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/makigjuro/cloudstack-ai-plugins/diagnosenpx skills add makigjuro/cloudstack-ai-plugins --skill diagnosegit clone --depth 1 https://github.com/makigjuro/cloudstack-ai-pluginsWhat 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 | $0.00044 | $0.01471 |
| Opus 5 | $0.00022 | $0.00736 |
| Sonnet 5 | $0.00009 | $0.00294 |
| Haiku 4.5 | $0.00004 | $0.00147 |
Grade A, and why
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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnose
Systematically investigate a problem using evidence-based reasoning. This skill follows a structured investigation methodology to find root causes and propose solutions.
Arguments
{problem}-- Description of the problem to investigate (required)--quick-- Fast diagnosis, skip deep analysis--logs-- Include log analysis in investigation
Process
Phase 1: Problem Statement
Clarify the problem:
- What is the expected behavior?
- What is the actual behavior?
- When did it start? (commit, deployment, time)
- Is it reproducible? How?
- What's the impact? (severity, affected users)
Phase 2: Evidence Collection
Use an investigator approach to gather evidence systematically.
Source 1: Error Messages & Logs
# Recent git history
git log --oneline -20
# Search for related errors in codebase
grep -r "ERROR_CODE" src/
# If --logs flag, search log patterns
grep -rn "{error pattern}" logs/
Source 2: Code Analysis
# Find related code
grep -rn "{symptom keyword}" src/
# Check recent changes to affected area
git log -p --since="1 week ago" -- {affected paths}
# Find usages and dependencies
grep -rn "{function/class name}" src/
Source 3: Configuration
# Check appsettings
cat src/**/appsettings*.json | grep -i "{related config}"
# Check environment variables
grep -rn "GetEnvironmentVariable\|GetValue<" src/ | grep -i "{related}"
Source 4: Tests
# Find related tests
grep -rn "{feature}" tests/
# Check if tests are passing
dotnet test --filter "{test pattern}" --no-build
Source 5: External Context
- Search for similar issues in GitHub issues
- Search for related error messages online
- Check documentation for expected behavior
- Use context7 to look up library docs when the error involves a specific library -- this often reveals known issues or correct usage patterns faster than web search
Phase 3: Hypothesis Generation
Based on evidence, generate hypotheses ranked by likelihood:
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.
- 2d ago First seen · 269 lines · 44 tokens per session scan A c01c310b36fe
diagnose is a skill published in the GitHub repository makigjuro/cloudstack-ai-plugins (1 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 1,471 once invoked, about $0.0002 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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