RRepoGEO

REPOGEO REPORT · LITE

F-R-L/forge-film

Default branch main · commit f67f0a00 · scanned 6/5/2026, 7:02:18 PM

GitHub: 657 stars · 9 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface F-R-L/forge-film, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README H1 to specify core category and differentiator

    Why:

    CURRENT
    # 🎬 Forge

**One story, multiple AI models, zero manual stitching.**
    COPY-PASTE FIX
    # 🎬 Forge: DAG-driven Parallel AI Film Generation Engine

**The only engine that models scene dependencies as a DAG and uses CPM for optimal parallel scheduling.** One story, multiple AI models, zero manual stitching.
  • highhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/F-R-L/forge-film
  • mediumreadme#3
    Expand the README's initial solution paragraph to explicitly state core function

    Why:

    CURRENT
    **Forge automates the entire pipeline.** You write a story. Forge compiles it into a scene graph, routes each scene to the right model, runs them in parallel, keeps visual continuity across model boundaries, and outputs a single `final.mp4`.
    COPY-PASTE FIX
    **Forge is a multi-model, DAG-driven parallel AI film generation engine that automates the entire pipeline.** It uniquely models scene narrative dependencies as a Directed Acyclic Graph (DAG) and uses the Critical Path Method (CPM) for optimal parallel scheduling, allowing you to generate film scenes simultaneously instead of one by one. You write a story. Forge compiles it into a scene graph, routes each scene to the right model, runs them in parallel, keeps visual continuity across model boundaries, and outputs a single `final.mp4`.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface F-R-L/forge-film
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
RunwayML
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. RunwayML · recommended 1×
  2. Stability AI · recommended 1×
  3. Pika Labs · recommended 1×
  4. Midjourney · recommended 1×
  5. Deforum Stable Diffusion · recommended 1×
  • CATEGORY QUERY
    How to automate multi-scene AI video generation with parallel processing for faster output?
    you: not recommended
    AI recommended (in order):
    1. RunwayML
    2. Stability AI
    3. Pika Labs
    4. Midjourney
    5. Deforum Stable Diffusion
    6. EbSynth
    7. Google Cloud AI Platform
    8. AWS SageMaker
    9. Azure Machine Learning
    10. FFmpeg
    11. OpenAI
    12. Ray
    13. Dask
    14. torch.distributed

    AI recommended 14 alternatives but never named F-R-L/forge-film. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to manage dependencies and orchestrate multiple generative AI models for filmmaking?
    you: not recommended
    AI recommended (in order):
    1. Kubeflow Pipelines
    2. MLflow
    3. Apache Airflow
    4. Prefect
    5. Metaflow
    6. Dagster

    AI recommended 6 alternatives but never named F-R-L/forge-film. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of F-R-L/forge-film?
    pass
    AI named F-R-L/forge-film explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts F-R-L/forge-film in production, what risks or prerequisites should they evaluate first?
    pass
    AI named F-R-L/forge-film explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo F-R-L/forge-film solve, and who is the primary audience?
    pass
    AI did not name F-R-L/forge-film — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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  • Brand-free category queries5 vs 2 in Lite
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