Agent skill

Clear Tech Explainer

by yuema137 in yuema137/DongbeiGPT

Explain technical concepts in Chinese through concrete causal structure, before/after mechanisms, boundaries, and trade-offs.

MITAuto-check passed

Install Clear Tech Explainer

skills CLI
$ npx skills add yuema137/DongbeiGPT --skill clear-tech-explainer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install yuema137/DongbeiGPT clear-tech-explainer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/yuema137/DongbeiGPT.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/clear-tech-explainer .claude/skills/clear-tech-explainer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
clear-tech-explainer
GitHub stars
100
Token cost
~581 tokens
SKILL.md length
291 words
Files
3 (incl. references)
Skills in repo
5
Repo updated
First seen
Licence
MIT

At a glance

Explain technical concepts in Chinese through concrete causal structure, before/after mechanisms, boundaries, and trade-offs.

  • Works in 8 steps: What job does it do? → What happened before it existed or was… → Where did the old approach fail? → …
  • Rewriting dense technical prose
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Do not add regional voice unless requested

What it does

Clear Tech Explainer is an agent skill from yuema137/DongbeiGPT. Explain technical concepts in Chinese through concrete causal structure, before/after mechanisms, boundaries, and trade-offs. Use for explanation, teaching, onboarding, or rewriting dense technical prose; do not add regional voice unless requested.

Its SKILL.md is about 580 tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/anti-patterns.md` and `references/explanation-patterns.md`).

The repository describes itself as: Explain tech and coding problems in clear, plan language. The licence is MIT.

When your agent uses it

  • Rewriting dense technical prose
  • Do not add regional voice unless requested

Example prompts

  • “/clear-tech-explainer”

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. What job does it do?
  2. What happened before it existed or was used?
  3. Where did the old approach fail?
  4. Which step did it add, remove, or change?
  5. How does one concrete item move through it?
  6. What is the easiest misconception?
  7. What does it solve, and what remains unsolved?
  8. What does it cost?

What it can do on your machine

Read from SKILL.md and the folder at commit 3f72262. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Clear Tech Explainer loads about 581 tokens when it runs, and up to ~988 if it reads all its reference files. Until then it costs about 67 tokens; SKILL.md has 291 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~67
When it runs · the whole SKILL.md, loaded when a task matches
~581
With references · SKILL.md plus every file in references/, read only if the agent opens them
~988

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from yuema137/DongbeiGPT at commit 3f72262, republished under its MIT licence (© yuema137). 291 words, ~581 tokens.

Download SKILL.mdSave it as .claude/skills/clear-tech-explainer/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
clear-tech-explainer
description
Explain technical concepts in Chinese through concrete causal structure, before/after mechanisms, boundaries, and trade-offs. Use for explanation, teaching, onboarding, or rewriting dense technical prose; do not add regional voice unless requested.
license
MIT

Clear Tech Explainer

Make the reader understand what changes and why. Technical correctness outranks ease or tone.

Before writing, determine the audience and requested depth. If neither is stated, assume a reader with general STEM background who can follow technical reasoning but may have no computer-science training and no familiarity with this project. Default to level 2.

Depth

  • Level 1: what it is, why it matters, and one boundary.
  • Level 2: mechanism step by step, with a small trace when useful.
  • Level 3: actual control/data flow, structures, code, or equations.
  • Level 4: intuitive entry plus complete formal distinctions and limitations.

Build only the relevant parts of this reasoning spine; do not print it as eight mandatory headings:

  1. What job does it do?
  2. What happened before it existed or was used?
  3. Where did the old approach fail?
  4. Which step did it add, remove, or change?
  5. How does one concrete item move through it?
  6. What is the easiest misconception?
  7. What does it solve, and what remains unsolved?
  8. What does it cost?

Prefer actors, verbs, causal chains, explicit before/after comparisons, and one main idea per sentence. Introduce a formal term when it becomes useful, explain it according to the audience, then reuse it exactly. Preserve code, equations, APIs, and standard English terminology.

STEM literacy does not imply software-engineering vocabulary or project familiarity. The default reader may not know watchdog, sidecar, runtime, composition projection, or Gate 2. Explain the concrete actor and responsibility on first meaningful use; do not treat internal labels as shared jargon.

End with a preservation check: compare the explanation to the source concept and restore any condition, distinction, or trade-off lost during simplification.

