factual-knowledge-extractor

factual-knowledge-extractor is an agent for Claude Code from jingnanzhou/fellow. It costs 25 tokens per session (2,539 once invoked), scanned A, original, Apache-2.0.

A code-analysis agent that catalogs the data models and relationships in a project. Data models are the classes, schemas, and structures used to represent information such as users, products, or orders.

In plain words
What is it for?
Use it to document classes, interfaces, structs, schemas, configuration objects, DTOs, value objects, and domain entities. It can record fields, methods, constraints, visibility, and relationships such as containment or inheritance.
Why use it?
It gives coding assistants a factual map of what already exists before new code is added. This helps avoid duplicating entities or misunderstanding their fields and relationships.

Agent for Claude Code

Written for Claude Code: ${CLAUDE_PLUGIN_ROOT} variable. Also seen: model in frontmatter; names the TodoWrite tool.

Runs only inside its plugin — its command needs a path that Claude Code sets for a plugin’s own hooks and for nothing else. Install the plugin, not this.

Part of the fellow plugin — 4 commands, 4 agents shipped together

Good fit Use it to document classes, interfaces, structs, schemas, configuration objects, DTOs, value objects, and domain entities. It can record fields, methods, constraints, visibility, and relationships such as containment or inheritance.

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Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add jingnanzhou/fellow
Claude Code
/plugin install fellow

Made for: Claude Code.

Or install fellow, the plugin that ships this one along with the rest of its 4 commands, 4 agents.

Wrote 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.

agentmods badge for factual-knowledge-extractor

README.md
[![agentmods](https://agentmods.dev/badge/agents/jingnanzhou/fellow/factual-knowledge-extractor/github.svg)](https://agentmods.dev/agents/jingnanzhou/fellow/factual-knowledge-extractor)
Your own site
<a href="https://agentmods.dev/agents/jingnanzhou/fellow/factual-knowledge-extractor"><img src="https://agentmods.dev/badge/agents/jingnanzhou/fellow/factual-knowledge-extractor/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.

agentmods 80×15 button for factual-knowledge-extractor

Your own site · 80×15
<a href="https://agentmods.dev/agents/jingnanzhou/fellow/factual-knowledge-extractor"><img src="https://agentmods.dev/badge/agents/jingnanzhou/fellow/factual-knowledge-extractor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 25 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,539 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00025 $0.02539
Opus 5 $0.00013 $0.01269
Sonnet 5 $0.00005 $0.00508
Haiku 4.5 $0.00003 $0.00254

Measured 8d ago against content hash 8f73fee05779, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

factual-knowledge-extractor 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 8d 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.

agents/factual-knowledge-extractor.md · 332 lines

How it starts

The opening of the file, as written. The whole thing — 332 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Factual Knowledge Extraction Agent

Objective

Analyze the target codebase and extract data/object models to understand WHAT exists in this project.

This extraction provides the foundation for coding context - understanding what entities, data structures, and domain concepts already exist before adding new code.


What to Extract

1. Classes and Data Structures

For each significant class, data structure, or entity:

Identify:

  • Primary classes (e.g., User, Product, Order)
  • Data transfer objects (DTOs)
  • Value objects
  • Domain entities
  • Configuration objects
  • Data schemas

For Each Entity, Extract:

  • Name: Class/struct/entity name
  • Type: class, interface, struct, schema, etc.
  • Purpose: What does this represent in the domain?
  • Attributes/Fields:
    • Name
    • Type (string, int, object, etc.)
    • Required/optional
    • Default value (if any)
    • Description/purpose
    • Constraints (e.g., unique, min/max length, format)
  • Methods/Functions (if applicable):
    • Name
    • Signature (parameters, return type)
    • Purpose (what does it do?)
    • Visibility (public, private, protected)
  • Relationships:
    • Has-a (composition): Entity contains other entities
    • Is-a (inheritance): Entity extends/implements
    • Uses-a (dependency): Entity uses/references other entities
    • Multiplicity: one-to-one, one-to-many, many-to-many
  • Invariants/Constraints:
    • Business rules that must always hold
    • Validation rules
    • Required fields
    • Format constraints
    • Cross-field constraints
  • Grounding:
    • File path
    • Line numbers (start, end)
    • Module/package

File Filtering

IMPORTANT: Use the shared filtering utilities to skip non-production code.

Using the Filter Module

The filtering utilities are located at ${CLAUDE_PLUGIN_ROOT}/tools/file_filters.py. When you need to programmatically check files, you can use the helper script:

# Check if files should be analyzed
python3 ${CLAUDE_PLUGIN_ROOT}/tools/should_analyze.py src/app.js node_modules/lib.js
# Output: ANALYZE: src/app.js
#         SKIP: node_modules/lib.js

Read the full file on GitHub · 332 lines

Changes

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.

  1. 8d ago First seen · 332 lines · 25 tokens per session scan A 8f73fee05779

Subscribe to this mod's changes

factual-knowledge-extractor is an agent published in the GitHub repository jingnanzhou/fellow (4 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 25 tokens to every session and 2,539 once invoked, about $0.0001 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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