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 agentmods add skills/anpy-j/graylog-mcp/code-runtime-diagnosenpx skills add anpy-j/graylog-mcp --skill code-runtime-diagnosegit clone --depth 1 https://github.com/anpy-j/graylog-mcpWhat 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 | $0.00082 | $0.00719 |
| Opus 5 | $0.00041 | $0.00360 |
| Sonnet 5 | $0.00016 | $0.00144 |
| Haiku 4.5 | $0.00008 | $0.00072 |
Grade A, and why
code-runtime-diagnose 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code runtime diagnosis
Connect static code paths to runtime evidence through the Graylog Agent Toolkit MCP. Keep code inference separate from observed log facts.
Workflow
- Confirm the environment, symptom time, timezone, business identifier, and requested scope. Begin read-only unless the user also requests a fix.
- Locate the entrypoint and follow the relevant branches, async submissions, callbacks, consumers, and persistence boundaries.
- Extract stable literal fragments from relevant log statements. Prefer text such as
收到回调,callback handle success, or a structuredstage=value over class names or variable-only text. - Collect known identifiers such as
studentExamId,studentId,taskId,requestNo,traceId,requestId,itemKey,ossKey, andbizType. - If a matching business profile exists, read it with
graylog_get_profile. Profiles add vocabulary, entrypoints, stages, and correlation keys; they do not replace code inspection. - Call
graylog_diagnose_code_logswith the literals, identifiers, profile, and the narrowest useful time range. If exact absolute times are unavailable, use a short relative range and state it. - Follow identifiers discovered by the first result across async boundaries. Use
graylog_search_messagesonly for a targeted raw Lucene query that the correlation tool cannot express. - Map important events back to source files and branch conditions. Identify the first missing, failed, skipped, or contradictory stage.
- Report the timeline and the smallest next verification step. Do not edit code, rerun a job, or change data unless the user authorized it.
Query rules
- Use UTC RFC3339 timestamps for MCP absolute ranges; translate user-local time explicitly.
- Search stable literals and exact identifiers together when that narrows results without hiding alternate branches.
- Start with at most a few strong terms. Expand through returned correlation values instead of using a broad wildcard query.
- Treat no results as an observation, not proof that code did not execute. Check environment, time range, timezone, stream, ingestion, and literal accuracy.
- Never place Graylog credentials, session IDs, cookies, authorization headers, or signed URL secrets in prompts or reports.
What ships with it
2 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.
- yesterday First seen · 62 lines · 82 tokens per session scan A b44a85d9bc23
code-runtime-diagnose is a skill published in the GitHub repository anpy-j/graylog-mcp (0 stars, last pushed 1mo ago), licensed MIT. It adds 82 tokens to every session and 719 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 2026-08-31.
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