research-pipeline-runner

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Run this repo’s Units+Checkpoints research pipelines end-to-end (survey/综述/review/调研/教程/系统综述/审稿), with workspaces + checkpoints.**Trigger**: run pipeline, kickoff, 继续执行, 自动跑, 写一篇, survey/综述/review/调研/教程/系统综述/审稿.**Use when**: 用户希望端到端跑流程（创建 `workspaces/<name>/`、生成/执行 `UNITS.csv`、遇到 HUMAN checkpoint 停下等待）。**Skip if**: 用户明确要手工逐条执行（用 `unit-executor`），或你不应自动推进到 prose 阶段。**Network**: depends on selected pipeline (arXiv/PDF/citation verification may need network; offline import supported where available).**Guardrail**: 必须尊重 checkpoints（无 Approve 不写 prose）；遇到 HUMAN 单元必须停下等待；禁止在 repo root 创建 workspace 工件。

⭐1stars🔀0forks📁View on GitHub🕐Updated Jan 8, 2026
[Academic Research]

When & Why to Use This Skill

The Research Pipeline Runner is a sophisticated orchestration skill designed to automate end-to-end academic and technical research workflows. It manages the entire lifecycle of research projects—from literature surveys and systematic reviews to peer-review simulations—using a structured unit-based execution model. By integrating human-in-the-loop checkpoints and workspace-specific artifact management, it ensures that complex research tasks are auditable, high-quality, and strictly aligned with user goals.

Use Cases

  • Automated Literature Surveys: Generate comprehensive, citation-backed surveys on specific topics (e.g., 'LLM Agents') with options for Markdown or LaTeX output.
  • Systematic Reviews: Execute rigorous research pipelines that scan arXiv and other databases to synthesize current state-of-the-art findings.
  • Technical Tutorial Generation: Create structured, end-to-end tutorials for complex software or research methodologies using a predefined pipeline.
  • Peer Review Simulation: Conduct automated preliminary reviews of manuscripts to identify weaknesses and ensure compliance with academic standards before submission.
  • Knowledge Synthesis: Rapidly produce 'snapshots' or summaries of recent literature to stay updated on fast-moving research fields.
nameresearch-pipeline-runner
description|
**Trigger**run pipeline, kickoff, 继续执行, 自动跑, 写一篇, survey/综述/review/调研/教程/系统综述/审稿.
**Use when**用户希望端到端跑流程（创建 `workspaces/<name>/`、生成/执行 `UNITS.csv`、遇到 HUMAN checkpoint 停下等待）。
**Skip if**用户明确要手工逐条执行（用 `unit-executor`），或你不应自动推进到 prose 阶段。
**Network**depends on selected pipeline (arXiv/PDF/citation verification may need network; offline import supported where available).
**Guardrail**必须尊重 checkpoints（无 Approve 不写 prose）；遇到 HUMAN 单元必须停下等待；禁止在 repo root 创建 workspace 工件。

Research Pipeline Runner

Goal: let a user trigger a full pipeline with one natural-language request, while keeping the run auditable (Units + artifacts + checkpoints).

This skill is coordination:

  • semantic work is done by the relevant skills’ SKILL.md
  • scripts are deterministic helpers (scaffold/validate/compile), not the author

Inputs

  • User goal (one sentence is enough), e.g.:
    • “给我写一个 agent 的 latex-survey”
  • Optional:
    • explicit pipeline path (e.g., pipelines/arxiv-survey-latex.pipeline.md)
    • constraints (time window, language: EN/中文, evidence_mode: abstract/fulltext)

Outputs

  • A workspace under workspaces/<name>/ containing:
    • STATUS.md, GOAL.md, PIPELINE.lock.md, UNITS.csv, CHECKPOINTS.md, DECISIONS.md
    • pipeline-specific artifacts (papers/outline/sections/output/latex)

Non-negotiables

  • Use UNITS.csv as the execution contract; one unit at a time.
  • Respect checkpoints (CHECKPOINTS.md): no long prose until required approvals are recorded in DECISIONS.md (survey default: C2).
  • Stop at HUMAN checkpoints and wait for explicit sign-off.
  • Never create workspace artifacts in the repo root; always use workspaces/<name>/.

Decision tree: pick a pipeline

User goal → choose:

  • Survey/综述/调研 + Markdown draft → pipelines/arxiv-survey.pipeline.md
  • Survey/综述/调研 + PDF output → pipelines/arxiv-survey-latex.pipeline.md
  • Snapshot/速览 → pipelines/lit-snapshot.pipeline.md
  • Tutorial/教程 → pipelines/tutorial.pipeline.md
  • Systematic review/系统综述 → pipelines/systematic-review.pipeline.md
  • Peer review/审稿 → pipelines/peer-review.pipeline.md
  1. Initialize workspace (C0):
  • create workspaces/<name>/
  • write GOAL.md, lock pipeline (PIPELINE.lock.md), seed queries.md
  1. Execute units sequentially:
  • follow each unit’s SKILL.md to produce the declared outputs
  • only mark DONE when acceptance criteria are satisfied and outputs exist
  1. Stop at HUMAN checkpoints:
  • default survey checkpoint is C2 (scope + outline)
  • write a concise approval request in DECISIONS.md and wait
  1. Writing-stage self-loop (when drafts look thin/template-y):
  • prefer local fixes over rewriting everything:
    • writer-context-pack (C4→C5 bridge) makes packs debuggable
    • subsection-writer writes per-file units
    • writer-selfloop fixes only failing sections/*.md
    • draft-polisher removes generator voice without changing citation keys
  1. Finish:
  • merge → audit → (optional) LaTeX scaffold/compile

Practical commands (optional helpers)

  • Kickoff + run (recommended): python scripts/pipeline.py kickoff --topic "<topic>" --pipeline <pipeline-name> --run --strict
  • Resume: python scripts/pipeline.py run --workspace <ws> --strict
  • Approve checkpoint: python scripts/pipeline.py approve --workspace <ws> --checkpoint C2
  • Mark refined unit: python scripts/pipeline.py mark --workspace <ws> --unit-id <U###> --status DONE --note "LLM refined"

Handling common blocks

  • HUMAN approval required: summarize produced artifacts, ask for approval, then record it and resume.
  • Quality gate blocked (output/QUALITY_GATE.md exists): treat current outputs as scaffolding; refine per the unit’s SKILL.md; mark DONE; resume.
  • No network: use offline imports (papers/imports/ or arxiv-search --input).
  • Weak coverage: broaden queries or reduce/merge subsections (outline-budgeter) before writing.

Quality checklist

  • UNITS.csv statuses reflect actual outputs (no DONE without outputs).
  • No prose is written unless DECISIONS.md explicitly approves it.
  • The run stops at HUMAN checkpoints with clear next questions.
  • In strict mode, scaffold/stub outputs do not get marked DONE without refinement.