Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ThreatRecall/zettelforgenpx agentmods add skills/threatrecall/zettelforge/openclawWrote 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/threatrecall/zettelforge/openclaw)<a href="https://agentmods.dev/skills/threatrecall/zettelforge/openclaw"><img src="https://agentmods.dev/badge/skills/threatrecall/zettelforge/openclaw/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/skills/threatrecall/zettelforge/openclaw"><img src="https://agentmods.dev/badge/skills/threatrecall/zettelforge/openclaw.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 | $0.00080 | $0.00484 |
| Opus 5.5 | $0.00032 | $0.00194 |
| Sonnet 5.5 | $0.00016 | $0.00097 |
| Haiku 4.5 | $0.00008 | $0.00048 |
Grade A, and why
zettelforge 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 1mo 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
ZettelForge v2.0.0: CTI Agentic Memory
Store, recall, and synthesize threat intelligence. All systems operational.
API
from zettelforge import MemoryManager
mm = MemoryManager()
# Store
note, status = mm.remember("APT28 uses Cobalt Strike via CVE-2024-1111", domain="cti")
# Two-phase extraction (selective, deduplicating)
results = mm.remember_with_extraction("APT28 dropped DROPBEAR", domain="cti")
# Ingest report (auto-chunks)
results = mm.remember_report(content="...", source_url="https://...", domain="cti")
# Recall (blended vector + graph + cross-encoder reranking)
results = mm.recall("What tools does APT28 use?", k=10)
# Entity lookup (O(1), alias resolution: Fancy Bear -> APT28)
mm.recall_actor("Fancy Bear")
mm.recall_cve("CVE-2024-3094")
mm.recall_tool("cobalt-strike")
# Graph traversal
paths = mm.traverse_graph("actor", "apt28", max_depth=2)
# Synthesize
result = mm.synthesize("Summarize APT28", format="synthesized_brief")
# OpenCTI sync
from zettelforge.opencti_sync import sync_opencti
stats = sync_opencti(mm, limit=50)
Web UI
python web/app.py # http://localhost:8088
Benchmarks
| Benchmark | Score | p50 Latency |
|---|---|---|
| CTI Retrieval | 75.0% | 111ms |
| LOCOMO | 18.0% | 157ms |
| RAGAS | 78.1% | — |
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.
- 1mo ago First seen · 57 lines · 80 tokens per session scan A ff5f873cbb76
zettelforge is a skill published in the GitHub repository ThreatRecall/zettelforge (66 stars, last pushed 2d ago), licensed MIT. It adds 80 tokens to every session and 484 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-11.
Other skills, from other repositories
qdrant
Vector search engine for production RAG systems.
dspy
DSPy: declarative LM programs, auto-optimize prompts, RAG.
chroma
Embedding database for RAG and semantic search.
pinecone
Managed vector DB for production RAG and search.
faiss
Fast vector similarity search at billion scale.
pinecone-research
Agent RAG and long-term memory with Pinecone.