Canonicalize Entity

Organization

yogsoth-ai

canonicalize-entity

Resolve aliases and near-duplicates into canonical entities while preserving provenance and rejecting false merges; entity schema is configurable.

Overview

Publisher	yogsoth-ai
Repository	`de-anthropocentric-research-engine`
Skill name	`canonicalize-entity`
Stars	505
Forks	43
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by yogsoth-ai on GitHub. Read the source before you install it.

Installation

Install the Canonicalize Entity AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/canonicalize-entity
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/canonicalize-entity

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine.git /tmp/de-anthropocentric-research-engine
mkdir -p .claude/skills
cp -r /tmp/de-anthropocentric-research-engine/skills/canonicalize-entity .claude/skills/canonicalize-entity

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Canonicalize Entity in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Canonicalize Entity on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Canonicalize Entity is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

canonicalize-entity

Purpose

Resolve aliases and near-duplicates into canonical entities while preserving provenance and rejecting false merges.

Input contract

yaml
required: [raw_entities, entity_schema, alias_evidence]
optional: [synonym_dictionary, organization_relationships, graph_edges, canonicalization_policy]
constraints: [the entity schema is explicit; merge requires semantic identity evidence; preserve provenance and redirect relationships]

Procedure

Normalize names, identifiers, and aliases under the caller-supplied entity schema.
Search for near-duplicates and compare definitions, identifiers, and provenance.
Merge only semantically identical entities into a canonical record and redirect dependent edges.
Preserve rejected-merge pairs and rationale, then return the canonicalized register.

If canonical entities are stable enough to compare against the target domain, consider map-coverage-space as the next tactic.

Output contract

yaml
produces: [canonical_entities, alias_map, merge_decisions, redirected_edges, rejected_merges]
delta_fields: [findings, evidence_updates, decisions, uncertainties]

Quality gates

Preserve the source merge-candidates gate: scan at least 10 concepts, or all concepts if fewer than 10 exist; a candidate score >7.0 is a merge flag, not proof.
Before merging aliases, verify pages refer to the same concept rather than merely related concepts; retain inline links and edge redirects where applicable.
Assignee normalization preserves parent, subsidiary, and acquired-entity relationships rather than collapsing organizations into one name.

Parameterization

The caller must provide the entity type and schema, canonical naming policy, alias/synonym evidence, merge confidence rule, graph-edge policy, and whether the task concerns concepts, assignees, or another entity class.

Failure and counterexamples

Reject merges without semantic identity evidence, threshold-only matches, or records that lose provenance or dependent edges.

Provenance map

resolved: knowledge-structuring/merge-candidates
resolved: knowledge-structuring/alias-resolution
resolved: knowledge-acquisition/assignee-normalization
intermediate: Pass3/merge-near-duplicate-concepts
intermediate: Pass3/normalize-assignee
intermediate: Pass4/map-field-taxonomy

Verbatim source criteria excerpts

merge-candidates line 18: If search returns another concept with score > 7.0, flag as merge candidate.
merge-candidates line 25: Must scan at least 10 concepts (or all concepts if fewer than 10 exist).
alias-resolution line 29: Before merging, verify the pages truly refer to the same concept (not related but distinct concepts).

Preserved source criteria ledger

source	physical line	kind	source criterion
knowledge-structuring/merge-candidates	22	numeric	Scan at least 10 concepts, or all if fewer than 10; score >7.0 flags a merge candidate.
knowledge-structuring/alias-resolution	28	gate	Verify semantic identity before merging; related-but-distinct concepts must not merge.

Frequently asked questions

What does the Canonicalize Entity AI skill do?

Resolve aliases and near-duplicates into canonical entities while preserving provenance and rejecting false merges; entity schema is configurable.

Why use Canonicalize Entity on TypingMind?

Because you install it once and use it with any model. Canonicalize Entity is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Canonicalize Entity in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/canonicalize-entity. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Canonicalize Entity?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Canonicalize Entity?

As many as you like. As long as a model supports skills, you can use Canonicalize Entity with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Canonicalize Entity AI skill free?

Yes. It is published on GitHub by yogsoth-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

canonicalize-entity

Purpose

Input contract

Procedure

Output contract

Quality gates

Parameterization

Failure and counterexamples

Provenance map

Verbatim source criteria excerpts

Preserved source criteria ledger

Frequently asked questions

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