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Agent skill

Pipeline Manifest

by flonat in flonat/flonat-research

Build a traceability manifest linking analysis scripts to inputs, outputs, and manuscript figures or tables.

MITAuto-check passedResearch & Science

Install Pipeline Manifest

skills CLI
$ npx skills add flonat/flonat-research --skill pipeline-manifest -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install flonat/flonat-research pipeline-manifest --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/flonat/flonat-research.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/pipeline-manifest .claude/skills/pipeline-manifest && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
pipeline-manifest
GitHub stars
146
Token cost
~2.6k tokens
SKILL.md length
742 words
Files
1
Skills in repo
83
Repo updated
First seen
Licence
MIT

At a glance

Build a traceability manifest linking analysis scripts to inputs, outputs, and manuscript figures or tables.

  • Works in 6 steps: Discover Scripts → Extract Pipeline Information → Build Dependency Graph → …
  • Documenting a computational pipeline
  • SKILL.md covers When to Use, When NOT to Use, Modes and Structured Script Header Format, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Pipeline Manifest is an agent skill from flonat/flonat-research. Build a traceability manifest linking analysis scripts to inputs, outputs, and manuscript figures or tables. Use when documenting a computational pipeline or locating the source of a reported artefact. Not for verifying that paper claims match code outputs; use the code-paper auditor.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science. The repository describes itself as: Shareable Claude Code + Codex infrastructure for PhD researchers — skills, agents, hooks, and rules for academic workflows. The licence is MIT.

When your agent uses it

  • Documenting a computational pipeline
  • Locating the source of a reported artefact

Example prompts

  • “/pipeline-manifest”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Glob, Grep, AskUserQuestion

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Discover Scripts
  2. Extract Pipeline Information
  3. Build Dependency Graph
  4. Build Paper Linkage
  5. Write pipeline.md
  6. (Add Headers Mode Only): Insert Headers

What it can do on your machine

Read from SKILL.md and the folder at commit da27600. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Glob
    • Grep
    • AskUserQuestion

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python, r, stata, julia and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Pipeline Manifest loads about 2.6k tokens when it runs. Until then it costs about 76 tokens; SKILL.md has 742 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~76
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from flonat/flonat-research at commit da27600, republished under its MIT licence (© flonat). 742 words, ~2,588 tokens.

Download SKILL.mdSave it as .claude/skills/pipeline-manifest/SKILL.md (or your agent's skills folder).
name
pipeline-manifest
description
Build a traceability manifest linking analysis scripts to inputs, outputs, and manuscript figures or tables. Use when documenting a computational pipeline or locating the source of a reported artefact. Not for verifying that paper claims match code outputs; use the code-paper auditor.
allowed-tools
Read, Write, Edit, Glob, Grep, AskUserQuestion
argument-hint
[project-path]

Pipeline Manifest

Build and maintain a pipeline.md that maps every script in a research project to its inputs, outputs, and the paper figures/tables it feeds. Optionally add structured headers to scripts that lack them.

When to Use

  • When starting a multi-step empirical project (establish the pipeline early)
  • Before sharing code with coauthors (makes the data flow legible)
  • Before submission (ensures the replication package is traceable)
  • After inheriting or reviving old code (pair with code-archaeology)
  • When you can't remember which script produces Figure 3

When NOT to Use

  • Pure theory projects with no empirical pipeline
  • Single-script projects (no pipeline to map)
  • Code quality review — use the code-review agent instead (this skill maps structure, not quality)

Modes

Ask the user which mode to run:

ModeWhat it doesWrites to
Scan (default)Read-only. Scans scripts, builds pipeline.mdpipeline.md only
Add headersScan + insert structured headers into scripts that lack thempipeline.md + script files

In Add headers mode, show the proposed header for each script and get confirmation before writing. Never overwrite an existing structured header — only add to scripts that lack one.

Structured Script Header Format

Every research script should begin with a structured header block. The format adapts to the language:

