investigate

name	investigate
description	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.
allowed-tools	Read, Glob, Grep, Agent

Project Context

• CLAUDE.md: !find . -maxdepth 1 -name "CLAUDE.md" -type f
• project-discovery.md: !find . -maxdepth 3 -name "project-discovery.md" -type f

Investigation Approach

• Trace backward from symptoms — don't guess, follow the code.
• Launch parallel han-core:evidence-based-investigator agents for different angles simultaneously — one for the error path, one for the data flow, one for recent changes.
• Add one or more specialist analysts in parallel with the investigators when the bug type calls for it (concurrency, data flow across boundaries, database or query behavior). Specialist analysts find root causes generalists miss.
• The han-core:adversarial-validator agent handles all three validation strategies (challenge evidence, challenge fix, challenge assumptions) internally.
• Apply the evidence rule from ../../references/evidence-rule.md to every finding. Codebase findings (file path, line number, log line, test output) carry the trust-class label "codebase" and stand on their citation. Web-source context (RFCs, vendor docs, Stack Overflow, blog posts) carries the trust-class label "web" and is subject to the corroboration gate when it drives the proposed fix. When the investigation hits a point where no evidence at any tier resolves a question, label the no-evidence state rather than guessing.
• Lazy-create the output sections. Include a section in the plan file only when the investigation produced meaningful content for it; omit any section that would be empty, and keep the sections that remain in the template's order. Never emit a heading with placeholder or "N/A" content.
• Load and apply the readability rule at ../../references/readability-rule.md as you write the findings, holding the named audience: the engineer who will implement the fix and may be paged on the bug. Scope that frame per section so the technical specifics the engineer needs (function names, exact failing conditions, file:line citations) are preserved, never simplified away.

Investigate

Step 1: Research and Investigation

Always dispatch

Launch at least 2 han-core:evidence-based-investigator agents in parallel, each investigating from a different angle — for example, one tracing the error path and another following the data flow.

Conditional specialist dispatch

Classify the bug from the user's symptom description before launching. Skip any specialist that does not apply. Dispatch every applicable specialist in parallel with the han-core:evidence-based-investigator agents in the same message.

1. Launch han-core:concurrency-analyst — when the symptom involves intermittent failures, race conditions, deadlocks, ordering issues, stale reads after writes, timeouts, dropped messages, or anything that only reproduces under load or concurrent users. Prompt: "Investigate the concurrency and async behavior of the code paths implicated by this symptom: {symptom}. Focus on race conditions, lock ordering, shared-resource contention, async error handling, and missing cancellation/timeout handling. Return numbered findings keyed to file paths and line numbers."

2. Launch han-core:behavioral-analyst — when the symptom involves data transformed wrong, values lost between modules, errors swallowed, state mutated unexpectedly, or integration boundaries passing bad data. Prompt: "Trace the data flow for the code paths implicated by this symptom: {symptom}. Focus on data transformation across module boundaries, error propagation and loss, state mutation, and integration-boundary assumptions. Return numbered findings keyed to file paths and line numbers."

3. Launch han-core:data-engineer — when the symptom involves wrong data in the database, slow queries, N+1, lock contention, migration failures, unbounded scans, lost data, broken referential integrity, or isolation-level surprises. Prompt: "Investigate the schema, queries, migrations, and data-access code implicated by this symptom: {symptom}. Focus on the specific data-engineering principles violated and the concrete data-level impact. Return numbered findings keyed to file paths, line numbers, and schema or migration references."

After all agents complete (investigators and specialists), compile an evidence summary — a numbered list of concrete findings (E1, E2, E3, ...) that will feed into the root cause analysis. Specialist findings go into the same E-series list, tagged with the specialist's domain (e.g., E3 (concurrency)).

