Render Hook Replacement logo

Render Hook Replacement

OrganizationPopular
gooseworks-ai
render-hook-replacement

Replace an existing video's opening with a supplied clip or free kinetic text hook while retaining and verifying every original body frame, audio, captions and ending. Use for surgical hook tests rather than regenerating the complete ad.

Overview

Publishergooseworks-ai
Repositorygoose-skills
Skill namerender-hook-replacement
Stars
1.2K
Forks
216
Bundled files
8
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by gooseworks-ai on GitHub. Read the source before you install it.

Installation

Install the Render Hook Replacement AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-hook-replacement
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git /tmp/goose-skills
mkdir -p .claude/skills
cp -r /tmp/goose-skills/skills/ads/capabilities/render-hook-replacement .claude/skills/render-hook-replacement
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Render Hook Replacement in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Render Hook Replacement on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Render Hook Replacement is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Human version

Replace only the opening of a selected video. The rest comes from the original, including its ending and burned captions. A supplied hook uses free local assembly; short text can become a free animated card. Generated footage or voice is optional and requires a separate estimate and approval.

The output is a complete MP4 and an edit manifest identifying the exact original, cut, duration change and preservation checks. It stays needs review until the finished video has been watched and its sound checked.


Agent version

Purpose

Perform one hard-cut opening replacement with immutable source identity and decoded retained-body verification. There is no fixed three-second boundary, mandatory creator, new music, body regeneration, whole-video caption pass or automatic final selection. Read the editing guide.

Inputs

  • Python 3.10+, working ffmpeg/ffprobe with libx264 and AAC. Test the binaries themselves before any paid step. Supplied-clip execution uses only Python stdlib.
  • Config JSON following config.example.json. Paths resolve relative to that config, so a fetched package works outside the author's checkout.
  • source.path: exact selected original, never a template sample. Optional source.sha256 refuses changed bytes; project_id/render_id are opaque caller identifiers preserved in the manifest. Compute and freeze the hash at intake.
  • hook_end_sec: positive opening boundary leaving a body. If speech crosses it, choose a measured word/silence boundary. No silent correction of that boundary.
  • hook.path: supplied video, any dimensions/frame rate, normalized to the original. Or hook.text, duration_sec, font_path and optional #RRGGBB colors. Text needs Pillow (part of the video toolchain) and an explicitly supplied licensed font.
  • Optional words:[{start,end,word}] measured on this source, or word_times JSON containing source_sha256 and words. A cut through a measured word fails.
  • Optional captions:{path,output_path}: original separate SRT or timed JSON cues. Complete retained cues shift by the measured duration delta. A cue crossing the cut requires measured words, so removed opening copy does not linger over the body.
  • output:{path,manifest_path?}: a new .mp4 path. Existing outputs and every input are protected from overwrite. The default manifest is <output>.manifest.json.

The original must be CFR, unrotated square-pixel 8-bit yuv420p with zero video start time. Other sources fail before assembly. If normalization is necessary, make it a separately approved source version and freeze that version; do not quietly remaster the selected original. Audio is optional. An original audio/container tail more than 2ms beyond the last video frame also blocks; make a reviewed source version with a final-frame hold before editing. The renderer never trims that narration/music tail and calls it preserved.

Workflow

  1. Watch the exact original and confirm source identity, hook boundary and selected replacement. Keep the original file read-only. Ask only for missing choices.
  2. Probe source/hook, validate SHA256, measured words and captions before encoding. Before creating any new hook, run python3 scripts/replace_hook.py --preflight --config /path/to/source-config.json. This needs only source/cut and optional words/captions, verifies runtime encoders and supported source shape, and writes no media or paid job. Generated hooks are separate work: read the chosen generation capability, quote only the required new opening, obtain approval, then bind its finished clip here.
  3. Run python3 scripts/replace_hook.py --config /path/to/config.json. The script normalizes only the hook geometry/fps; joins original body at normal speed; uses browser-compatible High-profile libx264 CRF 12; retains original body audio with AAC 256k; shifts separate captions; and checks source integrity before publishing files.
  4. Examine the manifest. All four required preservation booleans must be true. Every retained decoded video frame is compared with PSNR ≥45dB and identical frame count/order. Audio is compared at identical sample positions with normalized RMS error ≤0.08 and correlation ≥0.99 (silence uses absolute RMS error ≤0.0001). These are encode tolerances, not a listening verdict.
  5. Watch the actual complete output, especially the join and final frame; listen to the hook/body join, narration/music and ending. Check the new hook copy and brand facts. Fix only the opening and re-run to a new path. Keep the result awaiting review until the review report names this output hash and source. Only after actual watch/audio/caption review, record review:{status:'passed',source_sha256:<original hash>,output_sha256:<finished hash>,checked_at:<ISO datetime>} alongside the passing quality report. Never recycle another output's review.

