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Starlight-IntelligenceStarlight-Intelligence/starlight-agent-skills/skills/coding/starlight-investigate/SKILL.md

starlight-investigate

Skill1 starsChanged 4 months agoMITView on GitHub

Diagnose a reproducible software failure by separating observations from hypotheses, locating the failing boundary, and defining regression evidence. Use when a bug, failed check, production incident, or unexpected behavior needs a root-cause report; modify code only when the user also authorizes a fix.

1 other project uses a version of this.

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The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Run the cheapest discriminating checks first.

    line 31
  2. Distinctive:

    If a fix is authorized, apply the smallest causal change and run focused plus regression checks.

    line 33
  3. Distinctive:

    Prefer repository search, focused tests, logs, and read-only platform inspection.

    line 50

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Starlight Investigate

Purpose

Find the smallest evidence-supported explanation for a failure without speculative edits or unrelated cleanup.

When it fires

  • A test, build, deployment, API, UI flow, or runtime behavior fails.
  • The failure is intermittent and needs boundary isolation.
  • The user asks for diagnosis, root cause, or incident analysis.

Inputs

  • Expected and observed behavior.
  • Reproduction steps, logs, timestamps, environment, and relevant revision.
  • Repository instructions and known constraints.

Workflow

  1. Capture the exact failure and determine whether it reproduces.
  2. Reduce it to the smallest safe reproduction while preserving the failing condition.
  3. Trace inputs, state transitions, and outputs across the suspected boundary.
  4. List competing hypotheses and the observation that would falsify each one.
  5. Run the cheapest discriminating checks first.
  6. State the root cause only when evidence rules out plausible alternatives.
  7. If a fix is authorized, apply the smallest causal change and run focused plus regression checks.
  8. Record residual uncertainty, monitoring, and rollback needs.

Output contract

Return:

  • symptom and reproducibility;
  • evidence with commands, locations, and relevant revisions;
  • hypotheses_tested and falsification results;
  • root_cause or not_yet_proven;
  • impact_boundary;
  • recommended_fix without implementation when diagnosis-only;
  • verification and residual_risk.

Tools & MCP

Prefer repository search, focused tests, logs, and read-only platform inspection. Redact credentials and personal data. Do not alter production state to make a diagnosis easier.

Quality bar

  • Observation, inference, and decision are clearly separated.
  • “Could be” is not presented as root cause.
  • The reproduction and verification are copyable.
  • The proposed fix addresses the causal boundary rather than masking the symptom.
  • Unrelated working-tree changes remain untouched.

Example

Input: “The preview works, but the production API returns 415.”

Good output: evidence showing the exact content-type mismatch at the request boundary, alternatives ruled out, a minimal header-validation fix, and focused plus production-preview checks.


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