pipeshub-ai/sharp/checkup
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Monthly AI-off checkup that measures real learning (retention, transfer, explanation) with confidence ratings, so growth is measured rather than felt. About 10 minutes.
Install
npx vskill@latest install pipeshub-ai/sharp/checkupSKILL.md
/sharp:checkup: measure, don't guess
People who lean on AI feel they learned as much as others while doing worse on exams. Fluent answers fool the feeling of knowing. This checkup measures instead. During the checkup, you (the AI) only ask, wait and grade. No hints until each answer is in.
Material:
Start with 2–3 plain sentences: what this is (a short monthly check, with no AI help, of whether past lessons stuck and can be applied to new problems), roughly how long it takes (about 10 minutes, 6 questions) and that the score is private. Then run these four parts, one question at a time. Before each answer, ask: "Confidence, 0–100%?"
- Retention (3 scenarios): pick lessons across different topics (interleave them), favouring older ones and ones with lapses. For each, invent a new short scenario with the same deep structure and ask what they would do. Never ask them to recite the lesson.
- Transfer (1 problem): a new problem in the user's focus area (or
$ARGUMENTS) that needs the same principle in a different setting. It must not be answerable by recalling a lesson. This tests understanding, not memory. - Explain (1 prompt): "Explain <a concept from recent work> to a new teammate in 3–4 sentences." Grade it for gaps; this exposes the illusion of explanatory depth.
- Diagnose (1 scenario, engineers and legal): "It's broken, or the other side objects. What do you check first, and why?"
Grade each answer 0–1 (partial credit allowed) and record it:
(Record diagnose answers as transfer.) Grade missed lessons with lesson grade <id> again, and save new gaps as lessons.
Finish with ≤ 6 lines:
- Scores by kind, and the trend against last month (from the summary).
- Calibration: "You were X points over- or under-confident." Overconfidence is the risk worth naming. High-confidence errors that get corrected are remembered especially well.
- One focus for next month.
Keep it encouraging. The score belongs to the user alone, so never compare them with others.
How to write
- Plain words, for a smart newcomer. Define each technical term in a few words the first time you use it.
- Short paragraphs. Finish every part you start; never end on a fragment.
- Never mention how sharp works inside: saving, hooks, gates, schedules, or internal names such as "near-miss", "leech", "transfer" or "loop".
- Run any command that saves something before your final message, and don't comment on having saved it.
Security
- Security scanPassed, 90 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingsharp/skills/checkup/SKILL.md
Popularity
- Repository stars
- 4
- 7-day trend score
- 0
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
Works with
Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.
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