Skip to content
cccskills
FreePublished on GitHub

establishing-telemetry-baselines

Establishes behavioral baselines from historical telemetry (process, network, or logon events) so hunts can flag rare and first-seen activity instead of relying on static signatures. Activates for requests to build a telemetry baseline, find rare or first-seen activity, or compute frequency baselines for anomaly hunting.

The English summary is being prepared. Showing the original description.

See how to install

Files included (4)

  • SKILL.md2.8 KB
  • LICENSE340 B
  • references/api-reference.md1.2 KB
  • scripts/analyst.py3.0 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Reverses Android malware: unpacking APKs, decompiling DEX bytecode to readable Java, auditing the manifest for abused permissions and components, and locating dynamically loaded or native payloads. Activates for requests to analyze an APK, decompile DEX, or investigate a suspicious Android app.

The English summary is being prepared. Showing the original description.

meltedinhex/analyst-ai-pack22Updated Jul 7, 2026

Analyzes API call traces from a sandbox or API monitor (JSON) to group calls by category, reconstruct high-level behaviors (process injection, file drops, network, crypto), and flag suspicious call sequences. Activates for requests to analyze an API trace, interpret sandbox API logs, or identify behavior from Win32/Native API calls.

The English summary is being prepared. Showing the original description.

meltedinhex/analyst-ai-pack22Updated Jul 7, 2026

Analyzes Windows Authenticode signatures on PE files: checking for a signature, reading the signer certificate chain, detecting revoked/expired/stolen certs, and recognizing signature-stripping and catalog-signing abuse. Activates for requests to analyze a code signature, verify Authenticode, or assess signer trust on a PE.

The English summary is being prepared. Showing the original description.

meltedinhex/analyst-ai-pack22Updated Jul 7, 2026

Analyzes banking trojan webinject configurations to extract targeted institutions, injected JavaScript/HTML, and data-theft hooks, mapping the fraud workflow. Activates for requests to analyze banking trojan webinjects, parse a webinject config, or map targeted banks and credential-theft injections.

The English summary is being prepared. Showing the original description.

meltedinhex/analyst-ai-pack22Updated Jul 7, 2026

Analyzes Python-based malware packaged as PyInstaller/py2exe executables by detecting the packer, locating the embedded archive, and identifying bundled .pyc modules for extraction and decompilation. Activates for requests to analyze compiled Python malware, unpack a PyInstaller binary, or extract pyc modules from a frozen Python executable.

The English summary is being prepared. Showing the original description.

meltedinhex/analyst-ai-pack22Updated Jul 7, 2026

Analyzes cryptojacking/coinminer malware by extracting mining pool endpoints (stratum), wallet addresses, algorithm and miner identifiers (XMRig and forks), and persistence or resource-control settings from static strings and config. Activates for requests to analyze a cryptominer, extract mining pool and wallet IOCs, or identify coinminer configuration.

The English summary is being prepared. Showing the original description.

meltedinhex/analyst-ai-pack22Updated Jul 7, 2026

See all skills by meltedinhex

Report a problem with this skill