Python
python
# ============================================================================
# PURPOSE: [One sentence describing what this script does]
# INPUTS:  [Comma-separated list of input files, relative to project root]
# OUTPUTS: [Comma-separated list of output files, relative to project root]
# DEPENDS: [Scripts that must run before this one, or "none"]
# PAPER:   [Figure/table references this feeds, e.g. "Figure 2, Table 1", or "none"]
# ============================================================================
R
r
# ============================================================================
# PURPOSE: [One sentence describing what this script does]
# INPUTS:  [Comma-separated list of input files, relative to project root]
# OUTPUTS: [Comma-separated list of output files, relative to project root]
# DEPENDS: [Scripts that must run before this one, or "none"]
# PAPER:   [Figure/table references this feeds, e.g. "Figure 2, Table 1", or "none"]
# ============================================================================
Stata
stata
* ============================================================================
* PURPOSE: [One sentence describing what this script does]
* INPUTS:  [Comma-separated list of input files, relative to project root]
* OUTPUTS: [Comma-separated list of output files, relative to project root]
* DEPENDS: [Scripts that must run before this one, or "none"]
* PAPER:   [Figure/table references this feeds, e.g. "Figure 2, Table 1", or "none"]
* ============================================================================
Julia
julia
# ============================================================================
# PURPOSE: [One sentence describing what this script does]
# INPUTS:  [Comma-separated list of input files, relative to project root]
# OUTPUTS: [Comma-separated list of output files, relative to project root]
# DEPENDS: [Scripts that must run before this one, or "none"]
# PAPER:   [Figure/table references this feeds, e.g. "Figure 2, Table 1", or "none"]
# ============================================================================

Header Field Definitions

FieldWhat it containsHow to populate
PURPOSEOne sentence. What does this script do?Read the script and summarise
INPUTSFiles this script reads. Paths relative to project root.Grep for read, load, import, open, use patterns
OUTPUTSFiles this script writes. Paths relative to project root.Grep for write, save, export, ggsave, savefig, sink patterns
DEPENDSOther scripts that must run first (their outputs are this script's inputs).Trace input files back to the scripts that produce them
PAPERWhich figures, tables, or sections in the paper use this script's output.Match output filenames against \includegraphics, \input, \include in .tex files

Workflow

Phase 1: Discover Scripts

Scan the project for research scripts:

code/**/*.{py,R,r,do,jl,m}
src/**/*.{py,R,r,do,jl,m}
scripts/**/*.{py,R,r,do,jl,m}

Exclude:

  • __pycache__/, .venv/, renv/, node_modules/
  • Test files (test_*.py, *_test.R)
  • Setup/config scripts (setup.py, conftest.py)

Sort by filename (numerical prefixes like 01_, 02_ determine natural order).

Phase 2: Extract Pipeline Information

For each script:

  1. Check for existing header. Look for the PURPOSE: / INPUTS: / OUTPUTS: / DEPENDS: / PAPER: pattern in the first 20 lines.

  2. If header exists: Parse it directly. Trust the header as ground truth.

  3. If no header: Read the full script and infer:

    • Inputs: File read operations (pd.read_csv, read.csv, readRDS, load, use, open, import delimited, fread, arrow::read_parquet, readr::read_*)
    • Outputs: File write operations (to_csv, write.csv, saveRDS, save, ggsave, plt.savefig, export, sink, write_parquet, fwrite, outsheet, estout)
    • Dependencies: Cross-reference — if script B reads a file that script A writes, then B depends on A
    • Paper links: Match output filenames against \includegraphics{...} and \input{...} in .tex files
Show full SKILL.md (287 more words)Show less
Phase 3: Build Dependency Graph

From the extracted information, construct:

  1. Execution order — topological sort of the dependency graph. Flag cycles as errors.
  2. Orphan detection — scripts whose outputs are never used by another script or the paper. These may be exploratory/deprecated.
  3. Missing link detection — inputs that no script produces and that don't exist in data/raw/.
Phase 4: Build Paper Linkage

Scan all .tex files in paper/ for:

  • \includegraphics{path} — figures
  • \input{path} — tables or sub-documents
  • \include{path} — chapters

Match these paths to script outputs. Build a reverse map: for each figure/table in the paper, which script(s) produce it?

Phase 5: Write pipeline.md

Write pipeline.md to the project root using the format below.

Phase 6 (Add Headers Mode Only): Insert Headers

For scripts missing structured headers:

  1. Generate the header from Phase 2 analysis
  2. Show the proposed header to the user
  3. On confirmation, insert at the top of the file (after any shebang line or encoding declaration)

pipeline.md Format

markdown
# Pipeline Manifest

> Auto-generated by `pipeline-manifest` on YYYY-MM-DD.
> Manually edit the PAPER column and any inferred values that are wrong.
> Re-run `pipeline-manifest` to refresh after adding or modifying scripts.

