How to choose the first AI agent skill for your team

01 / Decision

Choose the problem before the skill

Use popularity and agent support as filters, not as the decision. Set the scoring rule before reviewing candidates: no disqualifier and at least 8 of 10 points across problem fit, source inspectability, required access, reproducibility, and team setup.

02 / Workflow

A six-step selection test

Compare no more than three candidates for one real task. Reject unsafe or uninspectable options first, score the remainder, and hand only the winner to a teammate who did not select it.

1. 01

   Name one repeated problem

   Pick a task that already recurs, costs attention, and has a recognizable output. Write down the current approach and the failure you want the skill to prevent. Avoid broad goals such as ‘improve engineering’ that cannot be tested in one sitting.

   Output: One sentence naming the trigger, current friction, and expected result.

2. 02

   Build a shortlist of three or fewer

   Find candidates through trusted sources, catalogs, or teammate suggestions, then stop at three. Record each canonical source and claimed use case. A short list forces a real comparison and prevents popularity from becoming the default decision.

   Output: A bounded candidate list tied to the same problem and expected result.

3. 03

   Apply the disqualifier gate

   Read SKILL.md and every referenced script, template, example, and external tool. Reject a candidate when the source cannot be inspected, required access exceeds the task, data handling is unacceptable, or the instructions conceal a dependency the team cannot authorize.

   Output: A pass or reject decision with the exact disqualifier, if any.

4. 04

   Score the surviving candidates

   Give each candidate 0, 1, or 2 points for problem fit, source inspectability, least required access, reproducibility, and fit with the team’s actual setup. Use the rule set before comparison: a candidate needs at least 8 of 10 and no disqualifier.

   Output: A comparable score with one sentence of evidence for every dimension.

5. 05

   Run the winner on one fixture

   Use an input that resembles the team’s real work and define the acceptable output before running the skill. Record the exact commit or tag reviewed, the review date, the agent, and the setup. Skills Board surfaces the latest upstream files, so an upstream change requires another review.

   Output: A before-and-after example with an observable pass or fail result and reviewed source state.

6. 06

   Save and test the team handoff

   Save the winner in Skills Board with the visible source, score, review date, truthful recommendation, and search tag. Invite a second teammate and ask them to choose the source, compatible install command, or ZIP path for their setup. Keep the recommendation only if they can find it and reproduce the expected result.

   Output: One searchable recommendation plus an independent keep, revise, or reject result.

03 / Record

First-skill selection scorecard

Score evidence, not enthusiasm. A candidate is ready to recommend only when the source is inspectable, the fixture passes, and another teammate can reproduce the path.

Repeated problem

The task trigger, current friction, frequency, and expected output.

Candidate source

The canonical URL, reviewed commit or tag, review date, and exact instructions, scripts, permissions, and data paths inspected.

Disqualifier gate

Inspectable source, acceptable access and data handling, authorized dependencies, and no concealed setup requirement.

Selection score

0 to 2 points each for problem fit, inspectability, least access, reproducibility, and team setup, with an 8 of 10 threshold.

Fixture and handoff

The test input and result, invited teammate, access path chosen, and whether they reproduced it without private context.

Decision

Keep, revise, or reject, with an owner and the event that should trigger another review.

# First AI agent skill scorecard

## Repeated problem
- Trigger: [when this task starts]
- Current friction: [time, inconsistency, or failure]
- Expected output: [observable result]
- Frequency: [how often the team does it]

## Candidate source
- Skill: [name]
- Canonical source: [URL]
- Reviewed commit or tag: [source state]
- Review date: [date]
- Instructions and supporting files reviewed: [yes/no plus notes]
- Scripts, permissions, and data paths reviewed: [yes/no plus notes]

## Disqualifier gate
- Complete source is inspectable: [yes/no]
- Required access fits the task: [yes/no]
- Data handling is acceptable: [yes/no]
- External dependencies are authorized and visible: [yes/no]
- Result: [pass/reject plus evidence]

## Selection score
- Problem fit: [0/1/2 plus evidence]
- Source inspectability: [0/1/2 plus evidence]
- Least required access: [0/1/2 plus evidence]
- Reproducibility: [0/1/2 plus evidence]
- Team setup fit: [0/1/2 plus evidence]
- Total: [0-10; threshold is 8 with no disqualifier]

## Representative fixture
- Test input: [realistic, non-sensitive example]
- Acceptable result: [pass criteria]
- Agent and setup tested: [observed path only]
- Result: [pass/fail plus evidence]

## Team recommendation
- Why we recommend it: [specific reason]
- What remains untested: [limits]
- Upstream note: Skills Board surfaces the latest source files; re-review after changes.
- Search tag: [term a teammate will use]
- Owner: [person or team]

## Independent handoff
- Teammate invited: [name or role]
- Path chosen: [source, compatible command, or ZIP]
- Could they find and run it without private context? [yes/no]
- Second result: [pass/fail plus evidence]

## Decision
[Keep / revise / reject]
Review again when: [source, agent, permissions, or workflow changes]

04 / Pitfalls

What weakens the first choice

05 / Checklist

Ready to share with the team?

• The candidate solves one repeated problem with an observable expected result.
• The shortlist contains no more than three candidates for the same expected result.
• The complete source passed every disqualifier and the candidate scored at least 8 of 10.
• The test recorded a representative fixture, reviewed source state, date, and actual agent path.
• The Skills Board note explains the score, recommendation, limits, and upstream re-review trigger.
• A second teammate found the recommendation and reproduced it, or the candidate was revised or rejected.