Step 2: Document Root Cause

Write to the plan file using the template at template.md. Fill the sections in the workflow order below; this is deliberately not the template's on-page order, which leads with the Summary and places the supporting Evidence Summary, Validation Results, and Coding Standards Reference near the end for the reader. Fill in these sections:

1. Problem Statement — document the symptoms, expected behavior, conditions under which it occurs, and impact.
2. Evidence Summary — consolidate evidence from all agents into a unified numbered list (E1, E2, E3, ...); merge duplicates and resolve conflicting findings while preserving each item's output structure.
3. Root Cause Analysis — write a one-to-three sentence summary of the root cause, then a detailed analysis referencing evidence items by number (e.g., "The handler passes an unvalidated ID (E1) to the service layer, which assumes non-nil (E3)").

Step 3: Plan the Fix

Resolve project config: read CLAUDE.md's ## Project Discovery section for docs, ADR, and coding-standards directories; fall back to project-discovery.md; fall back to Glob defaults (docs/, docs/adr/, docs/coding-standards/). Search found directories for relevant standards, ADRs, and docs. Also check CLAUDE.md, AGENTS.md, and linter/formatter configs for coding standards. If none found, infer conventions from surrounding code.

Design a fix that directly addresses the root cause from Step 2 — fix the underlying problem, not symptoms. Then fill in the remaining sections of template.md in the plan file:

1. Coding Standards Reference — for each standard, convention, ADR, or pattern inferred from surrounding code that governs the fix, document what it is, where it was found (file path, ADR number, or "inferred from surrounding code"), and which files or changes it governs. If nothing governs the fix, omit the section per the lazy-create rule.
2. Planned Fix — write a one-sentence summary, then for each file that needs to change: full path from repo root, what will be modified/added/removed, which evidence items (E1, E2, ...) justify the change, which coding standards apply, and implementation specifics (new function signatures, changed logic, updated tests).

Step 4: Validation (CRITICAL)

Launch han-core:adversarial-validator agents and pass them the complete evidence summary (all E1-EN items with full code snippets), the root cause analysis, and the planned fix with all file changes. Do not summarize — the validator needs verbatim detail to challenge effectively. Their job is adversarial — they must actively try to disprove the findings and break the fix.

When counter-evidence is found, document it as a validation finding (V1, V2, ...), investigate whether it changes the root cause analysis, adjust the plan (evidence, root cause, and fix sections) as needed, and fill in the Adjustments Made section listing what changed and which validation finding triggered each change. When counter-evidence is not found, document what was checked and why it supports the original findings, recording it as a validation finding confirming the analysis.

After all validation is complete, incorporate the han-core:adversarial-validator agents' Confidence Assessment and Remaining Risks into the plan.

Step 5: Summary and User Review

Add the Summary section at the top of the plan file with one sentence each for: root cause (what caused the problem), fix (what the planned changes will do), why correct (reference the strongest evidence), validation outcome (what validation confirmed or changed), and remaining risks (reference the Confidence Assessment).

Once the write-up draft is complete, dispatch han-core:readability-editor (one Agent call) to audit and rewrite the findings against the readability rule. This is separate from the Step 4 adversarial-validator pass: that pass checks the fix is correct (accuracy); this pass checks how the write-up reads. Keep both. Pass the editor the plan file path, the rule path ../../references/readability-rule.md, and the named audience: the engineer who will implement the fix and may be paged on the bug. It must preserve every fact and operate on prose regions only — never inside code fences, function signatures in code blocks, diagram bodies, or file:line citation identifiers. Apply its rewrite to the plan file.

Then run the standardized readability self-check from ../../references/readability-rule.md over the write-up's prose regions only — never inside code fences, function signatures, diagram bodies, or file:line citation identifiers. Confirm each criterion and fix any failure before presenting:

1. The opening line states the main point (the root cause / the answer).
2. Each heading names its content and is not a generic label.
3. Each paragraph carries one idea and leads with it.
4. No sentence runs past the soft length flag (about thirty words) without reason.
5. No word from the vocabulary blocklist (the writing-voice profile's "Avoided words and phrases" and "AI slop to avoid" lists) is present.
6. Every fact is preserved — every claim, quantity, named entity, and stated condition or qualifier survives with its precision intact.

Fidelity wins: the standard governs how the content is said, never whether a required technical fact appears.

Present the plan file to the user for approval. The user can approve the plan (triggering implementation) or provide feedback for revisions.