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Agent skill

Algo NLP Ner

by asgard-ai-platform in asgard-ai-platform/skills

Implement Named Entity Recognition to identify and classify entities in text.

MITAuto-check passedAI & LLM Engineering

Install Algo NLP Ner

skills CLI
$ npx skills add asgard-ai-platform/skills --skill algo-nlp-ner -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install asgard-ai-platform/skills algo-nlp-ner --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-nlp-ner .claude/skills/algo-nlp-ner && rm -rf skills-src

Use ~/.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/

Facts

Skill name
algo-nlp-ner
GitHub stars
242
Token cost
~1k tokens
SKILL.md length
408 words
Files
4 (incl. references)
Skills in repo
207
Repo updated
First seen
Licence
MIT

At a glance

Implement Named Entity Recognition to identify and classify entities in text.

  • Works in 4 steps: Input Validation → Core Algorithm → Verification → …
  • The user needs to extract people
  • SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Algo NLP Ner is an agent skill from asgard-ai-platform/skills. Implement Named Entity Recognition to identify and classify entities in text. Use this skill when the user needs to extract people, organizations, locations, dates, or custom entities from documents — even if they say 'extract names from text', 'find companies mentioned', or 'entity extraction'.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/bio-annotation.md` and `references/transformer-ner.md`).

It sits in AI & LLM Engineering, covering Natural language processing. The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.

When your agent uses it

  • The user needs to extract people
  • Custom entities from documents — even if they say extract names from text
  • Find companies mentioned
  • Entity extraction

Example prompts

  • “extract names from text”
  • “find companies mentioned”
  • “entity extraction”
  • “/algo-nlp-ner”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Input Validation
  2. Core Algorithm
  3. Verification
  4. Output

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. 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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    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

Algo NLP Ner loads about 1k tokens when it runs, and up to ~6.9k if it reads all its reference files. Until then it costs about 77 tokens; SKILL.md has 408 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~77
When it runs · the whole SKILL.md, loaded when a task matches
~1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.9k

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 asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 408 words, ~1,047 tokens.

Download SKILL.mdSave it as .claude/skills/algo-nlp-ner/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
algo-nlp-ner
description
Implement Named Entity Recognition to identify and classify entities in text. Use this skill when the user needs to extract people, organizations, locations, dates, or custom entities from documents — even if they say 'extract names from text', 'find companies mentioned', or 'entity extraction'.
metadata.category
WP-45 NLP 演算法
metadata.tags
nlp, ner, entity-extraction, information-extraction

Named Entity Recognition

Overview

NER identifies and classifies named entities in text into predefined categories (Person, Organization, Location, Date, Money, etc.). Approaches: rule-based (regex, gazetteers), statistical (CRF), neural (BiLSTM-CRF, transformer-based). Modern NER uses spaCy or Hugging Face models with F1 scores 85-95%.

When to Use

Trigger conditions:

  • Extracting structured entities from unstructured text
  • Building knowledge graphs from documents
  • Preprocessing for information retrieval or question answering

When NOT to use:

  • For text classification (categorizing whole documents, not extracting entities)
  • For relation extraction between entities (need additional RE model)

Algorithm

IRON LAW: NER Performance Depends on DOMAIN Match
A model trained on news text (OntoNotes) performs poorly on medical
records or legal documents. Domain-specific entities (drug names,
legal citations, product SKUs) require domain-specific training data
or fine-tuning. Always evaluate on YOUR domain's data.
Phase 1: Input Validation

Determine: target entity types (standard: PER, ORG, LOC, DATE, MONEY or custom), input language, domain. Select appropriate pre-trained model or prepare training data. Gate: Entity types defined, model or training data available.

Phase 2: Core Algorithm

Pre-trained model approach:

  1. Load model (spaCy, Hugging Face NER pipeline)
  2. Process text through the pipeline
  3. Extract entity spans with type labels and confidence scores

Fine-tuning approach:

  1. Annotate 200+ domain-specific examples in BIO format
  2. Fine-tune transformer model (BERT, RoBERTa) on annotated data
  3. Evaluate on held-out test set
Phase 3: Verification

Evaluate: precision, recall, F1 per entity type. Check: boundary detection (exact span match) and type classification accuracy. Gate: F1 > 0.80 per entity type on domain-relevant test data.

Phase 4: Output

Return extracted entities with types, positions, and confidence.

