원클릭으로 Manus에서 모든 스킬 실행

tutorial-context-pack

스타472

포크36

업데이트2026년 4월 15일 16:26

Use when module planning and source coverage are done and the run needs writer-ready per-module packs. **Trigger**: tutorial context pack, module pack, writer pack, 教程上下文包, 模块写作包. **Use when**: `source-tutorial` 的 C2，已有 module plan + source coverage，需要组织成稳定写作输入。 **Skip if**: module/source coverage 还没完成。 **Network**: none. **Guardrail**: 只整理上下文，不直接写教程正文。

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

WILLOSCAR

WILLOSCAR/research-units-pipeline-skills

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

Tutorial Context Pack

Combines module structure, source coverage, and source snippets into one deterministic JSONL pack per module.

Inputs

outline/module_plan.yml
outline/source_coverage.jsonl
sources/provenance.jsonl

Output

outline/tutorial_context_packs.jsonl

Contract

Each pack must include:

module_id
title
objective
core_concepts
exercise_seed
source_snippets

Script boundary

scripts/run.py should:

read the current module plan and coverage file
select source snippets per module
write one stable pack per module

Keep snippet ranking and pack synthesis in shared tutorial tooling rather than in the thin wrapper script.

Acceptance

one pack per module
every pack has module_id and objective
every pack includes actual source-backed snippets

Non-goals

final tutorial prose
PDF/slides delivery

이 저장소의 다른 Skills

같은 저장소

agent-survey-corpus

WILLOSCAR/research-units-pipeline-skills

Download a small corpus of open-access arXiv survey/review PDFs about agentic systems and extract text for style learning. **Trigger**: agent survey corpus, ref corpus, download surveys, 学习综述写法, 下载 survey. **Use when**: you want to study how real agent surveys structure sections (6–8 H2), size subsections, and write evidence-backed comparisons. **Skip if**: you cannot download PDFs (no network) or you don't want local PDF files. **Network**: required. **Guardrail**: only download arXiv PDFs; store under `ref/` and keep large files out of git.

2026-05-30472

global-reviewer

WILLOSCAR/research-units-pipeline-skills

Global consistency review for survey drafts: terminology, cross-section coherence, and scope/citation hygiene. Writes `output/GLOBAL_REVIEW.md` and (optionally) applies safe edits to `output/DRAFT.md`. **Trigger**: global review, consistency check, coherence audit, 术语一致性, 全局回看, 章节呼应, 拷打 writer. **Use when**: Draft exists and you want a final evidence-first coherence pass before LaTeX/PDF. **Skip if**: You are still changing the outline/mapping/notes (do those first), or prose writing is not approved. **Network**: none. **Guardrail**: Do not invent facts or citations; do not add new citation keys; treat missing evidence as a failure signal.

2026-05-30472

literature-engineer

WILLOSCAR/research-units-pipeline-skills

Multi-route literature expansion + metadata normalization for evidence-first surveys. Produces a large candidate pool (`papers/papers_raw.jsonl`, target ≥1200) with stable IDs and provenance, ready for dedupe/rank + citation generation. **Trigger**: evidence collector, literature engineer, 文献扩充, 多路召回, snowballing, cited by, references, 元信息增强, provenance. **Use when**: 需要把候选文献扩充到 ≥1200 篇并补齐可追溯 meta（survey pipeline 的 Stage C1，写作前置 evidence）。 **Skip if**: 已经有高质量 `papers/papers_raw.jsonl`（≥1200 且每条都有稳定标识+来源记录）。 **Network**: 可离线（靠 imports）；雪崩/在线检索需要网络。 **Guardrail**: 不允许编造论文；每条记录必须带稳定标识（arXiv id / DOI / 可信 URL）和 provenance；不写 output/ prose。

2026-05-30472

pdf-text-extractor

WILLOSCAR/research-units-pipeline-skills

Download PDFs (when available) and extract plain text to support full-text evidence, writing `papers/fulltext_index.jsonl` and `papers/fulltext/*.txt`. **Trigger**: PDF download, fulltext, extract text, papers/pdfs, 全文抽取, 下载PDF. **Use when**: `queries.md` 设置 `evidence_mode: fulltext`（或你明确需要全文证据）并希望为 paper notes/claims 提供更强 evidence。 **Skip if**: `evidence_mode: abstract`（默认）；或你不希望进行下载/抽取（成本/权限/时间）。 **Network**: fulltext 下载通常需要网络（除非你手工提供 PDF 缓存在 `papers/pdfs/`）。 **Guardrail**: 缓存下载到 `papers/pdfs/`；默认不覆盖已有抽取文本（除非显式要求重抽）。

2026-05-30472

prose-writer

WILLOSCAR/research-units-pipeline-skills

Write `output/DRAFT.md` (or `output/SNAPSHOT.md`) from an approved outline and evidence packs, using only verified citation keys from `citations/ref.bib`. **Trigger**: write draft, prose writer, snapshot, survey writing, 写综述, 生成草稿, section-by-section drafting. **Use when**: structure is approved (`DECISIONS.md` has `Approve C2`) and evidence packs exist (`outline/subsection_briefs.jsonl`, `outline/evidence_drafts.jsonl`). **Skip if**: approvals are missing, or evidence packs are incomplete / scaffolded (missing-fields, TODO markers). **Network**: none. **Guardrail**: do not invent facts or citations; only cite keys present in `citations/ref.bib`; avoid pipeline-jargon leakage in final prose.

2026-05-30472

schema-normalizer

WILLOSCAR/research-units-pipeline-skills

Normalize cross-skill JSONL interfaces (ids + titles + citation key formats) so downstream skills do not rely on best-effort joins. **Trigger**: schema normalize, jsonl contract, interface drift, join drift, 字段不一致, schema 规范化. **Use when**: you have generated C2-C4 JSONL artifacts (outline/briefs/bindings/packs/anchors) and want deterministic, stable fields before self-loops/writing. **Skip if**: you are not using the survey pipelines, or the workspace already has a fresh PASS `output/SCHEMA_NORMALIZATION_REPORT.md` for the current artifacts. **Network**: none. **Guardrail**: NO PROSE; deterministic transforms only; do not invent evidence/claims; only fill missing ids/titles from `outline/outline.yml`.

2026-05-30472

name

tutorial-context-pack

description

Tutorial Context Pack

Combines module structure, source coverage, and source snippets into one deterministic JSONL pack per module.

Inputs

outline/module_plan.yml
outline/source_coverage.jsonl
sources/provenance.jsonl

Output

outline/tutorial_context_packs.jsonl

Contract

Each pack must include:

module_id
title
objective
core_concepts
exercise_seed
source_snippets

Script boundary

scripts/run.py should:

read the current module plan and coverage file
select source snippets per module
write one stable pack per module

Keep snippet ranking and pack synthesis in shared tutorial tooling rather than in the thin wrapper script.

Acceptance

one pack per module
every pack has module_id and objective
every pack includes actual source-backed snippets

Non-goals

final tutorial prose
PDF/slides delivery