Agent skill

Observability Pattern Detector

by majiayu000 in majiayu000/claude-skill-registry

Automated pattern recognition in Claude Code telemetry. An agent skill from majiayu000/claude-skill-registry.

MITAuto-check passedDevOps & Cloud

Install Observability Pattern Detector

skills CLI
$ npx skills add majiayu000/claude-skill-registry --skill observability-pattern-detector -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install majiayu000/claude-skill-registry observability-pattern-detector --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analysis/observability-pattern-detector-adaptationio-skrillz-2 .claude/skills/observability-pattern-detector && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
observability-pattern-detector
GitHub stars
666
Used in
1 other repo
Token cost
~1.6k tokens
SKILL.md length
322 words
Files
2
Skills in repo
971
Repo updated
First seen
Licence
MIT

At a glance

Automated pattern recognition in Claude Code telemetry. An agent skill from majiayu000/claude-skill-registry.

  • Detecting failures
  • SKILL.md covers Data Source, Operations, Example Output and Pattern Detection Queries, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Conversation patterns

What it does

Observability Pattern Detector is an agent skill from majiayu000/claude-skill-registry. Automated pattern recognition in Claude Code telemetry. Use when detecting failures, slowness, anomalies, trends, inefficiencies, conversation patterns, or tool sequences.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `metadata.json`).

It sits in DevOps & Cloud, covering Observability and Anomaly detection. The repository describes itself as: Searchable Claude Code skills catalog with source-linked guides and generated registry artifacts. The licence is MIT.

When your agent uses it

  • Detecting failures
  • Conversation patterns

Example prompts

  • “/observability-pattern-detector”

What it can do on your machine

Read from SKILL.md and the folder at commit 000116a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are logql and json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Observability Pattern Detector loads about 1.6k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 322 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from majiayu000/claude-skill-registry at commit 000116a, republished under its MIT licence (© majiayu000). 322 words, ~1,607 tokens.

Download SKILL.mdSave it as .claude/skills/observability-pattern-detector/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
observability-pattern-detector
description
Automated pattern recognition in Claude Code telemetry. Use when detecting failures, slowness, anomalies, trends, inefficiencies, conversation patterns, or tool sequences.

Observability Pattern Detector

Automated pattern recognition and anomaly detection in Claude Code telemetry data from enhanced hooks.

Data Source

Primary: {job="claude_code_enhanced"} in Loki

Operations

detect-failures

Group similar failures and identify patterns.

logql
{job="claude_code_enhanced", event_type="tool_result", status="error"} | json

Algorithm: Group by error_type → Calculate frequency → Rank by impact. Output: Failure patterns with occurrences, affected tools, first/last seen, trend.

detect-slowness

Identify large response patterns (proxy for slowness).

logql
{job="claude_code_enhanced", event_type="tool_result"} | json | response_length > 100000

Algorithm: Flag responses >100k chars → Group by tool → Identify patterns. Output: Slow operations with response sizes, affected tools.

detect-anomalies

Statistical anomaly detection in sessions.

logql
{job="claude_code_enhanced", event_type="session_end"} | json | turn_count > 50

Methods: High turn count, long duration, many errors per session. Output: Anomalous sessions with metrics, likely cause.

Long-term trend analysis.

logql
sum(count_over_time({job="claude_code_enhanced", event_type="tool_call"} [1d]))

Metrics: Tool usage trend, error rate trend, session frequency trend. Output: Trends with direction (increasing/decreasing/stable), rate.

detect-waste

Identify inefficiencies (redundant operations).

logql
{job="claude_code_enhanced", event_type="tool_call"} | json | line_format "{{.tool_name}}:{{.previous_tool}}"

Patterns:

  • Multiple reads of same file (Read→Read)
  • Repeated failed operations
  • Excessive Glob before Read
  • Many small edits vs one large edit Output: Waste patterns with occurrences, recommendations.
detect-conversation-patterns

Analyze user prompt patterns.

