qbug

name	qbug
description	Hunt for the root cause of a bug — trace symptoms back to the underlying defect and report findings, without applying fixes (hunt-only, so you stay in control of the change). Use when investigating a failure, unexpected behavior, or a flaky test and you want the true cause rather than a surface patch.

Bug Hunter — Root Cause Analysis (Hunt-Only)

You are a high-recall bug hunter. Your job is to surface every plausible bug in a target piece of code and produce a structured findings list. You do not fix bugs in this skill. The pipeline is:

/qbug   →   /qbcheck   →   fix
(hunt)      (validate)    (in /qcode or by hand)

Stopping at "findings" is intentional. The hunt is deliberately aggressive (high recall), so the raw list contains false positives. /qbcheck filters them. Don't pre-filter here — flag generously and let validation do its job.

If the user explicitly asks you to also fix what you find, say: "I'll hunt now and produce findings; run /qbcheck next to validate before fixing. Want me to chain them?"

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Mode detection

Look at the arguments to the slash command:

• No target given (no args, or just whitespace) → Session-diff mode: hunt for bugs in code changed during this session.
• Target given (error message, test failure, file/feature reference, behavior description) → Targeted mode: hunt for the cause of the specific symptom described.

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Session-diff mode

Gather what changed:

git status -s
git diff
git diff --cached
git log --oneline -5

If there are no changes, say so and stop — nothing to hunt. Don't invent a target.

If there are changes, the diff content is your code context. Skip to Swarm below; the diff is what you pass to the agents.

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Targeted mode

Before launching the swarm, establish enough context to make the hunt worth running:

1. Confirm the target

Restate what you're hunting for in one sentence ("test X fails with Y", "production endpoint Z returns 500 sometimes", "Record totals are wrong intermittently"). If the description is too vague to act on, ask the user for:

• The exact error / failure (full message + stack trace beats a paraphrase)
• A reproduction path, or what the user was doing when they saw it
• Affected files, endpoints, or features if known

Don't launch a 5-agent swarm on a one-line description. The output is only as good as the target.

2. Try to reproduce / locate

Spend a few minutes establishing where the bug actually lives:

• Read the file(s) cited in the error.
• git log -p -- <files> to see recent changes that might be implicated.
• If a test reproduces it, run the test and read the actual failure (don't trust the user's paraphrase).
• Note what should happen vs what does. The gap is where the bug lives.

If you cannot locate any plausible suspect code, say so and ask for more context. Don't guess.

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Memory

MEMORY.md is already loaded by the auto-memory system. Skim what's there for relevant past bugs (same file, same pattern, same symptom) and apply them — many bugs are repeats. Don't re-read the index file.

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Swarm

The swarm uses 5 specialized subagents in parallel, each with a different attack angle. The design is high recall: each agent is told to flag aggressively because qbcheck will filter.

Anti-bias: randomize file order

When the target spans multiple files, give each agent the file list in a different order. Agents reading identical code in the same order tend to fixate on the same things and miss the same bugs.

Agent 1: [file_C, file_A, file_D, file_B]
Agent 2: [file_B, file_D, file_A, file_C]
...

For single-file targets, randomization is unnecessary — skip.

Per-agent attack angles

Each agent gets:

• A specific class of bug to hunt (don't say "find bugs" — too generic).
• The code context (diff for session mode, suspect files for targeted mode).
• The Missing / Wrong / Unclear lens — for each code element, ask: what's MISSING that should be there? what's WRONG? what's UNCLEAR enough to cause misuse?
• Mandatory codegraph step (#1): before emitting ANY finding for a function/method, the agent MUST consult callgraph context via mcp__codegraph__codegraph_callers, _callees, and _impact on the changed symbol. Findings without callgraph evidence (i.e. agent didn't verify how the symbol is actually used) → auto-downgrade confidence to C3 unless the bug is trivially obvious from the diff alone (typo, syntax error, off-by-one in literal). Per GitHub Copilot's Mar-2026 agentic rearchitecture and CodeRabbit: "the relationships that matter are imports, call sites, and type hierarchies, not textual similarity." If .codegraph/ is not present in the worktree, fall back to grep over imports + call sites and note (no codegraph) next to confidence.
• Required output format: file:line + claim + trigger scenario + confidence (C1–C3) + severity (S1–S3) + callgraph: <yes|no|partial>.

Agent	Attack angle
root-cause-tracer	Trace errors backward through call chains. For each function: what error handling is missing? what logic produces incorrect results under normal conditions? what's ambiguous enough to enable caller misuse?
silent-failure-hunter	Hidden error suppression — catch blocks that swallow, fallbacks that mask, empty handlers, API calls without error checks, log-and-continue patterns that lose context.
logic-error-detector	Logic + data-integrity bugs — off-by-one, inverted conditions, missing null checks, wrong comparisons, type coercion boundaries, Decimal vs float, NULL/empty/missing confusion, round-trip consistency.
edge-case-hunter	Boundary stress — null, empty, zero, negative, max-int, large inputs, rapid calls, non-default config, missing env vars, error-recovery state corruption.
race-condition-spotter	Concurrency + cross-feature — shared state, async timing, transaction isolation, missing locks, API contract breaks for callers, downstream consumers (search index, exports, webhooks). Note: GIL does not eliminate races (async, threadpools, multi-process, check-then-act).

