agentleFS
JsnnmscJsnnmsc/10000x-engineer/skills/clear-view/SKILL.md

clear-view

Skill2 starsChanged 2 months agoMITView on GitHub

Explain the current situation as one short plain-language paragraph — what we thought, what turned out to be true, where that leaves us — then the one decision to make.

The file

We split this skill into the rules it gives an agent

8 rules. About 0 are common, 8 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈8

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Prose, not structure: No bullets, no section headers, no labels.

    line 27
  2. Distinctive:

    Plain spoken language: Write it the way you'd say it out loud.

    line 28
  3. Distinctive:

    No diagrams, no analogies, no history of how we got here beyond what the situation needs.

    line 34
  4. Distinctive:

    Don't write "I changed 3 files and passed two acceptance gates" — that's your progress, not their situation.

    line 57
  5. Distinctive:

    Don't slip back into structure.

    line 60
  6. Distinctive:

    Don't write for a reader who already knows.

    line 63
  7. Distinctive:

    Don't end on a menu of options.

    line 66
  8. Distinctive:

    If no request was supplied, brief on the current session's situation.

    line 68

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Clear View

Goal: user reads one paragraph and understands the situation the way you understand it — then knows the one thing they have to decide.

This is not a summary of your work — it's building the user's mental model. They didn't watch you run those tools; they don't have your context. Tell them what's going on the way you'd explain it to a colleague who just walked in: plainly, in order, with the part that actually matters up front.

Rules

  • Respond in the same language the user wrote in. If they write in Chinese, reply in Chinese. Technical terms (JWT, Redis, API, eval) stay in English.
  • Max 8 lines. Hard limit — one paragraph plus the decision line.
  • Prose, not structure. No bullets, no section headers, no labels. If you're reaching for a bullet, the sentence wasn't finished.
  • Plain spoken language. Write it the way you'd say it out loud. Short sentences. No noun stacks, no "additionally", no report voice.
  • Narrative order: what we believed → what's actually true → what that means now. The context is the point; a bare fact list is what this skill exists to avoid.
  • Conclusions, not process. "I ran X and found Y" → cut to "Y".
  • Numbers come in pairs. "34 cases hit the classifier" is useless alone — "34 of 4972" drives a decision, a lone number doesn't.
  • Say "we don't know yet" out loud for anything uncertain — don't paper over it with vague phrasing.
  • The last line is bold and holds the decision: one recommendation, or one binary question. Never a menu of three.
  • No diagrams, no analogies, no history of how we got here beyond what the situation needs.
  • No emoji.

Output format

[One paragraph, 3-5 sentences. What was assumed, what's actually true, where that leaves things. Plain spoken language.]

**[One line: the recommendation, or the single choice the user makes now.]**

Example

/clear-view

我們一直在看的 eval 數字量錯了東西。這三週的準確率報告看起來是跑了 4972 筆案例,但實際上只有 34 筆真的進到 classifier,其他都在 cache 那層就直接回傳了。所以「準確率下降」不是模型變差,是 cache 命中率變高、真正被評分的樣本被壓到只剩一點點——我們追了兩天的 regression 其實不存在,模型本身從頭到尾沒被動過。至於關掉 cache 之後真實準確率是多少,現在還不知道。

**先把 cache 關掉重跑一次才有意義——你要跑完整 4972 筆,還是先抽 500 筆確認?**

Anti-examples

Don't write "I changed 3 files and passed two acceptance gates" — that's your progress, not their situation.

Don't slip back into structure. A paragraph with "First, … Second, … Third, …" is a bullet list wearing a coat.

Don't write for a reader who already knows. "The cache short-circuit invalidated the scoring path" assumes the thing you're supposed to be explaining.

Don't end on a menu of options. More than two means you haven't converged for the user yet.

If no request was supplied, brief on the current session's situation.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.