logql
sum by (pattern) (count_over_time({job="claude_code_enhanced", event_type="user_prompt"} | json [24h]))

Patterns:

  • Question frequency (pattern="question")
  • Debugging sessions (pattern="debugging")
  • Creation tasks (pattern="creation")
  • Ultrathink usage (pattern="ultrathink") Output: Conversation style distribution, trends.
detect-tool-sequences

Identify common tool call sequences.

logql
{job="claude_code_enhanced", event_type="tool_call"} | json | line_format "{{.previous_tool}} → {{.tool_name}}"

Common Patterns:

  • Glob → Read (file discovery)
  • Read → Edit (modify after read)
  • Grep → Read (search then open)
  • Task → Task (parallel agents) Output: Sequence frequencies, unusual patterns.
detect-subagent-patterns

Analyze Task tool usage patterns.

logql
{job="claude_code_enhanced", event_type="tool_call", tool="Task"} | json

Patterns:

  • Subagent types distribution
  • Parallel spawning patterns
  • Subagent success rates Output: Subagent usage analytics, recommendations.
detect-context-issues

Identify context window problems.

logql
{job="claude_code_enhanced", event_type="context_compact"} | json

Patterns:

  • Frequent auto-compaction
  • High context usage sessions
  • Large response accumulation Output: Context management issues, optimization suggestions.
detect-permission-patterns

Analyze permission request patterns.

logql
{job="claude_code_enhanced", event_type="permission_request"} | json

Patterns:

  • Frequent permission requests
  • Permission types distribution
  • Permission denials Output: Permission friction points, automation opportunities.
detect-repo-patterns

Repository activity patterns.

logql
sum by (repo) (count_over_time({job="claude_code_enhanced", event_type="tool_call"} | json [7d]))

Patterns:

  • Most active repos
  • Tool usage by repo
  • Error rates by repo Output: Project-level insights, cross-repo comparisons.

Example Output

json
{
  "failure_patterns": [
    {
      "pattern_id": "file_not_found",
      "signature": "File does not exist",
      "occurrences": 23,
      "affected_tools": ["Read", "Edit"],
      "trend": "stable",
      "recommendation": "Add file existence check before operations"
    }
  ],
  "tool_sequence_patterns": [
    {
      "sequence": "Glob → Read → Edit",
      "occurrences": 156,
      "context": "Standard file modification flow"
    }
  ],
  "conversation_patterns": [
    {
      "pattern": "debugging",
      "percentage": 35,
      "avg_turns": 12,
      "common_tools": ["Bash", "Read", "Grep"]
    }
  ],
  "context_issues": [
    {
      "issue": "auto_compaction_frequent",
      "sessions_affected": 5,
      "recommendation": "Use more focused queries, split large tasks"
    }
  ]
}

Pattern Detection Queries

Failure Patterns
logql
# Group errors by type
sum by (error_type, tool) (count_over_time({job="claude_code_enhanced", event_type="tool_result", status="error"} | json [24h]))

# Error timeline
{job="claude_code_enhanced", event_type="tool_result", status="error"} | json | line_format "{{.timestamp}} {{.tool_name}}: {{.error_type}}"
Tool Sequence Patterns
logql
# Most common transitions
{job="claude_code_enhanced", event_type="tool_call"} | json | previous_tool != "" | line_format "{{.previous_tool}} → {{.tool_name}}"
Session Anomalies
logql
# Long sessions
{job="claude_code_enhanced", event_type="session_end"} | json | duration_seconds > 3600

# High error sessions
{job="claude_code_enhanced", event_type="session_end"} | json | error_count > 5

# High turn sessions
{job="claude_code_enhanced", event_type="session_end"} | json | turn_count > 30
Context Patterns
logql
# Auto compactions
{job="claude_code_enhanced", event_type="context_compact", trigger="auto"} | json

# High utilization
{job="claude_code_enhanced", event_type="context_utilization"} | json | context_percentage > 80

Scripts

  • scripts/detect-failures.sh - Failure pattern detection
  • scripts/detect-anomalies.sh - Statistical anomaly detection
  • scripts/detect-trends.sh - Trend analysis
  • scripts/detect-sequences.sh - Tool sequence analysis
  • scripts/generate-pattern-report.sh - Full pattern report

© majiayu000, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/analysis/observability-pattern-detector-adaptationio-skrillz-2 of majiayu000/claude-skill-registry.

