detect-anomalies-aiops SKILL.md skillA
detect-anomalies-aiops from pjt222/agent-almanac is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add pjt222/agent-almanac/detect-anomalies-aiops@v1.0Pins v1.0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- pjt222/agent-almanac
- Path
i18n/caveman-lite/skills/detect-anomalies-aiops/SKILL.md- Versions recorded
- 3
- Latest version
- v1.0, committed Aug 17, 2026
- First version recorded
- Apr 19, 2026
- Size
- 9,170 bytes
- Repository stars
- 43
- License
- MIT
Implement AI-powered anomaly detection for operational metrics using time series analysis (Isolation Forest, Prophet, LSTM), alert correlation, and root cause analysis. Reduce alert fatigue by intelligently identifying true anomalies in system metrics, logs, and traces. Use when operations teams are overwhelmed by alert volume, when detecting complex multi-metric anomalies beyond static thresholds, when seasonal patterns make thresholds ineffective, or when needing to predict issues…
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Detect Anomalies for AIOps > See [Extended Examples](references/EXAMPLES.md) for complete configuration files and templates. Apply machine learning to detect anomalies in operational metrics, correlate alerts, and reduce false positives. ## When to Use - Operations team overwhelmed by alert volume (>100 alerts/day) - Need to detect complex multi-metric anomalies (not threshold breaches) - Seasonal patterns make static thresholds ineffective - Want to predict issues before they impact users (proactive detection) - Need to correlate related alerts to identify root cause - Monitoring system generates too many false positives - Want to detect subtle performance degradation trends ## Inputs - **Required**: Time series metrics from monitoring system (CPU, memory, latency, error rate) - **Required**: Historical data (30-90 days minimum) - **Optional**: Alert history with labels (true positive / false positive) - **Optional**: System topology (service dependencies) - **Optional**: Log data for correlation - **Optional**: Deployment/change events for context ## Procedure ### Step 1: Set Up Environment and Load Data Install dependencies and prepare time series data for analysis. …
Read the whole file at its exact version.
How to install
mdr add pjt222/agent-almanac/detect-anomalies-aiops@sha256:a1f935059f8fd3c6The line at the top of this page pins the label v1.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_daucbct35ghnt6g5)
3 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 3 versions of this file, between Apr 19, 2026 and Aug 17, 2026, about one every 60 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.0 latest | 2026-08-17 | a784899 | 9,170 B | A | view · diff |
| v1.0 | 2026-04-23 | eb36d45 | 9,141 B | A | view · diff |
| v1.0 | 2026-04-19 | e334ebd | 9,201 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (9170 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
pjt222/agent-almanac · 43 stars · license MIT · pushed 2026-11-06 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_daucbct35ghnt6g5 GET https://markdownregistry.com/api/v1/resolve?ref=pjt222/agent-almanac/detect-anomalies-aiops GET https://markdownregistry.com/api/v1/blob/a1f935059f8fd3c6ff65c94d19a00e4db182bfc523decd1de96547e22dfd5ea0
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.