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 skills add meltedinhex/analyst-ai-pack --skill extracting-iocs-from-analysis-outputgit clone --depth 1 https://github.com/meltedinhex/analyst-ai-packWrote 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/meltedinhex/analyst-ai-pack/extracting-iocs-from-analysis-output)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/extracting-iocs-from-analysis-output"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/extracting-iocs-from-analysis-output/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/meltedinhex/analyst-ai-pack/extracting-iocs-from-analysis-output"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/extracting-iocs-from-analysis-output.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 | $0.00079 | $0.00665 |
| Opus 5 | $0.00039 | $0.00332 |
| Sonnet 5 | $0.00016 | $0.00133 |
| Haiku 4.5 | $0.00008 | $0.00067 |
Grade A, and why
extracting-iocs-from-analysis-output 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 11d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extracting IOCs from Analysis Output
When to Use
- You have raw analysis output (strings, sandbox JSON, PCAP notes, logs) and need the atomic indicators out of it.
- You want a deduplicated, typed indicator set to feed enrichment, defanging, or detection.
- You are building the indicator section of a report from analysis artifacts.
Do not use naive substring matching that produces noise — validate indicator shapes and filter obvious false positives (version strings that look like IPs, library domains).
Prerequisites
- The analysis artifacts as text/JSON; the defanging skill for safe output.
Workflow
Step 1: Gather the artifacts
Collect strings output, sandbox report fields, network summaries, and relevant log excerpts into text the extractor can scan.
Step 2: Extract by pattern
Pull URLs, domains, IPv4 addresses, email addresses, and hashes (MD5/SHA-1/SHA-256) with validated patterns; also capture host artifacts (mutexes, registry keys, file paths) where the format allows.
python scripts/analyst.py extract analysis.txt
Step 3: Filter and deduplicate
Drop benign noise (Microsoft/CDN domains, localhost, RFC1918 where irrelevant) and deduplicate; keep a record of what was filtered and why.
Step 4: Type and hand off
Tag each indicator with its type and pass the set to enrichment/defanging for reporting.
Validation
- Extracted indicators match valid shapes (no malformed IPs/hashes).
- The set is deduplicated and obvious benign noise is filtered with a rationale.
- Each indicator is typed and ready for enrichment/defanging.
Pitfalls
- Capturing version numbers as IPs or library hostnames as C2.
- Missing indicators split across lines or encoded (base64) in the artifacts.
- Not recording what was filtered, losing analyst auditability.
References
- See
references/api-reference.mdfor the IOC extractor. - MITRE ATT&CK and indicator-extraction patterns (linked in frontmatter).
What ships with it
3 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.
- 11d ago First seen · 86 lines · 79 tokens per session scan A 14d8689dd449
extracting-iocs-from-analysis-output is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 79 tokens to every session and 665 once invoked, about $0.0004 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 2026-08-30.
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