research-issue

Solid

Investigate production issues and bugs through hypothesis-driven debugging. Accepts stacktraces, logs, error messages, or vague behavioral descriptions and produces a root cause analysis.

AI & Automation 30 stars 1 forks Updated today MIT

Install

View on GitHub

Quality Score: 85/100

Stars 20%
50
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# Research Issue !`${CLAUDE_PLUGIN_ROOT}/bin/accelerator config context --skill research-issue --fail-safe` !`${CLAUDE_PLUGIN_ROOT}/bin/accelerator config agents --fail-safe` If no "Agent Names" section appears above, use these defaults: accelerator:reviewer, accelerator:codebase-locator, accelerator:codebase-analyser, accelerator:codebase-pattern-finder, accelerator:documents-locator, accelerator:documents-analyser. **Research directory**: !`${CLAUDE_PLUGIN_ROOT}/bin/accelerator config path research_issues --fail-safe` You are tasked with investigating production issues and bugs through hypothesis-driven debugging. You accept structured input (stacktraces, logs, error messages) or vague behavioral descriptions and produce a root cause analysis document. ## Initial Setup: When this command is invoked, respond with: ``` I'm ready to investigate the issue. Please provide the stacktrace, error message, logs, or description of the behavior you're seeing, and I'll conduct a hypothesis-driven root cause analysis. ``` Then wait for the user's issue description. ## Steps to follow after receiving the issue description: ### Step 1: Extract and classify input - Determine input type: Structured (stacktrace/logs), Vague (behavioral description), or Mixed - For structured input: extract error messages, file paths, line numbers, timestamps, request IDs, affected services - For vague input: identify keywords, affected features, conditions under which the issue occurs, frequ...

Details

Author
atomicinnovation
Repository
atomicinnovation/accelerator
Created
5 months ago
Last Updated
today
Language
HTML
License
MIT

Bundled in these plugins

Similar Skills

Semantically similar based on skill content — not just same category

AI & Automation Solid

research-codebase

Conduct comprehensive codebase research by spawning parallel subagents and synthesising findings into a research document. Use when the user needs to deeply understand a codebase area or answer technical questions.

30 Updated today
atomicinnovation
Code & Development Solid

debug

Investigation-first debugging — gather evidence, form confirmed root-cause hypothesis, hand off to fix mode with diagnosis file. TRIGGER when: user reports a symptom or failing test with Python traceback, or asks to investigate a runtime/CI failure with reproducible evidence; phrases: "debug this failure", "why is X broken", "find the root cause of <error>", "investigate this CI failure". SKIP when: pure config quality issues (use `/foundry:audit`); broad system-wide diagnosis without traceback (use `/foundry:investigate`); user already knows the fix (use `/develop:fix`); non-Python project.

25 Updated today
Borda
Code & Development Featured

investigate

Evidence-based investigation of issues, bugs, API calls, integrations, and other aspects of software development that need a deep dive to find the root cause and solutions. Use when you need to debug, troubleshoot, diagnose, or figure out why something is broken. Does not review code for quality or style — use code-review for auditing changes or post-code-review-to-pr for posting review feedback to GitHub. Does not assess architectural health or structural risk — use architectural-analysis for architectural concerns. Does not research open-ended options, prior art, or how something works when nothing is broken — use research for that. Does not capture feedback on Han's own skills — use han-feedback for that.

224 Updated today
testdouble