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 mapping-hunts-to-mitre-attackgit 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/mapping-hunts-to-mitre-attack)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/mapping-hunts-to-mitre-attack"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/mapping-hunts-to-mitre-attack/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/mapping-hunts-to-mitre-attack"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/mapping-hunts-to-mitre-attack.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 | $0.00717 |
| Opus 5 | $0.00036 | $0.00358 |
| Sonnet 5 | $0.00014 | $0.00143 |
| Haiku 4.5 | $0.00007 | $0.00072 |
Grade A, and why
mapping-hunts-to-mitre-attack 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 7d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mapping Hunts to MITRE ATT&CK
When to Use
- You have a backlog of hunts/detections and want a coverage view by technique.
- You need to prioritize the next hunt by mapping current coverage against relevant adversary TTPs (e.g., from a threat profile).
- You want to produce an ATT&CK Navigator layer to visualize coverage and gaps.
Do not use technique counts as a quality metric — broad shallow coverage can hide weak detections; pair the map with detection confidence.
Prerequisites
- An inventory of hunts/detections with the data sources each uses.
- The ATT&CK enterprise matrix (technique IDs and tactics) for reference.
Workflow
Step 1: Tag each hunt with techniques
Annotate every hunt/detection with the ATT&CK technique(s) it covers and a confidence score (e.g., high/medium/low based on telemetry quality and FP rate).
Step 2: Build the coverage matrix
Aggregate tags into a technique → coverage map. Group by tactic to see where the kill chain is strong or thin.
python scripts/analyst.py coverage hunts.json --out layer.json
Step 3: Overlay your threat model
Weight techniques by relevance (actors targeting your sector, observed in incidents). A gap on a high-relevance technique outranks a gap on an unlikely one.
Step 4: Identify and prioritize gaps
Surface techniques with no coverage or low confidence; rank by threat relevance and data availability to pick the next hunt.
Step 5: Visualize and share
Emit an ATT&CK Navigator layer (scores/colors) for stakeholders and to track progress over time.
Validation
- Every hunt maps to at least one valid technique ID (
^T\d{4}(\.\d{3})?$). - Coverage reflects confidence, not just presence/absence.
- The Navigator layer loads and renders the intended scores.
Pitfalls
- Counting a low-confidence hunt as full coverage.
- Mapping to overly broad parent techniques when a sub-technique is more accurate.
- Ignoring data-source feasibility when prioritizing gaps.
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.
- 7d ago First seen · 94 lines · 71 tokens per session scan A 60f9865227aa
mapping-hunts-to-mitre-attack 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 717 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 2026-09-03.
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