원클릭으로 Manus에서 모든 스킬 실행

source-tutorial-spec

스타472

포크36

업데이트2026년 4월 15일 16:26

Use when a `source-tutorial` workspace has ingested sources and needs a grounded tutorial contract before structure planning. **Trigger**: source tutorial spec, tutorial from sources, learner profile, 教程规格, 从资料生成教程. **Use when**: `source-tutorial` 的 C2，需要根据 `sources/index.jsonl` / `sources/provenance.jsonl` 锁定 audience、prerequisites、learning objectives、source scope 和 running example policy。 **Skip if**: source ingest 还没完成，或 tutorial scope 已被人工冻结。 **Network**: none. **Guardrail**: 不要发明 sources 没支持的内容；running example 不稳时要明确写无统一 running example。

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

Source Tutorial Spec

Builds the C2 tutorial contract from the ingested source corpus, not from a bare topic prompt.

Inputs

sources/index.jsonl
sources/provenance.jsonl
GOAL.md
DECISIONS.md

Output

output/TUTORIAL_SPEC.md

Contract

The spec must include:

## Audience
## Prerequisites
## Learning objectives
## Non-goals
## Source scope
## Running example policy
## Delivery shape

It should also embed machine-readable structured data for downstream deterministic planning.

Script boundary

scripts/run.py should:

read the ingested source bundle
derive a grounded tutorial scope
write the markdown spec and structured seed block

Keep phrase extraction, concept selection, and source matching in shared tutorial tooling, not in the thin skill wrapper.

Acceptance

output/TUTORIAL_SPEC.md exists
the required sections are present
source families are named explicitly
running example policy is concrete or explicitly omitted

Non-goals

concept DAG construction
module sequencing
tutorial prose writing

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

source-tutorial-spec

description

Source Tutorial Spec

Builds the C2 tutorial contract from the ingested source corpus, not from a bare topic prompt.

Inputs

sources/index.jsonl
sources/provenance.jsonl
GOAL.md
DECISIONS.md

Output

output/TUTORIAL_SPEC.md

Contract

The spec must include:

## Audience
## Prerequisites
## Learning objectives
## Non-goals
## Source scope
## Running example policy
## Delivery shape

It should also embed machine-readable structured data for downstream deterministic planning.

Script boundary

scripts/run.py should:

read the ingested source bundle
derive a grounded tutorial scope
write the markdown spec and structured seed block

Keep phrase extraction, concept selection, and source matching in shared tutorial tooling, not in the thin skill wrapper.

Acceptance

output/TUTORIAL_SPEC.md exists
the required sections are present
source families are named explicitly
running example policy is concrete or explicitly omitted

Non-goals

concept DAG construction
module sequencing
tutorial prose writing