Borrowing it
Nothing to install: this file belongs to omeryemba/mcp-hayabusa. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/omeryemba/mcp-hayabusa/master/.claude/skills/ingest-ti/SKILL.mdgit clone --depth 1 https://github.com/omeryemba/mcp-hayabusaWrote 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/omeryemba/mcp-hayabusa/ingest-ti)<a href="https://agentmods.dev/skills/omeryemba/mcp-hayabusa/ingest-ti"><img src="https://agentmods.dev/badge/skills/omeryemba/mcp-hayabusa/ingest-ti/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/omeryemba/mcp-hayabusa/ingest-ti"><img src="https://agentmods.dev/badge/skills/omeryemba/mcp-hayabusa/ingest-ti.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 | $0.00060 | $0.01486 |
| Opus 5 | $0.00030 | $0.00743 |
| Sonnet 5 | $0.00012 | $0.00297 |
| Haiku 4.5 | $0.00006 | $0.00149 |
Grade A, and why
ingest-ti 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 10d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Threat Intelligence Ingestion & Correlation
Ingests IOC data from local files, normalizes every indicator to a common schema, then correlates it — across multiple TI sources, against this project's installed Sigma rule coverage, and optionally against a saved Hayabusa scan/timeline result — via two deterministic scripts. This is the backing skill for the /ingest-ti command (.claude/commands/ingest-ti.md).
Normalized IOC schema
Every indicator, regardless of source format, is normalized to exactly these fields (all keys always present; a value may be null, but the key isn't dropped):
{
"type": "ip",
"value": "1.2.3.4",
"confidence": 0.8,
"source": "native:watchlist",
"first_seen": "2026-01-01T00:00:00Z",
"attack_technique": "T1071.001",
"notes": "category=Network activity; comment=C2 IP"
}
type is a closed set: ip, domain, hash, url, other. An entry with an unrecognized type is coerced to other (not dropped); an entry missing value entirely is skipped (it can't be correlated on anything).
How to apply
- Ingest each source file separately with
ingest_ti.py— one file in, one normalized JSON report out. Never hand-parse a TI file's JSON yourself; the format-detection and normalization logic here is deterministic and already handles both supported formats' edge cases (MISP type mapping, epoch timestamp conversion, missing-field defaulting). - Correlate normalized reports together with
correlate_ti.py— takes 1+ ofingest_ti.py's output files, merges/dedupes IOCs sharing a(type, value)key across sources, and checks every referenced ATT&CK technique against this project's installed Sigma rules viaanalyze_coverage/suggest_rule(mcp_hayabusa.knowledge— the same functions backing this project'sanalyze_coverage/suggest_ruleMCP tools, not reimplemented here). - Optionally correlate against local evidence by passing
--hayabusa-result <path>tocorrelate_ti.py, pointing at a JSON file already saved to disk from a prior Hayabusa MCP tool call (scan_evtx,hayabusa_csv_timeline,hayabusa_json_timeline, etc.) — each merged indicator's value is then substring-matched against that file's raw text.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 69 lines · 60 tokens per session scan A 664b7c6a9bbf
ingest-ti is a skill published in the GitHub repository omeryemba/mcp-hayabusa (0 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 1,486 once invoked, about $0.0003 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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