How do I install the Problem Solve skill?

Run npx skillmds@latest add teklabsdigital/problem-solve in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Problem Solve safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Problem Solve?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Problem Solve free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Problem Solve?

teklabsdigital (@teklabsdigital) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Problem Solve by teklabsdigital — Agent Skill

Name: Problem Solve
Author: teklabsdigital

Problem Solve

Use when the product misbehaves at runtime and the cause is unknown, especially when reproducing needs the human's device, eyes, or account. Do not use for a red test with an evident cause (stay in the implement loop) or for green-but-wrong against the locked prototype (that is an adjudication turn; the fix goes in the decision file).

File contents

X2 Problem Solve

Diagnosis, priced in human turns. Every "try this and tell me what you see" is a round trip the human pays for, and the metric counts it. The kernel prevents classes of defects; it does not diagnose novel ones, so when one appears, spend the fewest observation turns that honestly find the cause.

The pipeline

Observe. Get the observation and visual evidence first (screenshot, recording, exact repro). Find the last working state in git; the diff between working and broken is the search space.
Hypothesize. Form two to four falsifiable hypotheses, each with the data signature it predicts. Write the expected values down before instrumenting; a hypothesis without a predicted signature is a guess.
Validate with one pass. Design ONE instrumentation deployment that discriminates between all hypotheses simultaneously: five to ten log points, state transitions only, a filterable prefix, no per-render logging. Deploy instrumentation only, no other changes. One round trip. If the data says "working correctly" and the human says otherwise, the hypotheses are wrong, not the human's eyes; return to step 2.
Confirm. State the root cause in one sentence tied to measured data. If it does not fit in one sentence, it is not understood yet.
Fix. A failing regression test first where the defect is testable; then the fix through the normal implement loop; then remove every log point added in step 3. If the root cause is a specification defect, that is green-but-wrong: the fix goes in the decision file, never patched into source.

Anti-patterns

No code changes before a confirmed cause. No layered speculative fixes; if you stack three changes, you learn nothing. No trying the opposite of a failed fix without new data.

Human-turn contract

No gate. The observation round trips are the turns; log each one to the ledger like any other, and design the instrumentation so there is usually exactly one.

What this skill must NOT produce

No fixes without a measured root cause, no instrumentation left behind, no commits without direction.

---name: problem-solvedescription: Use when the product misbehaves at runtime and the cause is unknown, especially when reproducing needs the human's device, eyes, or account. Do not use for a red test with an evident cause (stay in the implement loop) or for green-but-wrong against the locked prototype (that is an adjudication turn; the fix goes in the decision file).---# X2 Problem SolveDiagnosis, priced in human turns. Every "try this and tell me what you see" is a round trip the humanpays for, and the metric counts it. The kernel prevents classes of defects; it does not diagnosenovel ones, so when one appears, spend the fewest observation turns that honestly find the cause.## The pipeline1. **Observe.** Get the observation and visual evidence first (screenshot, recording, exact repro). Find the last working state in git; the diff between working and broken is the search space.2. **Hypothesize.** Form two to four falsifiable hypotheses, each with the data signature it predicts. Write the expected values down before instrumenting; a hypothesis without a predicted signature is a guess.3. **Validate with one pass.** Design ONE instrumentation deployment that discriminates between all hypotheses simultaneously: five to ten log points, state transitions only, a filterable prefix, no per-render logging. Deploy instrumentation only, no other changes. One round trip. If the data says "working correctly" and the human says otherwise, the hypotheses are wrong, not the human's eyes; return to step 2.4. **Confirm.** State the root cause in one sentence tied to measured data. If it does not fit in one sentence, it is not understood yet.5. **Fix.** A failing regression test first where the defect is testable; then the fix through the normal implement loop; then remove every log point added in step 3. If the root cause is a specification defect, that is green-but-wrong: the fix goes in the decision file, never patched into source.## Anti-patterns- No code changes before a confirmed cause. No layered speculative fixes; if you stack three changes, you learn nothing. No trying the opposite of a failed fix without new data.## Human-turn contract- No gate. The observation round trips are the turns; log each one to the ledger like any other, and design the instrumentation so there is usually exactly one.## What this skill must NOT produce- No fixes without a measured root cause, no instrumentation left behind, no commits without direction.

Frequently asked questions