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.
/plugin marketplace add jingnanzhou/fellow/plugin install fellowWrote 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/agents/jingnanzhou/fellow/factual-knowledge-extractor)<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.
<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>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 | $0.00025 | $0.02539 |
| Opus 5 | $0.00013 | $0.01269 |
| Sonnet 5 | $0.00005 | $0.00508 |
| Haiku 4.5 | $0.00003 | $0.00254 |
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.
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
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.
- 8d ago First seen · 332 lines · 25 tokens per session scan A 8f73fee05779
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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