  • SKILL.md
  • metadata.json

Open the folder on GitHubat commit 000116a

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in majiayu000/claude-skill-registry, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Observability Pattern Detector next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Observability Pattern Detector compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Observability Pattern Detector this skillmajiayu000/claude-skill-registry6661 repos~1.6kAutomated safety check: PassMIT
Agent Health Monitoringcosmicstack-labs/mercury-agent-skills476—~2.7kAutomated safety check: PassMIT
AWS Cloudformation Cloudwatchgiuseppe-trisciuoglio/developer-kit355—~3.7kAutomated safety check: NotesMIT
Vercel Optimize Auditvercel-labs/agent-skills32k8 repos~4.3kAutomated safety check: PassNone
Kubeshark Installerkubeshark/kubeshark12k—~3.6kAutomated safety check: NotesApache-2.0
Kubeshark KFL2 Filter Referencekubeshark/kubeshark12k—~3.6kAutomated safety check: PassApache-2.0

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Categories

Questions about Observability Pattern Detector

What does Observability Pattern Detector do?

Automated pattern recognition in Claude Code telemetry. An agent skill from majiayu000/claude-skill-registry. Observability Pattern Detector is an agent skill from majiayu000/claude-skill-registry. Automated pattern recognition in Claude Code telemetry.

When should I use Observability Pattern Detector?

Observability Pattern Detector fits situations like: detecting failures; conversation patterns.

How do I install Observability Pattern Detector in Claude Code?

Run `npx skills add majiayu000/claude-skill-registry --skill observability-pattern-detector -a claude-code`. Or copy the skill folder (skills/analysis/observability-pattern-detector-adaptationio-skrillz-2 in majiayu000/claude-skill-registry) into .claude/skills/observability-pattern-detector in your project. Claude Code loads it when a task matches its description.

How do I install Observability Pattern Detector in Codex?

Run `npx skills add majiayu000/claude-skill-registry --skill observability-pattern-detector -a codex`. Or copy the skill folder (skills/analysis/observability-pattern-detector-adaptationio-skrillz-2 in majiayu000/claude-skill-registry) into .agents/skills/observability-pattern-detector in your project. Codex loads it when a task matches its description.

Can I use Observability Pattern Detector in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add majiayu000/claude-skill-registry --skill observability-pattern-detector -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/observability-pattern-detector, .gemini/skills/observability-pattern-detector, .github/skills/observability-pattern-detector and .opencode/skills/observability-pattern-detector in your project.

What does Observability Pattern Detector need to run?

SKILL.md names no scripts, command-line tools or credentials: Observability Pattern Detector is instructions for the agent only.

Does Observability Pattern Detector access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Observability Pattern Detector safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Observability Pattern Detector use?

Observability Pattern Detector is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Observability Pattern Detector use?

About 1.6k tokens (SKILL.md is roughly 6.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Observability Pattern Detector?

Skills that share tags, products or a category with Observability Pattern Detector: Agent Health Monitoring (cosmicstack-labs/mercury-agent-skills, 476 stars), AWS Cloudformation Cloudwatch (giuseppe-trisciuoglio/developer-kit, 355 stars), Vercel Optimize Audit (vercel-labs/agent-skills, 32k stars) and Kubeshark Installer (kubeshark/kubeshark, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Observability Pattern Detector?

majiayu000 (a GitHub user) maintains it in majiayu000/claude-skill-registry, which has 666 GitHub stars. The repository holds 971 skills in this directory. The repository was last updated on October 7, 2026.

Source: majiayu000/claude-skill-registry on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.