For complex targets, optionally also run:

• data-flow-analyzer — trace data transformations through a path: where does data get corrupted, lost, or wrongly modified?
• dependency-checker — find every caller of the changed function: are they all updated? (#5: AUTO-PROMOTED to mandatory whenever any changed file in the diff lacks a co-located *test* / __tests__/ companion. Detection: git diff --name-only <BASE>..HEAD | while read f; do test -f "${f%.*}.test.${f##*.}" || dirname "$f" | xargs -I{} test -d "{}/__tests__" || echo "$f has no test"; done. Test-coverage-aware bug hunting reliably surfaces regressions diff-only swarms miss — see TDAD, arxiv 2603.17973.)
• security-scanner — OWASP top 10, injection, auth bypass.

Fan-out by complexity tier (#3)

/qbug fan-out is bounded by the qship §8.0 Complexity Tier (set in phase2-progress.md) when invoked from inside a qship pipeline. Outside qship (standalone targeted mode), default to T4.

Tier	Bug-hunter agents	Model mix
T1 Trivial	0 — qbug is skipped entirely; qbcheck still validates the diff	n/a
T2 Small	2 Sonnet — logic-error-detector + edge-case-hunter (drop Opus + 3 specialists)	2× Sonnet
T3 Medium	3 Sonnet — add data-flow-analyzer to T2	3× Sonnet
T4 Complex (default)	5 — current full swarm	1× Opus (root-cause-tracer) + 4× Sonnet

Critical/S1+C1 escalation (T2/T3 only): if any T2/T3 finding is marked S1 (severity 1) AND C1 (confidence 1), trigger ONE Opus pass with root-cause-tracer against just that finding's file before sending to qbcheck. This catches the "small PR with one nasty bug" case without paying Opus on every small PR. Per Mixture-of-Models routing research, 2026.

Orchestration

Use the Team mechanism if available (TeamCreate + Task + TaskList) so agents can collaborate and create follow-up tasks for each other when one finds something another should investigate. If team tools aren't available in this session, fall back to plain parallel Task calls — you lose follow-up chains but the core hunt still runs.

While the swarm runs:

• Watch incoming messages from teammates.
• If an agent surfaces something cross-cutting, route a follow-up task to the right specialist.
• Wait for convergence: all initial tasks done AND no unfinished follow-ups.

When done, shut down cleanly (delete the team in one call rather than messaging each agent individually). If the cleanup tools fail, note it and move on — orphan team state is not a hunt failure.

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Synthesis

Pull all findings into one list. For each finding, include:

• file:line and a code snippet (1–3 lines)
• Which agent flagged it (and any follow-up agents that confirmed it)
• The trigger scenario (concrete inputs / call path that would hit it)
• Confidence (C1–C3) and severity (S1–S3)
• Missing / Wrong / Unclear classification

Then surface, separately:

• Cross-agent discoveries — findings that emerged from one agent creating a follow-up for another. These often span dimensions a single agent would miss.
• Likely root cause hypothesis for targeted mode — the earliest point in the chain where something goes wrong, with reasoning. Mark it as a hypothesis, not a verdict, until validated.

Do NOT rank by "number of agents that flagged it." Vote-counting is a known false-positive trap (a single specialized scanner is often the only one that should catch a finding in its domain). /qbcheck reads the code itself; let it decide.

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Output format

## Bug Hunt Report — <target in one line>

> **Status:** Raw findings — not validated. Run `/qbcheck` before fixing anything.

### Target
[What you hunted for, plus mode (session-diff or targeted)]

### Evidence (targeted mode only)
- Error: …
- Manifest location: file:line
- Repro: …

### Root-cause hypothesis (targeted mode only)
- Earliest suspect: file:line
- Mechanism: how the failure propagates from there
- Violated assumption: …
- Confidence in the hypothesis: C1 / C2 / C3

### Findings

| # | Agent(s)                                      | file:line       | Class    | C  | S  | Claim |
|---|-----------------------------------------------|-----------------|----------|----|----|-------|
| 1 | root-cause-tracer                             | foo.py:42       | Wrong    | C2 | S2 | …     |
| 2 | silent-failure-hunter, edge-case-hunter       | bar.py:88       | Missing  | C3 | S3 | …     |
| 3 | logic-error-detector                          | baz.py:120      | Unclear  | C1 | S1 | …     |

**Legend.**
Confidence: C3 = reproduced; C2 = strong evidence; C1 = suspicious, unconfirmed.
Severity: S3 = critical (data loss / security / wrong money); S2 = moderate (wrong behavior); S1 = minor (edge case).
Class: Missing (should exist but doesn't), Wrong (exists but incorrect), Unclear (ambiguous, enables misuse).

### Cross-agent discoveries
[Findings that emerged from one agent's follow-up to another. These are often the most valuable. Empty if none.]

### Next step
Run `/qbcheck` on this list to filter false positives before fixing.

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Stop signals (don't ignore these)

• Target is too vague to hunt — ask for the exact error / repro / scope before launching the swarm.
• No code changes in session-diff mode — say so and stop.
• You can't read the suspect files — say so; don't invent findings.
• Findings are all C1 with no clear root cause — the hunt didn't converge. Tell the user the input was too broad; ask for narrower scope.
• Tempted to skip to fixing — don't. The pipeline exists because hunt-then-fix without validation is the #1 way to introduce new bugs while removing imaginary ones.

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Notes for the operator (Claude reading this)

• Lessons from this session worth keeping (root cause, prevention pattern, missed assumption) → run /qmemory after the user has validated and fixed. Don't write to memory from here; that's /qmemory's job.
• The framing "let qbcheck filter" is permission to be aggressive, not permission to be sloppy. Each finding still needs a specific file:line, a trigger scenario, and a claim that a reader could falsify by reading the code.

ARGUMENTS: $ARGUMENTS