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Get Started Free →Explain whatever the user is pointing at right now in plain language: a pending question, a piece of code, an error, a command output, or an artifact like a plan or findings report. Use when the user asks to "explain this", "what am I being asked", "what's happening right now", "help me understand this", "what does this mean", "what does this error mean", "what is this code doing", or "what do these options mean".
.claude/skills/tobihagemann-explain-this/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -12% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 6% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 0% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 25% | 0% |
| case-16 | ✗→✓ | ▲ Improved | 14% | 0% |
Take whatever the user is pointing at and translate it into plain language. "This" is usually the most recent assistant output or something pasted as an argument: a multi-option prompt, a free-form question, a code block, an error, a command output, or an artifact like a plan, findings report, or diff.
Pick the subject in priority order:
Read whatever makes the explanation concrete, no more:
SKILL.md when a skill-specific question is being askedgit status and git diff --stat when the subject involves staged or unstaged changesThe reader is an experienced developer who may not be fluent in every tech stack or acronym but doesn't need concepts from first principles. Skip ELI5. Use plain language; strip skill-internal jargon, or define it inline when the jargon is what the user needs explained (for example, "P1 peer" becomes "priority 1 finding from the peer reviewer").
Go beyond "what this does" and surface the trade-offs: pros and cons per choice, benefits and costs, whether the thing reads as clean and elegant or redundant and overcomplicated. Be honest about red flags; be honest about solid work.
Shape the output to match the subject. Use the branch that applies; drop the rest.
Question with discrete options — restate the question plainly, then enumerate options using this template:
**What you're being asked:** <plain-language restatement>
### Your Options
**1. <label as shown>** — <concrete effect: files changed, next step, what is lost>
- **Pros:** <what you gain>
- **Cons:** <what it costs>
**2. <label>** — <concrete effect>
- **Pros:** <...>
- **Cons:** <...>
### When Each Fits
- Pick **<option>** if your goal is <goal>.
- Pick **<option>** if your goal is <other goal>.Apply these option-specific rules:
auth-v2 and updates the 3 import sites in src/."Free-form question — restate the question plainly, say what the answer will be used for (which file gets written, which step consumes it), and call out what makes a good answer versus a problematic one. End by noting the question is still pending.
Code, error, or command output — say what it does or means, likely cause or purpose, and what action is typically expected. Judge the design: is it clean and idiomatic, or are there smells like redundant guards, tangled control flow, or unnecessary abstraction?
Artifact (plan, findings, diff, PR) — summarize its contents, the decisions it represents, and what step comes next. Flag strengths and weaknesses: over-engineering, missing cases, clear wins.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 15,429 | 6,415 | -58% | 1 | 1 | 0% | 2,440 | 2,157 | -12% | 0 | 0 | — |
case-02 | pass→pass | 12,521 | 8,473 | -32% | 1 | 1 | 0% | 2,087 | 2,451 | +17% | 0 | 0 | — |
case-03 | pass→pass | 6,325 | 6,629 | +5% | 1 | 1 | 0% | 1,079 | 2,168 | +101% | 0 | 0 | — |
case-04 | fail→pass | 12,568 | 8,125 | -35% | 1 | 1 | 0% | 2,109 | 2,237 | +6% | 0 | 0 | — |
case-05 | fail→pass | 12,956 | 7,373 | -43% | 1 | 1 | 0% | 2,162 | 2,164 | +0% | 0 | 0 | — |
case-06 | pass→pass | 14,165 | 12,010 | -15% | 1 | 1 | 0% | 2,752 | 3,070 | +12% | 0 | 0 | — |
case-07 | pass→pass | 11,173 | 8,435 | -25% | 1 | 1 | 0% | 1,822 | 2,355 | +29% | 0 | 0 | — |
case-08 | pass→pass | 10,899 | 8,558 | -21% | 1 | 1 | 0% | 1,994 | 2,433 | +22% | 0 | 0 | — |
case-09 | pass→pass | 12,973 | 9,992 | -23% | 1 | 1 | 0% | 2,167 | 2,457 | +13% | 0 | 0 | — |
case-10 | pass→pass | 10,601 | 8,390 | -21% | 1 | 1 | 0% | 1,627 | 2,223 | +37% | 0 | 0 | — |
case-11 | pass→pass | 10,525 | 4,881 | -54% | 1 | 1 | 0% | 1,772 | 1,767 | -0% | 0 | 0 | — |
case-12 | fail→pass | 10,071 | 7,740 | -23% | 1 | 1 | 0% | 1,747 | 2,180 | +25% | 0 | 0 | — |
case-13 | pass→fail | 7,230 | 6,389 | -12% | 1 | 1 | 0% | 1,243 | 1,279 | +3% | 0 | 0 | — |
case-14 | pass→pass | 8,835 | 7,429 | -16% | 1 | 1 | 0% | 1,620 | 2,263 | +40% | 0 | 0 | — |
case-15 | fail→fail | 7,965 | 12,830 | +61% | 1 | 1 | 0% | 1,212 | 2,805 | +131% | 0 | 0 | — |
case-16 | fail→pass | 14,233 | 14,100 | -1% | 1 | 1 | 0% | 2,198 | 2,514 | +14% | 0 | 0 | — |
case-17 | pass→pass | 10,751 | 7,256 | -33% | 1 | 1 | 0% | 2,037 | 2,327 | +14% | 0 | 0 | — |
case-18 | pass→pass | 8,437 | 9,132 | +8% | 1 | 1 | 0% | 1,371 | 2,359 | +72% | 0 | 0 | — |
case-19 | pass→pass | 3,772 | 4,598 | +22% | 1 | 1 | 0% | 603 | 1,581 | +162% | 0 | 0 | — |
case-20 | pass→fail | 4,086 | 8,835 | +116% | 1 | 1 | 0% | 560 | 2,483 | +343% | 0 | 0 | — |
case-21 | pass→fail | 24,108 | 12,164 | -50% | 1 | 1 | 0% | 3,805 | 2,974 | -22% | 0 | 0 | — |
case-22 | pass→pass | 8,429 | 15,754 | +87% | 1 | 1 | 0% | 1,483 | 3,501 | +136% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 21 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +9 percentage points is the difference between those two pass rates over the 21 comparable cases. 3 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.