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/qa10devteam/behive/hermesnpx skills add qa10devteam/behive --skill hermesgit clone --depth 1 https://github.com/qa10devteam/behiveWhat 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 | $0.00057 | $0.01577 |
| Opus 5 | $0.00028 | $0.00788 |
| Sonnet 5 | $0.00011 | $0.00315 |
| Haiku 4.5 | $0.00006 | $0.00158 |
Grade A, and why
behive-research scanned grade A with 1 finding 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 2d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s http://localhost:8091/health | jq . How it starts
The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BeHive Deep Research
Overview
BeHive is a deep research engine that extracts structured, scored claims from any topic. Unlike simple web search, it:
- Decomposes topics into research axes
- Fetches 1000+ sources via 8-layer stealth drones (70+ APIs)
- Extracts typed claims with per-claim quality scores (0.0–1.0)
- Builds entity relationship graphs (Neo4j)
- Produces synthesized reports with inline citations
This skill connects Hermes Agent to a running BeHive instance.
When to Use
- User says "research X deeply", "gather intelligence on Y", "find everything about Z"
- User needs verified facts with sources, not LLM-generated summaries
- User wants claims they can cite, filter by quality, or query later
- User asks to build a knowledge base on a topic over time
- User says "run a mission on X", "scale 100", "deep dive"
Don't Use For
- Simple factual lookups (use web_search)
- Single-page content extraction (use web_extract)
- Real-time news (BeHive takes 5-15 min per mission)
Prerequisites
BeHive API running at http://localhost:8091 or configured endpoint.
Verify:
curl -s http://localhost:8091/health | jq .
If using MCP (recommended), configure in ~/.hermes/config.yaml:
mcp_servers:
behive:
url: http://localhost:8090/mcp
transport: streamable-http
Quick Research (MCP)
If BeHive MCP is configured, use MCP tools directly:
mcp_behive_research_topic(request={"query": "NVIDIA GPU market 2025-2026", "depth": 3, "force": true})
Then poll:
mcp_behive_mission_status(job_id="<returned_id>")
Get report:
mcp_behive_get_report(job_id="<id>", format="markdown")
Search past knowledge:
mcp_behive_search_knowledge(query="NVIDIA revenue", limit=20)
Research via REST API (terminal)
Start a Mission
curl -s -X POST http://localhost:8091/research \
-H "Content-Type: application/json" \
-d '{"topic": "EU AI Act enforcement mechanisms 2026", "scale": 30, "depth": 3}' | jq .
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.
- 2d ago First seen · 188 lines · 57 tokens per session scan A ed189a733948
behive-research is a skill published in the GitHub repository qa10devteam/behive (143 stars, last pushed 19d ago), licensed MIT. It adds 57 tokens to every session and 1,577 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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