For recurring structures and failure modes, read references/explanation-patterns.md and references/anti-patterns.md.

© yuema137, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/clear-tech-explainer of yuema137/DongbeiGPT.

  • SKILL.md
  • references/anti-patterns.md
  • references/explanation-patterns.md

Open the folder on GitHubat commit 3f72262

Compare with similar skills

Clear Tech Explainer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Clear Tech Explainer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Clear Tech Explainer this skillyuema137/DongbeiGPT100—~581Automated safety check: PassMIT
Concept Explaineraipoch/medical-research-skills2k—~1.9kAutomated safety check: PassMIT
Plain-Language Concept Explainerlijigang/ljg-skills7.5k—~632Automated safety check: PassMIT
Tech Doc Style ChineseArcReel/ArcReel5.4k1 repos~845Automated safety check: PassMIT
Concept Explainerchmonitor/chmonitor299—~2.1kAutomated safety check: PassGPL-3.0
Explain Like Socratessickn33/agentic-awesome-skills47k2 repos~1.2kAutomated safety check: PassMIT

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More from yuema137/DongbeiGPT

  • Concrete Example

    yuema137/DongbeiGPT

    Add a small worked technical example, before/after trace, diagram, or structure-preserving analogy to an explanation.

    100 GitHub stars~329 tokensUpdated 1 mo ago
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  • Dongbei Explainer

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    100 GitHub stars~1.1k tokensUpdated 1 mo ago
    Auto-check passed
  • Dongbei Voice

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    Apply a restrained, widely understandable Northeastern-Chinese conversational rhythm to an existing Chinese explanation.

    100 GitHub stars~849 tokensUpdated 1 mo ago
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  • Plain Chinese

    yuema137/DongbeiGPT

    Rewrite or draft Chinese technical prose with fewer abstract noun piles, circular definitions, unexplained acronyms, and vague relationship claims.

    100 GitHub stars~633 tokensUpdated 1 mo ago
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Questions about Clear Tech Explainer

What does Clear Tech Explainer do?

Explain technical concepts in Chinese through concrete causal structure, before/after mechanisms, boundaries, and trade-offs. Clear Tech Explainer is an agent skill from yuema137/DongbeiGPT. Explain technical concepts in Chinese through concrete causal structure, before/after mechanisms, boundaries, and trade-offs.

When should I use Clear Tech Explainer?

Clear Tech Explainer fits situations like: rewriting dense technical prose; do not add regional voice unless requested.

How do I install Clear Tech Explainer in Claude Code?

Run `npx skills add yuema137/DongbeiGPT --skill clear-tech-explainer -a claude-code`. Or copy the skill folder (skills/clear-tech-explainer in yuema137/DongbeiGPT) into .claude/skills/clear-tech-explainer in your project. Claude Code loads it when a task matches its description.

How do I install Clear Tech Explainer in Codex?

Run `npx skills add yuema137/DongbeiGPT --skill clear-tech-explainer -a codex`. Or copy the skill folder (skills/clear-tech-explainer in yuema137/DongbeiGPT) into .agents/skills/clear-tech-explainer in your project. Codex loads it when a task matches its description.

Can I use Clear Tech Explainer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add yuema137/DongbeiGPT --skill clear-tech-explainer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/clear-tech-explainer, .gemini/skills/clear-tech-explainer, .github/skills/clear-tech-explainer and .opencode/skills/clear-tech-explainer in your project.

What does Clear Tech Explainer need to run?

SKILL.md names no scripts, command-line tools or credentials: Clear Tech Explainer is instructions for the agent only.

Does Clear Tech Explainer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Clear Tech Explainer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Clear Tech Explainer use?

Clear Tech Explainer is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Clear Tech Explainer use?

About 581 tokens (SKILL.md is roughly 2.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 407 tokens, read only when the agent opens those files.

What are the alternatives to Clear Tech Explainer?

Skills that share tags, products or a category with Clear Tech Explainer: Concept Explainer (aipoch/medical-research-skills, 2k stars), Plain-Language Concept Explainer (lijigang/ljg-skills, 7.5k stars), Tech Doc Style Chinese (ArcReel/ArcReel, 5.4k stars) and Concept Explainer (chmonitor/chmonitor, 299 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Clear Tech Explainer?

yuema137 (a GitHub user) maintains it in yuema137/DongbeiGPT, which has 100 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on September 1, 2026.

Source: yuema137/DongbeiGPT on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.