Borrowing it
Nothing to install: this file belongs to israel-salgado/dt-mcp-server. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/israel-salgado/dt-mcp-server/main/.agents/skills/dt-obs-logs/SKILL.mdgit clone --depth 1 https://github.com/israel-salgado/dt-mcp-serverWrote 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/israel-salgado/dt-mcp-server/dt-obs-logs)<a href="https://agentmods.dev/skills/israel-salgado/dt-mcp-server/dt-obs-logs"><img src="https://agentmods.dev/badge/skills/israel-salgado/dt-mcp-server/dt-obs-logs/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/israel-salgado/dt-mcp-server/dt-obs-logs"><img src="https://agentmods.dev/badge/skills/israel-salgado/dt-mcp-server/dt-obs-logs.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 | $0.00026 | $0.01965 |
| Opus 5 | $0.00013 | $0.00983 |
| Sonnet 5 | $0.00005 | $0.00393 |
| Haiku 4.5 | $0.00003 | $0.00197 |
Grade A, and why
dt-obs-logs 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 11d 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.
This is a copy
88% identical to dt-obs-logs — 29 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Log Analysis Skill
Query, filter, and analyze Dynatrace log data using DQL for troubleshooting and monitoring.
What This Skill Covers
- Fetching and filtering logs by severity, content, and entity
- Searching log messages using pattern matching
- Calculating error rates and statistics
- Analyzing log patterns and trends
- Grouping and aggregating log data by dimensions
When to Use This Skill
Use this skill when users want to:
- Find specific log entries (e.g., "show me error logs from the last hour")
- Filter logs by severity, process group, or content
- Search logs for specific keywords or phrases
- Calculate error rates or log statistics
- Identify common error messages or patterns
- Analyze log trends over time
- Troubleshoot issues using log data
Key Concepts
Log Data Model
- timestamp: When the log entry was created
- content: The log message text
- status: Log level (ERROR, FATAL, WARN, INFO, etc.)
- dt.process_group.id: Associated process group entity
- dt.process_group.detected_name: Resolves process group IDs to human-readable names
Query Patterns
- fetch logs: Primary command for log data access
- Time ranges: Use
from:now() - <duration>for time windows - Filtering: Apply severity, content, and entity filters
- Aggregation: Group and summarize log data
- Pattern Detection: Use
matchesPhrase()andcontains()for content search
Common Operations
- Severity filtering (single or multiple levels)
- Content search (simple and full-text)
- Entity-based filtering (process groups)
- Time-series analysis (bucketing, sorting)
- Error rate calculation
- Pattern analysis (exceptions, timeouts, etc.)
Core Workflows
1. Log Searching
Find specific log entries by time, severity, and content.
Typical steps:
- Define time range
- Filter by severity (optional)
- Search content for keywords
- Select relevant fields
- Sort and limit results
Example:
fetch logs, from:now() - 1h
| filter status == "ERROR"
| fields timestamp, content, process_group = dt.process_group.detected_name
| sort timestamp desc
| limit 100
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.
- 11d ago First seen · 249 lines · 26 tokens per session scan A f4aedb58600f
dt-obs-logs is a skill published in the GitHub repository israel-salgado/dt-mcp-server (2 stars, last pushed 4mo ago), licensed MIT. It adds 26 tokens to every session and 1,965 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to dt-obs-logs, differing in 29 lines, and is treated as a copy.
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