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 establishing-telemetry-baselinesgit 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/establishing-telemetry-baselines)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/establishing-telemetry-baselines"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/establishing-telemetry-baselines/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/establishing-telemetry-baselines"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/establishing-telemetry-baselines.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 | $0.00072 | $0.00669 |
| Opus 5 | $0.00036 | $0.00334 |
| Sonnet 5 | $0.00014 | $0.00134 |
| Haiku 4.5 | $0.00007 | $0.00067 |
Grade A, and why
establishing-telemetry-baselines 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.
What it actually says
Establishing Telemetry Baselines
When to Use
- You want to hunt for rare or first-seen behavior (uncommon process names, parent/child pairs, destinations) by comparing current activity to a historical baseline.
- You are reducing noise by establishing what "normal" looks like before alerting on outliers.
Do not use a baseline built from a compromised period as "normal" — seed it from a known-good window. This skill computes statistics from telemetry and executes nothing.
Prerequisites
- Historical telemetry (CSV/JSON) with a categorical field to baseline (e.g., process name, parent-child pair, destination host).
Workflow
Step 1: Build the baseline
python scripts/analyst.py baseline history.csv --field Image
Computes per-value counts, frequency (stacked-rank), and the set of values seen, saved as a JSON baseline.
Step 2: Score new activity against the baseline
python scripts/analyst.py compare new.csv --field Image --baseline baseline.json
Flags values not present in the baseline (first-seen) and values below a rarity threshold.
Step 3: Triage outliers
Investigate first-seen and rare values; many will be benign-but-new — corroborate with context.
Step 4: Maintain
Refresh the baseline on a rolling known-good window to avoid drift.
Validation
- The baseline captures counts and the value set from the historical window.
- First-seen values in new data are correctly identified as absent from the baseline.
- Rarity thresholds are explicit and tunable.
Pitfalls
- Baselining a compromised window, normalizing malicious activity.
- Too-short baseline windows making common items look rare.
- High-cardinality fields (full command lines) needing normalization before baselining.
References
- See
references/api-reference.mdfor the baseliner. - The ThreatHunting Project and ATT&CK hunting resources (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.
- 10d ago First seen · 91 lines · 72 tokens per session scan A 4684e51faff8
establishing-telemetry-baselines is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 72 tokens to every session and 669 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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