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Support deflection miner

Skill ClarentCinematics/Codex-Skills-for-Enterprise/skills/support-deflection-miner

Analyze support tickets, helpdesk exports, issue subjects, customer questions, or support thread summaries to find repeated themes, duplicate-looking requests, documentation gaps, automation candidates, and support-deflection opportunities. Use when Codex needs to convert support noise into prioritized KB, product, or workflow improvements without inventing customer facts.From its SKILL.md

Install
npx -y skills add ClarentCinematics/Codex-Skills-for-Enterprise --skill support-deflection-miner

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
  • runs commandsInstructs the agent to run 1 command, including `scripts/mine_support_themes.py --input <path>`.

SKILL.md

2.3 KB, 396 tokens by cl100k_base, as published. Nobody here has run it

Support Deflection Miner

Workflow

  1. Identify source type, date range, product area, customer segment, and support goal.
  2. Group repeated questions, symptoms, ticket subjects, and workaround requests.
  3. Separate documentation gaps, product friction, policy confusion, and automation opportunities.
  4. Prioritize by repetition, customer impact, confidence, and actionability.
  5. Produce proposed KB articles, product feedback, macros, automation candidates, and caveats.

Script-Assisted Workflow

When given ticket subjects in CSV or text form, run scripts/mine_support_themes.py --input <path> first. Use --json when structured theme counts are needed. The helper surfaces repeated text patterns; Codex must still judge whether a theme is a KB gap, product issue, or workflow candidate.

Output Standard

Use this structure by default:

  • Deflection Summary: source scope, top repeated issues, and confidence.
  • Top Themes: theme, evidence, count, likely category, and caveats.
  • Duplicate-Looking Requests: repeated subjects or near-repeated issue language.
  • KB Opportunities: proposed article titles, audience, source evidence, and missing facts.
  • Product / Process Signals: friction points requiring product, policy, or workflow review.
  • Automation Candidates: macro, bot, form, routing, or script opportunities.
  • Questions To Resolve: missing context needed before publishing or automating.

Rules

  • Do not invent customer names, ticket counts beyond the provided input, root causes, SLA impact, or product commitments.
  • Mark keyword clusters as heuristic, not definitive taxonomy.
  • Keep customer-sensitive data out of examples unless already sanitized.
  • Prefer small, reviewable deflection actions over broad automation claims.

References

Read references/deflection-rubric.md when deciding whether a repeated support theme should become a KB article, product bug, macro, or automation candidate.

What ships with it: 3 files

5.3 KB alongside SKILL.md, 1 of them executable

agents/

references/

scripts/

Keep looking

Skills are one crate of 621,086. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.