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Topic learn

Skill zhijunio/skills/topic-learn

Learn one topic end to end (学): scoped mission, layered sources, mastery map, verified evidence, canonical article, interview layer, learning records. Modes new or refresh. Use for 主题学习, 技术深挖, 溯源验证; not single-link summaries, blank-page article writing, or codebase audit.From its SKILL.md

Install
npx -y skills add zhijunio/skills --skill topic-learn

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

4.2 KB, ~1.0k tokens by cl100k_base, as published. Nobody here has run it

Topic Learn

Role: 学 — Turn one knowledge point into a reusable, source-backed, verifiable topic asset.

Examples: Java reflection, TCP congestion control, Raft, HTTP caching, Postgres MVCC.

Not for: single-link summaries, opinion without sources, shallow cheat sheets, marketing-only article drafts, codebase audit.

Invocation

RequiredOptional
Topic (one knowledge point)mode: new (default) or refresh
Boundary + out of scopeDepth: brief / standard (default) / deep
Success looks like (≥2 outcomes)Save path, language zh / en
Save directory

Missing Success or boundary → ask at most 3 questions. Do not start Source until Mission is writable.

Depth

LevelDeliverables
briefMission + map + sources/INDEX + slim main article (problem, mechanism, sources); interview: one-liner + ≥3 follow-ups
standardFull checklist; glossary optional
deep+ glossary and/or reference/; 0–2 side articles; mechanism-heavy topics default to diagrams (article-structure.md)

Detail: references/formats.md → Depth.

Modes (TOPIC.yamlmode)

ModeWhen
newNo workspace yet, or full first pass
refreshMerge, gap-fill, re-verify, or humanize-only

Refresh variants (detail in references/workflow.md):

  • Merge — align scattered drafts; diff-only Write
  • Gap-fill — only missing pieces (e.g. interview)
  • Verify / polish — re-run demos, fix verified_on, humanize touched files

Before you start

  1. Read existing topic dir; set mode in TOPIC.yaml (references/formats.md).
  2. New workspace — optional bootstrap:
/path/to/topic-learn/scripts/topic_scaffold.sh /path/to/<topic-dir> <slug> [zh|en]
  1. 按需读 — start at references/README.md; never bulk-read all references.

Flow

Scope → Source → Model → Verify → Write → Interviewize → Record → Humanize → Checklist
  • new: full flow (references/workflow.md).
  • refresh: delta on each step; touch ≤6 files unless full rewrite.

Stop points (user may say「一次跑完」):

  1. After Scope — confirm Mission + boundary.
  2. After Model — confirm mastery-map + gaps before Write.

Hard rules

  • One topic per run.
  • No mastery map → no Write.
  • No L1/L2 for key claims → no Write (or explicit Open).
  • No verification → not complete.
  • No interview layer → not complete.
  • All prose artifacts in one topic directory (code_repo for runnable code when documented).

Humanize boundary

Polish main + interview when the user asks — preserve sources, verification commands, claims, and glossary terms. User style notes override default tone.

NeedSkill
Learning-topic polish in this workspaceThis skill (Step 8)
Neutral de-AI only, no author personahumanizer (user triggers separately)
Author voice + publish-ready in writing flowarticle-write refine stage

Related skills

SkillWhen
article-readSource step — fetch URL/PDF into sources/ (references/source-ingest.md)
humanizerFinished main/interview, neutral de-AI only
article-writeTurn mastery assets into publish-oriented articles (user triggers separately)
flowguardLong new/refresh spanning many sessions — wrap with checkpoints

Validation

bash -n topic-learn/scripts/topic_scaffold.sh
bash -n topic-learn/scripts/topic_check.sh
topic-learn/scripts/topic_scaffold.sh /tmp/topic-smoke test-slug zh
topic-learn/scripts/topic_check.sh /tmp/topic-smoke    # expect warnings on fresh scaffold

Before marking phase: shipped, run topic_check.sh on the topic directory.

References

Start at references/README.md. Scripts: topic_scaffold.sh, topic_check.sh.

What ships with it: 12 files

25.5 KB alongside SKILL.md, 2 of them executable

scripts/

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.