diagnose

A structured method for finding the cause of software problems such as build errors, failed tests, crashes or deployment issues.

In plain words
What is it for?
Use it to investigate unexpected behavior, trace errors, check related code and form a tested explanation of what went wrong.
Why use it?
It replaces guesswork with evidence from error messages, logs, code and recent changes, making the underlying cause easier to identify.

Skill for Claude CodeCodex

Part of the dev-workflow plugin — 14 skills, 4 agents, 2 hooks, 1 MCP server shipped together

Install

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.

agentmods
npx agentmods add skills/makigjuro/cloudstack-ai-plugins/diagnose
Any agent
npx skills add makigjuro/cloudstack-ai-plugins --skill diagnose
Clone the repo
git clone --depth 1 https://github.com/makigjuro/cloudstack-ai-plugins

Made for: Claude Code, Codex.

Or install dev-workflow, the plugin that ships this one along with the rest of its 14 skills, 4 agents, 2 hooks, 1 MCP server.

Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,471 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash c01c310b36fe, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

plugins/dev-workflow/skills/diagnose/SKILL.md · 269 lines

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:

Read the full file on GitHub · 269 lines

Changes

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.

  1. 2d ago First seen · 269 lines · 44 tokens per session scan A c01c310b36fe

Subscribe to this mod's changes

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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