mediacollaborationcase-study

Collaboration Platform for Adapting Novels to Screen: An Open‑Source Toolkit for Creative Teams

oopensources

2026-02-12

10 min read

Design an open collaboration stack using Git, WYSIWYG editors, MAM, and LLMs to make novel-to-screen adaptations reproducible and rights-safe.

Hook: Turning a Novel into a Screenable, Reproducible Pipeline

Creative teams adapting novels to screen face two constant headaches: divergent collaboration workflows (writers, directors, legal, VFX, producers) and brittle asset/rights tracking. If you’ve ever lost a cut, argued over which draft was cleared for shooting, or had to hunt for chain-of-title evidence at the last minute, this article is for you. In 2026, with open LLMs, RAG pipelines and mature open-source media tooling, teams can build a reproducible, auditable adaptation stack that respects permissions and speeds iteration — even for high-profile properties like Lola Shoneyin’s The Secret Lives of Baba Segi’s Wives.

Executive Summary — What This Stack Solves

Problem: Creative collaboration is fragmented: screenplay drafts in a Google Doc, media on a cloud bucket, rights spreadsheets in Excel, and informal notes in chat. This causes rework, legal risk, and lost provenance.

Solution: An open-source collaboration stack using Git + Git LFS (or a MAM for heavy assets), a WYSIWYG editor with operational Fountain/Final Draft export, robust rights metadata, and LLM-assisted drafting integrated via RAG. The design emphasizes reproducibility (containerized builds, content-addressable storage) and permissions (provenance metadata, automated checks).

2026 Context: Why Now?

By late 2025 and into 2026 we saw three trends that make this practical:

Open-weight LLMs and accessible RAG toolchains matured, enabling controllable, auditable assistance for creative drafting without sending all IP to opaque third‑party APIs.
Open-source media asset management (MAM) solutions and S3-compatible object stores (e.g., MinIO) stabilized as production-ready alternatives to vendor lock-in.
Infrastructure for reproducibility — Nix/DevContainers, deterministic container builds, content-addressable storage (CAS) and IPFS integrations — became mainstream in DevOps practices and are now adaptable to creative production.

Core Principles of the Stack

Single source of truth: Store canonical text drafts and metadata in Git; store heavy binaries in Git LFS or a MAM and reference them from Git.
Provenance-first architecture: Every asset and draft has signed metadata (hashes, author, clearance state, timestamp).
Reproducible deliverables: Containerized build pipelines produce deterministic outputs (script PDFs, dailies packages, EDLs).
Permission automation: Rights metadata drives gating in CI; builds fail if required clearances are missing.
Human-in-the-loop LLM: LLMs assist drafting but never replace legal clearance or creative final sign-offs.

High-Level Architecture

Here’s the recommended stack, chosen to be open-source friendly and production-ready.

Source Control: Git server (GitLab/Gitea) + Git LFS for intermediate-size assets (storyboards, high-res images) and an integrated MAM for large video files.
Editor Layer: WYSIWYG web editor (ProseMirror/Tiptap) with real-time collaboration via Yjs/Automerge and export to Fountain/FinalDraft-compatible formats.
MAM + Storage: ResourceSpace or MediaGoblin for metadata-rich asset management; MinIO or S3-compatible storage for object storage; FFmpeg for transcode workflows.
Rights & Metadata DB: PostgreSQL with JSONB or a property graph (Neo4j) for modeling chain-of-title, clearances and contributor agreements.
LLM Assistance: Local or self-hosted open LLMs (or privacy-preserving hosted instances) with RAG, vector DB (Milvus/Weaviate) for script/note retrieval.
CI/CD & Reproducibility: GitHub Actions/GitLab CI, Nix/DevContainers, Docker images, and content addressable workflows to produce auditable deliverables.

Practical Workflow — From Novel to Shootable Script

1) Ingest & Canonicalize Source Material

Create a canonical text representation of the novel (OCR/epub → plain text → structured sections). Store the canonical text in Git as an archival source with committed metadata.