Free demonstration

From a fresh package folder:

bash
python3 scripts/make_demo.py --out-dir demo
python3 scripts/replace_hook.py --config demo/config.json
python3 -m unittest discover -s tests -v

The neutral fixture replaces 2.4s with 1.5s and retains the original body/end. --font /path/to/licensed.ttf adds burned captions to the fixture. Set HOOK_TEST_FONT=/path/to/licensed.ttf to test the optional kinetic text route too. The demo makes no network requests or paid calls. It is a timing fixture, not a customer ad or catalog upload. High-quality retained-body encoding can produce large files.

For a local pack, use python3 scripts/replace_pack.py --config pack.json with the same source, hook_end_sec, optional words/captions, output_dir and variants:[{label,hook:{path}}]. The wrapper freezes one source hash and writes one complete MP4/manifest per label plus pack-manifest.json. Successful siblings remain available if one fails; retry the failed target with the single renderer.

Output

  • Complete MP4; original untouched.
  • Version 1 manifest with source.{sha256,project_id,render_id}, hook_end_sec, body_start_sec, replacement_duration_sec, new_body_start_sec, duration_delta_sec, output.{path,sha256,duration_sec}, caption artifacts and verification.{source_unchanged,body_video_preserved,body_audio_preserved,caption_timing_preserved}.
  • Separate shifted captions if supplied. Existing burned captions stay in the original body frames; no whole-ad reburning or redesign.
  • status/review.status: needs_review. No project storage behavior is embedded in this generic renderer; the caller saves candidates and performs final review.

Quality Checks

  • Frozen source/hash matches the selected version; old source and hook bytes remain intact.
  • No body frame is missing, duplicated, reordered, graded or cropped.
  • Original body audio stays at normal speed/gain, with no crossfade or added bed.
  • Original ending survives; shifted captions follow the measured duration delta.
  • Subframe boundaries preserve the requested cut; the manifest discloses the first selected original video frame and its offset (less than one source frame).
  • Invalid inputs fail clearly; no paid work occurs inside this capability.

Failure Modes

ProblemAction
Wrong source hash or stale word fileRefetch the selected version and remeasure; never switch versions silently.
Cut clips speech or a separate captionChoose a safe boundary; supply measured words for a partial caption.
VFR, HDR/10-bit, rotated or nonzero-start originalCreate an explicitly reviewed normalized source version first.
Original audio outlasts videoMake a reviewed source version with a final-frame hold, then freeze that version; never drop the audio tail.
Missing/corrupt media or LFS pointerResolve real media before rendering or generation.
Existing output pathUse a fresh version path; never overwrite the original.
Local ffmpeg has no drawtextText uses Pillow PNG + fade/slide overlay; drawtext is not required.
Retained-body verification failsCandidate is not published; inspect the failed video/audio metric and original remains available.
Needed generated creator/voice unavailableKeep supplied footage/text options; show missing provider and new estimate before a paid retry.

Related

[[used-by::create-hook-variant-pack]], [[references::preflight-concat]], [[references::trim-video-clips]], [[references::retime-captions-to-words]], [[references::watch]].

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Render Hook Replacement AI skill do?

Replace an existing video's opening with a supplied clip or free kinetic text hook while retaining and verifying every original body frame, audio, captions and ending. Use for surgical hook tests rather than regenerating the complete ad.

Why use Render Hook Replacement on TypingMind?

Because you install it once and use it with any model. Render Hook Replacement is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Render Hook Replacement in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-hook-replacement. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Render Hook Replacement?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Render Hook Replacement?

As many as you like. As long as a model supports skills, you can use Render Hook Replacement with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Render Hook Replacement AI skill free?

Yes. It is published on GitHub by gooseworks-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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