Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/binary16labs/prime-silo/document_ingestionnpx skills add binary16labs/prime-silo --skill document_ingestiongit clone --depth 1 https://github.com/binary16labs/prime-siloWrote 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/skills/binary16labs/prime-silo/document_ingestion)<a href="https://agentmods.dev/skills/binary16labs/prime-silo/document_ingestion"><img src="https://agentmods.dev/badge/skills/binary16labs/prime-silo/document_ingestion.svg" alt="Measured on agentmods" 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 | $0.00025 | $0.00219 |
| Opus 5 | $0.00013 | $0.00110 |
| Sonnet 5 | $0.00005 | $0.00044 |
| Haiku 4.5 | $0.00003 | $0.00022 |
Grade A, and why
document-ingestion 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 4d 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.
What it actually says
Document Ingestion Skill
This skill enables agents to seamlessly ingest unstructured documents into the project's isolated Neo4j Knowledge Graph.
Capabilities
- Entity Extraction: Extract entities (people, concepts, systems) from documents.
- Relationship Mapping: Map the relationships between entities.
- Neo4j Integration: Push the extracted nodes and edges into a local Neo4j instance to serve as the Knowledge Mesh brain for this project.
When to use
Use this skill whenever a user uploads a new design document, requirements spec, or unstructured text file and wants it "ingested into the graph" or "added to the knowledge mesh".
Instructions
- Parse the target document.
- Formulate Cypher queries to
MERGEnodes and relationships. - Use your execution tools to run the Cypher queries against the Neo4j endpoint configured in the local workspace.
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.
- 4d ago First seen · 25 lines · 25 tokens per session scan A 06b39c544828
document-ingestion is a skill published in the GitHub repository binary16labs/prime-silo (5 stars, last pushed 11d ago), licensed MIT. It adds 25 tokens to every session and 219 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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