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
npx -y skills add zhijunio/skills --skill topic-learnAssembled 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
| Required | Optional |
|---|---|
| Topic (one knowledge point) | mode: new (default) or refresh |
| Boundary + out of scope | Depth: 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
| Level | Deliverables |
|---|---|
brief | Mission + map + sources/INDEX + slim main article (problem, mechanism, sources); interview: one-liner + ≥3 follow-ups |
standard | Full 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.yaml → mode)
| Mode | When |
|---|---|
new | No workspace yet, or full first pass |
refresh | Merge, 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
- Read existing topic dir; set
modeinTOPIC.yaml(references/formats.md). - New workspace — optional bootstrap:
/path/to/topic-learn/scripts/topic_scaffold.sh /path/to/<topic-dir> <slug> [zh|en]
- 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」):
- After Scope — confirm Mission + boundary.
- 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_repofor 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.
| Need | Skill |
|---|---|
| Learning-topic polish in this workspace | This skill (Step 8) |
| Neutral de-AI only, no author persona | humanizer (user triggers separately) |
| Author voice + publish-ready in writing flow | article-write refine stage |
Related skills
| Skill | When |
|---|---|
article-read | Source step — fetch URL/PDF into sources/ (references/source-ingest.md) |
humanizer | Finished main/interview, neutral de-AI only |
article-write | Turn mastery assets into publish-oriented articles (user triggers separately) |
flowguard | Long 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
evals/
- topic-learn-evals.json2.1 KB
references/
- article-structure.md2.1 KB
- formats.md3.5 KB
- interviewization.md1.3 KB
- README.md595 B
- source-ingest.md1.7 KB
- workflow.md5.4 KB
scripts/
- topic_check.shruns4.4 KB
- topic_scaffold.shruns2.2 KB
templates/
- interview-output.md436 B
- mastery-map.md1.6 KB
- TOPIC.yaml126 B