agentsclimarketplace

Aipom use case triage

Skill deanpeters/ai-product-operating-model-skills/skills/aipom-use-case-triage

Evidence-based skills for designing and improving AI product operating models

Install
npx -y skills add deanpeters/ai-product-operating-model-skills --skill aipom-use-case-triage

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

3 things to look at

  • 21 days oldThe repository was created 21 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 3 stars3 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.

What its author says it does

Copied from the file, not written here

Compare AI opportunities across outcome value, evidence, feasibility, responsibility, readiness, and reversibility to recommend explore, validate, defer, or reject.

SKILL.md

6.0 KB, 950 tokens by cl100k_base, as published. Nobody here has run it

AIPOM Use-Case Triage

What Is It

An adaptive comparison method for deciding which AI opportunities deserve exploration, validation, deferral, or rejection. It preserves evidence quality and critical constraints instead of disguising judgment in a single weighted score.

Why Use It

Idea funnels reward confident storytelling and false precision. Triage makes alternatives comparable while preventing strong value claims from averaging away unsafe behavior, unavailable data, or missing ownership.

When to Use It

Use for intake, portfolio shaping, innovation funds, or before creating detailed bet charters. Do not use it to decide scale; later-stage evidence belongs in investment gates.

What It Produces

  • Comparable opportunity records and confidence notes
  • Explore, validate, defer, or reject recommendation per use case
  • Non-negotiable constraints and portfolio dependencies
  • Next test, owner, decision rule, and review date

Who Should Participate

Include the portfolio decision owner, Product Operations, product and technology representatives, finance, and governance partners. Invite opportunity owners to clarify evidence, not set their own criteria.

Evidence to Bring

Bring opportunity frames, outcome maps, alternatives, user evidence, feasibility signals, data and context readiness, evaluation needs, governance consequences, adoption constraints, costs, and reversibility.

How to Do It

  1. Extract supplied context and normalize each opportunity to a comparable scope. For a single proposal, compare it with the current workflow, a non-AI alternative, or the decision to do nothing.
  2. Confirm the portfolio decision, constraints, capacity, and non-negotiables.
  3. Examine outcome value, evidence strength, AI fit, feasibility, responsibility, readiness, and reversibility.
  4. Record missing perspectives, disagreements, and evidence confidence.
  5. Apply critical constraints before comparing relative attractiveness.
  6. Present posture options and recommend a default with reasons.
  7. Define the smallest evidence-producing action for opportunities that proceed.
  8. Record the owner, decision rule, exceptions, and next review.

Triage determines the next investment posture; it does not establish launch or scale readiness. Route a selected opportunity to a bet charter, investment gate, or initiative-readiness review appropriate to the next decision.

Facilitation Protocol

Support guided, context-dump, and best-guess modes. In guided mode ask first which decision and capacity constraint govern the comparison. In context-dump mode extract existing answers before asking gaps. In best-guess mode label assumptions and avoid turning incomplete information into numeric certainty.

Decision Logic

  1. Explore: meaningful condition, credible AI fit, high uncertainty, bounded consequence, and a cheap learning path.
  2. Validate: promising evidence but material behavior, value, feasibility, adoption, or responsibility questions remain.
  3. Defer: attractive opportunity lacks a prerequisite, owner, capacity, or decision window.
  4. Reject: weak condition or AI fit, unacceptable consequence, unavailable critical input, or a clearly superior alternative.

Use dimensions as structured judgment, not a compensating arithmetic score. No value score overrides a non-negotiable safety, legal, privacy, security, or accountability gap.

Completion Criteria

Finish with the evidence used, assumptions, alternatives, posture for every use case, critical constraints, disagreements, owners, next tests, and review decisions. If a named owner or review date is unavailable, identify the required role, mark assignment unresolved, and use an evidence-completion trigger rather than inventing details.

Key Concepts

  • Comparable does not mean falsely precise.
  • Evidence quality changes confidence, not just rank.
  • Reversibility determines how cheaply uncertainty can be explored.
  • A prerequisite gap may justify defer rather than reject.

Organizational Applications

Use for product intake, innovation councils, annual planning, vendor proposals, and reducing duplicate or strategically incoherent pilots.

Common Pitfalls

  • Scoring proposals before framing them consistently
  • Letting presenters define favorable criteria
  • Averaging away critical constraints
  • Confusing a demo with feasibility evidence
  • Ranking opportunities without capacity or next decisions
  • Treating defer as a polite yes

Combine With

Use aipom-bet-charter for selected opportunities, aipom-investment-stage-gates for continuation decisions, and aipom-portfolio-posture-advisor for portfolio balance.

Assets and Templates

Sources

This advisor is an original AIPOM synthesis of evidence-based opportunity, portfolio, and responsible-investment practice.

What ships with it: 3 files

6.5 KB alongside SKILL.md

examples/

Gives 0 of the 12 instructions most debug triage skills give in 950 tokens

Counted across 839 of the 1,149 authors here whose files we hold, read 2026-08-07

  • Investigate root cause before proposing any fixin 102 of 839, across 67 files
  • Read error messages completelyin 89 of 839, across 49 files
  • Create a failing test case before fixingin 84 of 839, across 46 files
  • Reproduce the issue consistentlyin 82 of 839, across 41 files
  • Change one variable at a timein 82 of 839, across 42 files
  • Check recent changesin 74 of 839, across 36 files
  • Write the regression test before fixingin 74 of 839, across 40 files
  • Fix the root cause not the symptomin 60 of 839, across 45 files
  • Implement a single fix at a timein 59 of 839, across 20 files
  • Trace data flow backward to the sourcein 50 of 839, across 20 files
  • Remove all debug instrumentationin 49 of 839, across 13 files
  • Form a single hypothesisin 48 of 839, across 18 files

Said here and by no other author read

  • normalize opportunities to comparable scope
  • confirm constraints and non-negotiables
  • examine value evidence feasibility and reversibility
  • record missing perspectives and confidence
  • apply critical constraints before comparing attractiveness
  • recommend a posture with reasons

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 328,083. 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.