research-pipeline-runner

Original：🇺🇸 English

Translated

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。

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Sourcewilloscar/research-units-pipeline-skills

Added on2026-02-08

NPX Install

npx skill4agent add willoscar/research-units-pipeline-skills research-pipeline-runner

SKILL.md Content

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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
```

Recommended run loop (skills-first)

Initialize workspace (C0):

create
```
workspaces/<name>/
```
write
```
GOAL.md
```
, lock pipeline (
```
PIPELINE.lock.md
```
), seed
```
queries.md
```

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

Stop at HUMAN checkpoints:

default survey checkpoint is
```
C2
```
(scope + outline)
write a concise approval request in
```
DECISIONS.md
```
and wait

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

Strict-mode behavior (by design)

In

--strict

runs, several semantic C3/C4 artifacts are treated as scaffolds until explicitly marked refined. This is intentional: it prevents bootstrap JSONL from silently passing into C5 writing (a major source of hollow/templated prose).

Create these markers only after you have manually refined/spot-checked the artifacts:

```
outline/subsection_briefs.refined.ok
```
```
outline/chapter_briefs.refined.ok
```
```
outline/evidence_bindings.refined.ok
```
```
outline/evidence_drafts.refined.ok
```
```
outline/anchor_sheet.refined.ok
```
```
outline/writer_context_packs.refined.ok
```

The runner may BLOCK even if the JSONL exists; add the marker after refinement, then rerun/resume the unit.

Finish:

merge → audit → (optional) LaTeX scaffold/compile

Optional CLI helpers (debug only)

Kickoff + run (optional; convenient, not required):

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.