Research Issue
SkillDev toolsInvestigate production issues and bugs through hypothesis-driven
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Research Issue skill
What this skill tells your AI
The instructions your AI receives, as published by atomicinnovation/accelerator in skills/research/research-issue/SKILL.md and read by ahel’s review.
!${CLAUDE_PLUGIN_ROOT}/bin/accelerator config context --skill research-issue --fail-safe
!${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, frequency patterns
- For intermittent/vague issues: specifically look for race conditions, state variance, non-deterministic code paths
Step 2: Map to code
- For structured input: resolve stacktrace frames to actual source files, check if referenced lines still match (code may have changed since the error)
- For vague input: identify code paths for the affected functionality
- Read the relevant source files FULLY (no limit/offset) to understand context
- Note any error handling, state management, or concurrency patterns
Step 3: Check recent changes
- Run
git log --oneline -20 -- <affected-files>on each affected file - Look for recent modifications that correlate with when the issue started
- Check if any recent refactoring touched the affected code paths
- Use
git diffon suspicious commits if needed
Step 4: Form hypotheses (2-3 theories)
- Based on the evidence gathered, formulate 2-3 plausible root causes
- Each hypothesis should be testable through code inspection
- Rank hypotheses by likelihood based on available evidence
- For vague/intermittent issues: always consider timing, ordering, and state as hypothesis categories
Step 5: Investigate in parallel
- Spawn sub-agent tasks to investigate each hypothesis concurrently
- Use the {codebase analyser agent} to trace specific code paths
- Use the {codebase pattern finder agent} to find similar patterns that might reveal the issue
- Use the {codebase locator agent} to find related components
- Each agent should look for evidence FOR and AGAINST its assigned hypothesis
- Collect specific file paths and line numbers as evidence
Step 6: Synthesise into RCA document and populate frontmatter
- Wait for ALL sub-agents to complete
- Evaluate each hypothesis: Confirmed, Eliminated, or Inconclusive
- Identify the root cause with specific code references
- Construct the causal chain from trigger to failure
- Propose fix options with risk/effort assessment
- Gather metadata using
${CLAUDE_PLUGIN_ROOT}/bin/accelerator corpus metadata deriveto obtainCurrent Date/Time (UTC):,Current Revision:, andRepository Name:. - Write the RCA document to the configured research directory using this template:
!${CLAUDE_PLUGIN_ROOT}/bin/accelerator config template rca --fail-safe
Before writing the artifact file, substitute every field below with the indicated value:
type:←issue-researchid:← the filename stem, always quoted as a YAML stringtitle:←Investigation: {Brief Issue Description}date:← theCurrent Date/Time (UTC):valueauthor:← the author resolved per the standard chain (config → VCS user → prompt)producer:←research-issuestatus:←completerevision:← theCurrent Revision:valuerepository:← theRepository Name:valuelast_updated:← the sameCurrent Date/Time (UTC):valuelast_updated_by:← the same value resolved forauthorschema_version:←1(bare integer)
Optional linkage/foreign-ref keys are omit-by-default: the
template shows each as ""/[], but write a key into the artifact
only when it has a value, and omit it entirely otherwise (do not
carry the empty placeholder through).
-
parent:← the work item this investigation supports, as a typed-linkage ref ("work-item:NNNN"). Fill when the RCA has an owning work item; otherwise omit the key. -
relates_to:← list of typed-linkage refs to related research (["issue-research:NNNN", ...]). Fill when related artifacts are explicit; otherwise omit the key. -
work_item_id:← the linked work item's full ID (quoted). Fill when the RCA is linked to a work item; otherwise omit the key. -
Filename format:
YYYY-MM-DD-description.mdwhere description is a brief kebab-case summary of the issue (e.g.,2025-01-08-auth-timeout-on-refresh.md)
Validate the frontmatter: after writing, run corpus frontmatter validate over the RCA document you just wrote:
${CLAUDE_PLUGIN_ROOT}/bin/accelerator corpus frontmatter validate --file <path>
If it exits non-zero, the RCA document violates the canonical frontmatter standard; report the emitted violation and fix the frontmatter before completing.
Step 7: Present findings (ONLY after the file has been written)
- Confirm the file path where the RCA document was saved
- Summarise the root cause concisely
- Highlight the recommended fix with rationale
- Include key file references for easy navigation
- Ask if they want deeper investigation on any aspect
Important notes:
- MANDATORY FILE OUTPUT: You MUST write the RCA document to a file in the configured research directory. NEVER present findings only as conversation output. The file write in step 6 is NOT optional — it is the primary deliverable of this skill. If you reach step 7 without having written a file, STOP and go back to step 6.
- Always use parallel Task agents to maximise efficiency
- Hypothesis-driven: generate theories THEN test them — don't just explore breadth-first
- For vague/intermittent issues: look for race conditions, state variance, non-deterministic paths, timing dependencies
- Evidence-based: every conclusion must reference specific code
- The RCA document should be self-contained and actionable
- Include the causal chain — not just "what" but "why" and "how"
- File reading: Always read affected files FULLY (no limit/offset)
- Critical ordering: Follow the numbered steps exactly
- ALWAYS classify input before investigating (step 1)
- ALWAYS check git history on affected files (step 3)
- ALWAYS form hypotheses before spawning agents (step 4)
- ALWAYS wait for all sub-agents before synthesising (step 6)
- ALWAYS write the RCA document to a file before presenting findings (step 6 before step 7)
- NEVER write the RCA document with placeholder values
- NEVER skip the file write — the document IS the output of this skill
!${CLAUDE_PLUGIN_ROOT}/bin/accelerator config instructions research-issue --fail-safe
Signals
- GitHub stars
- 31
- Forks
- 1
- Last commit
- Sep 2026
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research-issue- Source
- github.com/atomicinnovation/accelerator