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 tuning-detections-to-reduce-false-positivesgit 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/tuning-detections-to-reduce-false-positives)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/tuning-detections-to-reduce-false-positives"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/tuning-detections-to-reduce-false-positives/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/tuning-detections-to-reduce-false-positives"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/tuning-detections-to-reduce-false-positives.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 | $0.00063 | $0.00623 |
| Opus 5 | $0.00032 | $0.00311 |
| Sonnet 5 | $0.00013 | $0.00125 |
| Haiku 4.5 | $0.00006 | $0.00062 |
Grade A, and why
tuning-detections-to-reduce-false-positives 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 9d 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
Tuning Detections to Reduce False Positives
When to Use
- A detection generates excessive false positives and you want a data-driven tuning plan: which fields/values drive the noise, what filters to add, and the precision impact.
- You are balancing recall against alert fatigue.
Do not use tuning that suppresses true positives — filters must target benign noise, not the malicious pattern. Always estimate the impact on true positives before applying.
Prerequisites
- Labeled alert history (CSV/JSON) with at least an outcome/label field (true_positive vs false_positive) and the alert's distinguishing fields.
Workflow
Step 1: Quantify false-positive sources
python scripts/analyst.py analyze alerts.csv --label verdict --field ParentImage
Ranks field values by how much false-positive volume they drive and computes current precision/false-positive rate.
Step 2: Recommend filters
Suggest allow-list filters for values that are overwhelmingly benign (high FP, no/low TP), with the projected precision gain.
Step 3: Estimate impact
Report how many true positives would be lost (should be zero/near-zero for safe filters).
Step 4: Apply and monitor
Add the safe filters to the rule's filter/exclusion and continue monitoring precision.
Validation
- FP-driving values are ranked by their false-positive contribution.
- Recommended filters target values with negligible true-positive loss.
- Projected precision and TP-loss are reported.
Pitfalls
- Filtering a value that also carries true positives, blinding the detection.
- Over-fitting to one time window's noise.
- Tuning by gut feel instead of measured FP/TP contribution.
References
- See
references/api-reference.mdfor the tuner. - Sigma spec and detection-engineering references (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.
- 9d ago First seen · 86 lines · 63 tokens per session scan A 22f0e3c64dfd
tuning-detections-to-reduce-false-positives is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 63 tokens to every session and 623 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 2026-09-03.
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