## Pipeline Table

| # | Script | Purpose | Inputs | Outputs | Depends | Paper |
|---|--------|---------|--------|---------|---------|-------|
| 1 | `code/01_clean.R` | Clean raw survey data | `data/raw/survey.csv` | `data/processed/survey_clean.rds` | none | -- |
| 2 | `code/02_merge.R` | Merge survey with admin data | `data/processed/survey_clean.rds`, `data/raw/admin.csv` | `data/processed/merged.rds` | `01_clean.R` | -- |
| 3 | `code/03_analysis.R` | Run main regressions | `data/processed/merged.rds` | `results/main_results.rds`, `paper/figures/fig_coef.pdf` | `02_merge.R` | Figure 2 |
| 4 | `code/04_robustness.py` | Robustness checks | `data/processed/merged.rds` | `results/robustness.csv`, `paper/figures/fig_robust.pdf` | `02_merge.R` | Figure 3, Table A1 |

## Figure & Table Manifest

| Paper Reference | Producing Script | Output File |
|----------------|-----------------|-------------|
| Figure 2 | `code/03_analysis.R` | `paper/figures/fig_coef.pdf` |
| Figure 3 | `code/04_robustness.py` | `paper/figures/fig_robust.pdf` |
| Table A1 | `code/04_robustness.py` | `results/robustness.csv` |

## Dependency Graph

data/raw/survey.csv ─┐ ├─> 01_clean.R ─> data/processed/survey_clean.rds ─┐ data/raw/admin.csv ──┘ ├─> 02_merge.R ─> data/processed/merged.rds ─┬─> 03_analysis.R │ └─> 04_robustness.py


## Diagnostics

### Orphan Scripts
Scripts whose outputs are not consumed by any other script or the paper.

### Missing Inputs
Files referenced as inputs but not produced by any script and not found in `data/raw/`.

### Execution Order
Recommended order based on dependency resolution:
1. `code/01_clean.R`
2. `code/02_merge.R`
3. `code/03_analysis.R`
4. `code/04_robustness.py` (can run in parallel with step 3)

Updating an Existing pipeline.md

If pipeline.md already exists:

  1. Read it and parse the existing table
  2. Re-scan scripts (some may have been added, removed, or modified)
  3. Preserve manual edits — if the user has edited the PAPER column or added notes, keep those values. Only update fields that can be re-derived from code (PURPOSE, INPUTS, OUTPUTS, DEPENDS).
  4. Flag changes: "2 scripts added, 1 script removed, 3 entries updated"
  5. Write the updated file

Cross-References

  • the code-review agent — Quality review for individual scripts (checks header presence in Category 2: Script Structure)
  • code-archaeology — For understanding unfamiliar code before building the manifest
  • pre-submission-report — Pipeline manifest helps verify the replication package is complete
  • init-project-research — New projects can run pipeline-manifest once scripts exist

© flonat, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/pipeline-manifest of flonat/flonat-research.

Open the folder on GitHubat commit da27600

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Categories

Questions about Pipeline Manifest

What does Pipeline Manifest do?

Build a traceability manifest linking analysis scripts to inputs, outputs, and manuscript figures or tables. Pipeline Manifest is an agent skill from flonat/flonat-research. Build a traceability manifest linking analysis scripts to inputs, outputs, and manuscript figures or tables.

When should I use Pipeline Manifest?

Pipeline Manifest fits situations like: documenting a computational pipeline; locating the source of a reported artefact.

How do I install Pipeline Manifest in Claude Code?

Run `npx skills add flonat/flonat-research --skill pipeline-manifest -a claude-code`. Or copy the skill folder (skills/pipeline-manifest in flonat/flonat-research) into .claude/skills/pipeline-manifest in your project. Claude Code loads it when a task matches its description.

How do I install Pipeline Manifest in Codex?

Run `npx skills add flonat/flonat-research --skill pipeline-manifest -a codex`. Or copy the skill folder (skills/pipeline-manifest in flonat/flonat-research) into .agents/skills/pipeline-manifest in your project. Codex loads it when a task matches its description.

Can I use Pipeline Manifest in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add flonat/flonat-research --skill pipeline-manifest -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pipeline-manifest, .gemini/skills/pipeline-manifest, .github/skills/pipeline-manifest and .opencode/skills/pipeline-manifest in your project.

What does Pipeline Manifest need to run?

SKILL.md names no scripts, command-line tools or credentials: Pipeline Manifest is instructions for the agent only. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Glob, Grep, AskUserQuestion.

Does Pipeline Manifest access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Pipeline Manifest safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Pipeline Manifest use?

Pipeline Manifest is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Pipeline Manifest use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Pipeline Manifest?

Skills that share tags, products or a category with Pipeline Manifest: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Pipeline Manifest?

flonat (a GitHub user) maintains it in flonat/flonat-research, which has 146 GitHub stars. The repository holds 83 skills in this directory. The repository was last updated on September 29, 2026.

Source: flonat/flonat-research on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.