=so a batch loads weights once.From
deidentifying-clinical-text: run NER onresult.deidentified_text, not raw PHI:python deid = openmed.deidentify(raw_note, method="mask", policy="hipaa_safe_harbor") ner = openmed.analyze_text(deid.deidentified_text, output_format="dict")To terminology grounding (out-of-process): map
ent.text/ent.labelto RxNorm / LOINC / SNOMED using the user's own licensed service — OpenMed does not bundle restricted terminologies.To batch processing: for large corpora use
openmed.process_batch(...)/BatchProcessor(see processing utilities) with a shared loader.
Edge cases & gotchas
- Attribute is
.confidence, not.score. Entity objects extendEntityPrediction(text,label,confidence,start,end). - Right model for the labels. A Disease model won't emit oncology staging or
gene labels — pick the category in
choosing-openmed-modelsand checkentity_types. - Offsets index
result.text. Slice the original string withstart:end; the surface form inent.textis whitespace-trimmed. - Long documents: keep
sentence_detection=Trueso chunks respect the model's max length (get_model_max_length); disabling it can truncate long notes. - NER assists, it does not diagnose. Treat output as decision support; surface a disclaimer for any clinical-facing use.
- No raw PHI in logs. Log labels, offsets, and hashes — never patient text.
Standards & references
- Token classification / NER (Hugging Face): https://huggingface.co/docs/transformers/tasks/token_classification
- pySBD sentence boundary detection: https://github.com/nipunsadvilkar/pySBD
- OpenMed model cards: https://huggingface.co/OpenMed
© maziyarpanahi, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/extracting-clinical-entities of maziyarpanahi/openmed.
Open the folder on GitHubat commit 34d7b8c
Compare with similar skills
Extracting Clinical Entities next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Extracting Clinical Entities this skillmaziyarpanahi/openmed | 5.5k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Anomalib Benchmarkingopen-edge-platform/anomalib | 6.2k | — | ~1k | Automated safety check: Pass | Apache-2.0 | |
| Codemie Analyticscodemie-ai/codemie-code | 294 | — | ~7.5k | Automated safety check: Pass | Apache-2.0 | |
| Perforatedai PlotPerforatedAI/PerforatedAI | 237 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Perforatedai AnalyzePerforatedAI/PerforatedAI | 237 | — | ~5.1k | Automated safety check: Pass | Apache-2.0 | |
| Yolo Trainingfcakyon/claude-codex-settings | 1.2k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 |
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Related
Categories
Questions about Extracting Clinical Entities
What does Extracting Clinical Entities do?
Run clinical and biomedical named-entity recognition on medical text with OpenMed's analyzetext. Extracting Clinical Entities is an agent skill from maziyarpanahi/openmed. Run clinical and biomedical named-entity recognition on medical text with OpenMed's analyzetext.
When should I use Extracting Clinical Entities?
Extracting Clinical Entities fits situations like: the user wants to extract diseases; other biomedical entities from notes; needs NER output as dict/json/html/csv; wants to filter by confidence.
How do I install Extracting Clinical Entities in Claude Code?
Run `npx skills add maziyarpanahi/openmed --skill extracting-clinical-entities -a claude-code`. Or copy the skill folder (skills/extracting-clinical-entities in maziyarpanahi/openmed) into .claude/skills/extracting-clinical-entities in your project. Claude Code loads it when a task matches its description.
How do I install Extracting Clinical Entities in Codex?
Run `npx skills add maziyarpanahi/openmed --skill extracting-clinical-entities -a codex`. Or copy the skill folder (skills/extracting-clinical-entities in maziyarpanahi/openmed) into .agents/skills/extracting-clinical-entities in your project. Codex loads it when a task matches its description.
Can I use Extracting Clinical Entities in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add maziyarpanahi/openmed --skill extracting-clinical-entities -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/extracting-clinical-entities, .gemini/skills/extracting-clinical-entities, .github/skills/extracting-clinical-entities and .opencode/skills/extracting-clinical-entities in your project.
What does Extracting Clinical Entities need to run?
Going by SKILL.md and its folder, Extracting Clinical Entities needs the command-line tools its instructions call (pip). Our summary lists: Python 3.
Does Extracting Clinical Entities access the network?
SKILL.md names 2 domains. As links in the text: huggingface.co and github.com. This is read from the text; nothing was executed.
Is Extracting Clinical Entities safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Extracting Clinical Entities use?
Extracting Clinical Entities is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Extracting Clinical Entities use?
About 1.9k tokens (SKILL.md is roughly 7.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Extracting Clinical Entities?
Skills that share tags, products or a category with Extracting Clinical Entities: Anomalib Benchmarking (open-edge-platform/anomalib, 6.2k stars), Codemie Analytics (codemie-ai/codemie-code, 294 stars), Perforatedai Plot (PerforatedAI/PerforatedAI, 237 stars) and Perforatedai Analyze (PerforatedAI/PerforatedAI, 237 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Extracting Clinical Entities?
maziyarpanahi (a GitHub user) maintains it in maziyarpanahi/openmed, which has 5,506 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on October 11, 2026.
Source: maziyarpanahi/openmed on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.