Use a deterministic conversion (Pandoc + a known template) so the transformation is reproducible.
Record provenance metadata in a simple JSON file next to the source, e.g., source/novel-canonical.json

2) Build the Story Bible

Story bible (characters, timeline, locations, themes) is a living repo folder. Each entity includes:

Textual descriptions (Markdown/Fountain)
Media references (asset IDs pointing to MAM)
Rights tags (who holds adaptation rights, constraints)

3) Break into Scenes & Index Cards

Use a kanban interface (open-source alternatives: Wekan, Taiga) bound to repository artifacts. Each card links to:

One or more Fountain scene files in Git (plain text) — good for diffs and automated merging
Media references (storyboard images, reference clips) in MAM
Rights metadata JSON

4) Authoring — WYSIWYG + Fountain

Non-technical writers use a WYSIWYG interface built on Tiptap (ProseMirror) that saves a dual representation:

Human-friendly rich text (for editors)
Plain Fountain file for Git diffs and toolchain interop

Implement the export with a serializer that guarantees round-tripping between rich editor state and Fountain, so merges and CI checks remain meaningful.

5) LLM-Assisted Drafting (Controlled & Auditable)

Provide LLM assistance through a RAG pattern:

Vectorize story bible, character notes, and relevant sections of the novel (Milvus/Weaviate).
When a writer requests help, the system retrieves relevant context and prompts an LLM with explicit constraints (tone, length, no new copyrighted text from other works).
All LLM outputs are stamped with metadata (model, parameters, retrieval sources) and saved as a non-authoritative draft branch in Git for human review.

“LLM-assisted drafts must always be flagged and require a named human signer before being considered cleared for production.”

Repository & Asset Layout (Example)

Use a clear, minimal repo layout. Example:

adaptation-project/
├─ novel-canonical/
│  ├─ novel.txt
│  └─ novel-canonical.json
├─ bible/
│  ├─ characters/
│  └─ timeline.json
├─ scenes/
│  ├─ 001-intro.fountain
│  └─ 002-kitchen.fountain
├─ assets/ (small reference images tracked in Git LFS)
├─ meta/ (rights metadata, contracts)
│  └─ rights.db.json
├─ ci/ (build scripts, reproducible Docker/Nix definitions)
└─ .gitattributes

Sample .gitattributes to route large files to LFS:

assets/* filter=lfs diff=lfs merge=lfs -text
*.mov filter=lfs diff=lfs merge=lfs -text
*.mp4 filter=lfs diff=lfs merge=lfs -text

Rights, Permissions & Chain-of-Title

Rights management is not optional. The system should treat clearances as first-class metadata and automate checks in CI:

Every scene and media asset must have a rights.json with fields: owner, rightsType (adaptation, performance), expiry, notes, scannedContractHash.
CI preflight checks read rights.json and deny merges to protected branches if required fields are missing or expirations are within a risky window.
Use digital authorization services and digital signatures (GPG or Sigstore) on contract scans and metadata to increase trustworthiness.

Sample rights JSON:

{
  "assetId": "asset-1234",
  "owner": "Lola Shoneyin",
  "rightsType": "adaptation",
  "grantedTo": "EbonyLife Films",
  "expiry": "2099-12-31",
  "contractScanHash": "sha256:...",
  "signedBy": "producer@example.com",
  "signature": "-----BEGIN PGP SIGNATURE-----..."
}

CI/CD: Enforce & Produce Reproducible Artifacts

CI pipelines verify rights and produce deterministic artifacts. Example CI steps:

Lint Fountain files and check for required metadata
Rights preflight: fail builds if rights.json missing or contract signatures invalid
Run LLM-assist tests in a sandboxed environment (audit logs recorded)
Build deliverables: export scripts to PDF (Pandoc + LaTeX), generate EDLs, transcode selects into dailies
Tag releases with content-hash-based versions for reproducibility

Example GitHub Actions snippet (simplified):

name: Build-Deliverables
on: [push]
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Setup Nix
        uses: cachix/install-nix-action@v18
      - name: Rights Check
        run: python ci/rights_check.py
      - name: Build PDF
        run: pandoc scenes/*.fountain -o build/script.pdf
      - name: Create Release Tag
        run: bash ci/create_tag.sh

Media Asset Management Strategies

Choose between two common configurations depending on scale:

Option A — Git + Git LFS (Small Teams / Low-Res Assets)

Pros: Simple, single repo; atomic commits linking text and small assets.
Cons: Not built for terabyte-scale video. Use only for storyboards, images, reference clips.

Option B — MAM + Object Store (Production Scale)

Use ResourceSpace or a bespoke MAM as the canonical store for video; store references (IDs/hashes) in Git.
Transcoding pipelines (FFmpeg) create lower-res proxies for editorial workflows; proxies live in MAM and are referenced in story cards.
Use MinIO (S3-compatible) for on-prem parity and to avoid vendor lock-in.

