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alexzhu0alexzhu0/agent-ready-skills/skills/support-ticket-to-eval/SKILL.md

support-ticket-to-eval

Skill0 starsChanged 5 months agoMITView on GitHub

Use when turning support tickets, user complaints, helpdesk transcripts, or failed assistant responses into reusable eval cases with expected behavior, assertions, and failure modes.

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:

    Use when support evidence should become a reusable regression or model-behavior eval.

    line 14
  2. Distinctive:

    Do not use when the ticket only needs a one-off human reply and no future check is useful.

    line 15
  3. Distinctive:

    Write assertions that can be checked by a human or automated judge.

    line 28

≈ “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

Support Ticket To Eval

Purpose

Turn real support pain into repeatable eval cases for product, support, or agent behavior.

Fit

  • Use when support evidence should become a reusable regression or model-behavior eval.
  • Do not use when the ticket only needs a one-off human reply and no future check is useful.

Inputs

  • Support ticket, complaint, chat transcript, or failed assistant output.
  • Product area, user role, and known expected behavior if available.
  • Any sensitive details that must be redacted.

Workflow

  1. Redact private data while preserving the failure pattern.
  2. Identify the user goal, current behavior, expected behavior, and severity.
  3. Convert the case into a minimal input that reproduces the behavior.
  4. Write assertions that can be checked by a human or automated judge.
  5. Record variants that should be tested later.

Output

Produce Markdown with:

  • Eval Scenario
  • Source Evidence
  • Test Input
  • Expected Behavior
  • Failure Mode
  • Assertions
  • Variants

Validation

  • The eval preserves the original user pain without exposing secrets.
  • Assertions are concrete enough to judge.
  • Expected behavior is not invented when product intent is unknown.
  • Severity is justified by evidence.
  • The case can be reused without the full ticket context.

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