Output Format

json
{
  "entities": [{"text": "Apple Inc.", "type": "ORG", "start": 0, "end": 10, "confidence": 0.95}],
  "metadata": {"model": "en_core_web_trf", "entities_found": 15, "types": {"PER": 5, "ORG": 6, "LOC": 4}}
}

Examples

Sample I/O

Input: "Tim Cook announced that Apple will open a new store in Taipei on March 15." Expected: [Tim Cook/PER, Apple/ORG, Taipei/LOC, March 15/DATE]

Show full SKILL.md (159 more words)Show less
Edge Cases
InputExpectedWhy
"Apple" (no context)Ambiguous (fruit or company)Context-dependent entity typing
Nested entitiesDepends on scheme"Bank of America" = ORG, "America" = LOC within
Misspelled entityMay miss"Appel" not in training data

Gotchas

  • Boundary errors: NER often gets the entity type right but the span wrong ("New" vs "New York City"). Evaluate with both exact and partial match metrics.
  • Ambiguity: "Jordan" can be a person, country, or brand. Context-dependent disambiguation is hard; some models output the most likely type.
  • Chinese/Japanese NER: No whitespace tokenization makes boundary detection harder. Use language-specific tokenizers (jieba for Chinese).
  • Annotation consistency: Training data quality is critical. Inconsistent annotations (sometimes labeling "Dr." as part of name, sometimes not) degrade model performance.
  • Entity linking: NER identifies mentions; entity linking resolves them to knowledge base entries. "Apple" → Apple Inc. (Q312) or apple (fruit). These are separate tasks.

References

  • For BIO annotation format and guidelines, see references/bio-annotation.md
  • For fine-tuning NER with transformers, see references/transformer-ner.md

© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in algo-nlp-ner of asgard-ai-platform/skills.

  • SKILL.md
  • examples/sample_scenario.md
  • references/bio-annotation.md
  • references/transformer-ner.md

Open the folder on GitHubat commit 4e7f4f8

Compare with similar skills

Algo NLP Ner 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.

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Gptqmodel Tokenizer NormalizationModelCloud/GPTQModel1.3k—~1.1kAutomated safety check: PassCustom licence
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Categories

Questions about Algo NLP Ner

What does Algo NLP Ner do?

Implement Named Entity Recognition to identify and classify entities in text. Algo NLP Ner is an agent skill from asgard-ai-platform/skills. Implement Named Entity Recognition to identify and classify entities in text.

When should I use Algo NLP Ner?

Algo NLP Ner fits situations like: the user needs to extract people; custom entities from documents — even if they say extract names from text; find companies mentioned; entity extraction.

How do I install Algo NLP Ner in Claude Code?

Run `npx skills add asgard-ai-platform/skills --skill algo-nlp-ner -a claude-code`. Or copy the skill folder (algo-nlp-ner in asgard-ai-platform/skills) into .claude/skills/algo-nlp-ner in your project. Claude Code loads it when a task matches its description.

How do I install Algo NLP Ner in Codex?

Run `npx skills add asgard-ai-platform/skills --skill algo-nlp-ner -a codex`. Or copy the skill folder (algo-nlp-ner in asgard-ai-platform/skills) into .agents/skills/algo-nlp-ner in your project. Codex loads it when a task matches its description.

Can I use Algo NLP Ner 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 asgard-ai-platform/skills --skill algo-nlp-ner -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-nlp-ner, .gemini/skills/algo-nlp-ner, .github/skills/algo-nlp-ner and .opencode/skills/algo-nlp-ner in your project.

What does Algo NLP Ner need to run?

SKILL.md names no scripts, command-line tools or credentials: Algo NLP Ner is instructions for the agent only.

Does Algo NLP Ner access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Algo NLP Ner 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 Algo NLP Ner use?

Algo NLP Ner is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Algo NLP Ner use?

About 1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.8k tokens, read only when the agent opens those files.

What are the alternatives to Algo NLP Ner?

Skills that share tags, products or a category with Algo NLP Ner: Hugging Face Tokenizers (Orchestra-Research/AI-Research-SKILLs, 13k stars), OpenMed Model Card Writer (maziyarpanahi/openmed, 5.5k stars), Gptqmodel Tokenizer Normalization (ModelCloud/GPTQModel, 1.3k stars) and Andrej Karpathy (K-Dense-AI/mimeo, 282 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Algo NLP Ner?

asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.

Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.