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investigate-first

Investigate unclear failures, intermittent behavior, and performance regressions before changing code. Separate symptoms from assumptions, trace how failures arise, and rank possible causes by evidence and ease of testing, then report the cause and supporting proof.

OriginalShow the original description

Diagnose ambiguous failures before editing. Use for unknown causes, intermittent behavior, performance regressions, or investigations needing evidence-ranked hypotheses.

AI rating90/ 100See how to install

Good for

  • Investigating a failure with an unknown cause
  • Diagnosing intermittent behavior
  • Finding the cause of a performance regression
  • Comparing possible causes against evidence

Overview

What it does

Investigate failures with unclear causes by gathering evidence before changing code. The skill separates observed symptoms from inferred causes, then traces inputs, state changes, boundaries of responsibility, and failure output. Possible explanations are ranked by the strength of their evidence and how easily they can be disproved with little effort. Product code remains unedited until a credible mechanism explains the evidence.

When it helps

Use it for failures with unknown triggering conditions, problems that happen only occasionally, or processing that has become slower than before. It also helps when several causes seem possible and you want to organize the evidence before moving ahead with a fix based on an assumption. The investigation compares hypotheses in an order guided by evidence and the effort needed to test them.

Example requests

  • “Investigate this intermittent error and report the symptoms separately from hypotheses about its cause.”
  • “Investigate the performance regression, testing the hypotheses with supporting evidence first.”

Notes

Investigation stops when enough evidence identifies the cause or a specific obstacle blocking further investigation. The report states the cause and supporting evidence. A fix is implemented only when the request authorizes it.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Objective evaluation

AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.

Clear instructionsIs it clear when to use it and what it does?
AGoodUnknown causes, intermittent behavior, and performance regressions are explicit triggers for gathering evidence before editing.
Concrete stepsAre steps, inputs, outputs, and examples spelled out?
BPartly lackingIt identifies what to trace and how to rank hypotheses, but provides no concrete investigation examples or report template.
SafetyAre sending data, deleting files, and running commands disclosed?
AGoodProduct code must remain unedited until evidence supports a mechanism, and implementing a fix requires task authorization.
Easy setupDoes it need few tools or API keys, and are they documented?
AGoodThe instructions describe evidence gathering and reporting, with no specific tools or credentials specified.

The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 20, 2026. Treat it as a guide and read the original before installing.

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Files included (2)

  • SKILL.md688 B
  • agents/openai.yaml202 B

SKILL.md (original)

Review the instructions the agent will receive before you install.

Investigate first

Gather evidence before changing product code.

  • Separate observed symptom from inferred cause.
  • Trace inputs, state transitions, ownership boundaries, and failure output.
  • Rank hypotheses by evidence and cheap falsification value.
  • Do not edit until one credible mechanism explains evidence.
  • Stop exploration when evidence is sufficient to name cause or exact blocker.

Report cause and proof. Make no fix unless task authorizes implementation.

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