Pipeline Manifest

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:

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

# ============================================================================
# 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

# ============================================================================
# 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

* ============================================================================
* 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

# ============================================================================
# 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

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

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

# 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