Security, Privacy & Ethical Use of LLMs

When LLMs are in the loop, institute guardrails:

Data minimization: Only send the minimum retrieval context to the model.
Attribution: Every LLM output is stamped with provenance metadata and stored in Git branches labeled "ai-draft".
Human sign-off policy: No ai-draft passes to production without explicit named approval.
Bias and cultural sensitivity review: For adaptations of culturally significant works (e.g., Lola Shoneyin’s), include community advisors and sensitivity readers in approval flows.

Auditability & Legal Readiness

Build audit reports automatically:

When a deliverable is produced, the CI also produces an audit bundle: script PDF + rights manifest + asset hashes + signature evidence + list of LLM prompts and retrievals.
Digitally sign the bundle and store an immutable record in CAS or IPFS for long-term proof of chain-of-title.

Migration & Onboarding Checklist

If you are migrating an adaptation project into this stack, follow a pragmatic checklist:

Inventory: catalog all text drafts, assets, contracts and chats.
Capture: add canonical copies to the repo or MAM with full metadata.
Normalize: convert all drafts to Fountain/Markdown where possible.
Rights import: for every contract, create a rights JSON and compute a scan hash; obtain digital signatures if missing.
Bootstrap CI: start with rights-check and build-pdf pipelines, iterate from there.

Case Study: Adapting a High‑Profile Novel (Hypothetical Workflow)

Imagine a production company acquiring rights to adapt Lola Shoneyin’s novel. They’d:

Ingest the novel and create a canonical repository with a signed rights manifest from the author/publisher.
Create a story bible in the repo and begin scene extraction into Fountain files.
Use LLM-assisted drafting to propose scene variations, always saving ai-drafts in a separate branch with full prompt logs.
Store location photos, reference clips and costume boards in the MAM, and link those assets to scene cards with explicit clearance metadata.
Run CI before any merge to the protected 'shoot' branch that asserts all clearances are present and valid.
Generate an audit bundle for each production deliverable, signed and archived.

Advanced Strategies & Future-Proofing

Use content-addressable databases: Make reproducibility stronger by addressing assets by hash, not mutable paths.
Policy-as-code: Express clearance rules in code (OPA/Gatekeeper) so merging to release branches enforces project policy automatically.
Contributor onboarding flows: Automate NDAs and contributor agreements with e-sign and link signed artifacts to contributor identities in the rights DB.
Localization & Subrights: Model sub-rights explicitly (e.g., theatrical, streaming, translations) in the rights DB so distribution windows are clear.

Tooling Recommendations (Open-Source Friendly)

Editors: Tiptap/ProseMirror with Yjs for realtime
Script format: Fountain for plain-text screenplay workflows
MAM: ResourceSpace or MediaGoblin
Object Storage: MinIO (S3-compatible)
Vector DB for RAG: Weaviate or Milvus
CI: GitLab CI / GitHub Actions with Nix for reproducibility
LLMs: Self-hosted open-weight models with audited prompts and retrieval logs

Common Pitfalls & How to Avoid Them

Storing full-size video in Git — use MAM + references instead.
Treating LLM outputs as final — always require human sign-off.
Loose metadata — define and enforce rights schemas early.
No audit logs — build CI to output signed audit bundles at every release.

Actionable Next Steps (Templates & Snippets)

Start small and iterate:

Initialize a repo with the layout above and add a .gitattributes for Git LFS.
Create a rights.json template and add a rights_check.py script to your CI that validates presence and signatures.
Deploy a MinIO instance and a lightweight MAM like ResourceSpace for media ingestion.
Set up a vector DB and index the story bible so writers can request context-aware LLM help safely.

Closing: Why This Matters for Creative Teams in 2026

Adapting novels like Lola Shoneyin’s requires both creative agility and airtight rights governance. The stack described here reduces operational friction, protects IP, and makes reproducible deliverables possible — turning months of manual reconciliation into automated verification. With open LLMs and mature MAM tooling (2025–2026), teams can experiment faster while preserving legal and cultural safeguards.

Call to Action

If you lead an adaptation project, start a pilot this quarter: spin up a repo with the suggested layout, deploy MinIO + ResourceSpace, and wire a simple CI rights check. Want a head start? Clone our starter repo (open-source template with CI snippets, rights schema and editor integrations) and join the discussion in the community channel to share best practices and compliance patterns.

Next step: Download the starter template, run the included CI rights-check, and invite legal + creative leads to the repository to sign off on the first audit bundle.

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