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 defanging-and-sharing-iocsgit 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/defanging-and-sharing-iocs)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/defanging-and-sharing-iocs"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/defanging-and-sharing-iocs/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/defanging-and-sharing-iocs"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/defanging-and-sharing-iocs.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 | $0.00071 | $0.00693 |
| Opus 5 | $0.00036 | $0.00347 |
| Sonnet 5 | $0.00014 | $0.00139 |
| Haiku 4.5 | $0.00007 | $0.00069 |
Grade A, and why
defanging-and-sharing-iocs 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.
What it actually says
Defanging and Sharing IOCs
When to Use
- You are about to put indicators in a report, ticket, chat, or email and must prevent accidental clicks or resolution.
- You need to export indicators in a structured format for a sharing platform (MISP/STIX) or feed.
- You are normalizing a messy indicator list before distribution.
Do not use raw, live indicators in any document a human or tool might auto-process — a clicked URL or auto-resolved domain can tip off the adversary or infect a reader.
Prerequisites
- A list of extracted indicators (from the IOC-extraction skill) and the target sharing format.
Workflow
Step 1: Normalize the indicators
Deduplicate and canonicalize (lowercase domains, strip trailing dots) so the output is clean.
Step 2: Defang for safety
Apply standard defanging: http→hxxp, .→[.], @→[at], ://→[://]. This blocks
hyperlinking and casual copy-paste resolution.
python scripts/analyst.py defang iocs.txt
Step 3: Classify and structure
Tag each indicator by type (url, domain, ipv4, email, hash) and emit a structured form (CSV or a minimal STIX-style bundle) for the target platform.
Step 4: Add context
Attach the source, first-seen date, confidence, and related ATT&CK technique so consumers can act on the indicator.
Validation
- No output indicator is clickable or auto-resolvable (all are defanged).
- Each indicator is correctly typed and deduplicated.
- The structured export imports cleanly into the target platform.
Pitfalls
- Defanging inconsistently, so some indicators remain live.
- Sharing indicators without context (source, confidence), reducing their value.
- Over-defanging hashes (no need) or mangling indicators so they cannot be re-fanged for use.
References
- See
references/api-reference.mdfor the defang/export helper. - STIX 2.1 and MISP (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 · 85 lines · 71 tokens per session scan A e25bf719f967
defanging-and-sharing-iocs is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 71 tokens to every session and 693 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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