Analyze — Evidence-Driven Investigation

Use this skill for ambiguous, causal, evidence-heavy questions where the goal is to explain why an observed result happened, not to jump directly into fixing or rewriting code.

Good entry cases

Use $analyze when the problem is:

• ambiguous or causal
• evidence-heavy
• best answered by exploring competing explanations
• requires reading multiple files and reasoning across them

Examples:

• runtime bugs and regressions
• performance / latency / resource behavior
• architecture / premortem / postmortem analysis
• config / routing / orchestration behavior explanation
• dependency analysis or impact assessment
• "given this output, trace back the likely causes"

Do not use when

• User wants code changes made — use $ralph or executor instead
• User wants a full plan with acceptance criteria — use $plan instead
• User wants a quick file lookup — use explore agent instead
• User asks a simple factual question answerable from one file — just read and answer

Core investigation contract

Always preserve these distinctions:

1. Observation — what was actually observed
2. Hypotheses — competing explanations
3. Evidence For — what supports each explanation
4. Evidence Against / Gaps — what contradicts it or is still missing
5. Current Best Explanation — the leading explanation right now
6. Critical Unknown — the missing fact keeping the top explanations apart
7. Discriminating Probe — the highest-value next step to collapse uncertainty

Do not collapse into:

• a generic fix-it coding loop
• a generic debugger summary
• a raw dump of output
• fake certainty when evidence is incomplete

Evidence strength hierarchy

Treat evidence as ranked, not flat. From strongest to weakest:

1. Controlled reproductions / direct experiments / uniquely discriminating artifacts
2. Primary source artifacts with tight provenance (trace events, logs, metrics, configs, git history, file:line behavior)
3. Multiple independent sources converging on the same explanation
4. Single-source code-path or behavioral inference
5. Weak circumstantial clues (timing, naming, stack order, resemblance to prior bugs)
6. Intuition / analogy / speculation

Explicitly down-rank hypotheses that depend mostly on lower tiers when stronger contradictory evidence exists.

Strong falsification rules

Every serious investigation must try to falsify its own favorite explanation.

For each top hypothesis:

• collect evidence for it
• collect evidence against it
• state what distinctive prediction it makes
• state what observation would be hard to reconcile with it
• identify the cheapest probe that would discriminate it from the next-best alternative

Down-rank a hypothesis when:

• direct evidence contradicts it
• it survives only by adding new unverified assumptions
• it makes no distinctive prediction compared with rivals
• a stronger alternative explains the same facts with fewer assumptions

Team-mode orchestration (when using $team)

For complex investigations, use $team to run parallel tracer lanes:

1. Restate the observed result or "why" question precisely
2. Generate multiple deliberately different candidate hypotheses
3. Spawn 3 tracer lanes via $team
4. Assign one lane per hypothesis
5. Each lane gathers evidence for and against its hypothesis
6. Run a rebuttal round between the leading hypothesis and the strongest alternative
7. Merge findings into a ranked synthesis

Default hypothesis lanes

Unless the problem strongly suggests a better partition:

1. Code-path / implementation cause
2. Config / environment / orchestration cause
3. Measurement / artifact / assumption mismatch cause

Worker contract

Each worker must:

• own exactly one hypothesis lane
• gather evidence for and against the lane
• rank evidence strength
• call out missing evidence and failed predictions
• name the critical unknown for the lane
• recommend the best discriminating probe
• avoid collapsing into implementation

Cross-check lenses

After the initial evidence pass, pressure-test with these lenses when relevant:

• Systems lens — queues, retries, backpressure, feedback loops, upstream/downstream dependencies
• Premortem lens — assume the current best explanation is wrong; what failure mode would embarrass the trace later?
• Science lens — controls, confounders, measurement bias, falsifiable predictions

Execution policy

• Default to concise, evidence-dense progress and completion reporting unless the user or risk level requires more detail
• Treat newer user task updates as local overrides for the active workflow branch while preserving earlier non-conflicting constraints
• If correctness depends on additional inspection, retrieval, execution, or verification, keep using the relevant tools until the analysis is grounded
• Continue through clear, low-risk, reversible next steps automatically; ask only when the next step is materially branching, destructive, or preference-dependent

Good: The user says continue after the workflow already has a clear next step. Continue the current branch of work instead of restarting or re-asking the same question.

Good: The user changes only the output shape or downstream delivery step. Preserve earlier non-conflicting workflow constraints and apply the update locally.

Execution steps

1. Identify the analysis type: Architecture, bug investigation, performance, or dependency analysis
2. Gather relevant context: Read or identify the key files involved
3. Generate hypotheses: At least 2-3 competing explanations
4. Route to analyzer:
   • For simple cases: investigate directly with file reads and reasoning
   • For complex cases: use $team with tracer lanes
   • Use ask_codex with agent_role: "architect" when available
5. Falsify: Try to break your own best hypothesis
6. Return structured findings: Present with evidence, file references, and actionable recommendations

Output format

Observed Result

[What happened]

Ranked Hypotheses

Evidence Summary by Hypothesis

• Hypothesis 1: ...
• Hypothesis 2: ...

Evidence Against / Missing Evidence

• Hypothesis 1: ...
• Hypothesis 2: ...

Most Likely Explanation

[Current best explanation with file:line references]

Critical Unknown

[Single missing fact keeping uncertainty open]

Recommended Next Step

[Single best action — either a discriminating probe or a fix recommendation]

Quality bar

Good analysis output is:

• evidence-backed with file:line references
• concise but rigorous
• skeptical of premature certainty
• explicit about missing evidence
• practical about the next action
• explicit about why weaker explanations were down-ranked

Task: {{ARGUMENTS}}