Creator · gooseworks-ai
Last updated · Sep 4, 2026
Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a n
Creator · gooseworks-ai
Last updated · Sep 4, 2026
Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a n
Creator · gooseworks-ai
Last updated · Sep 4, 2026
Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a n
Creator · gooseworks-ai
Last updated · Sep 4, 2026
Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a n
Sandbox only
Install targets
Codex install prompt
Install the "render-3d-character-explainer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-3d-character-explainer. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a narration track, it trims each clip to its scene window, re-encodes every segment to identical 1080x1920/30fps/libx264/yuv420p (decrease+pad, never crop) so the concat demuxer never drops frames, concats, and muxes audio — in RESTYLE mode the source ad's VO+music mix is reused verbatim, in ORIGINAL mode fresh per-scene VO (loudnorm I=-14) is mixed under an optional music bed (loudnorm I=-26). A static-still fallback loops a scene's keyframe when its clip is missing/failed, so the master always assembles; libass captions are burned last. FREE deterministic assembly (Python + ffmpeg, no bash, no paid calls); the recipe supplies the clips, keyframes, VO or source audio, and caption table and gates the paid cast-anc After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"gooseworks-ai-render-3d-character-explainer","task":"Install render-3d-character-explainer","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainer
Maintenance
fresh
4d since push
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
1.2K
78/100 Quality · 76/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision · The skill description claims 'no bash' but the smoke test uses bash commands for setup; this is a minor inconsistency.
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
1.2K GitHub stars
Repo activity
1.2K stars, 206 forks
Maintenance
4d since push
License
MIT
Install
npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainer
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Install command
npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainerDo not use when
Alternative
1.9K Stars
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Alternative
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npx skills add mvanhorn/last30days-skill -g
Alternative
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npx skills add Imbad0202/academic-research-skills
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28.0K Stars
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Agent safety v2
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Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
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/api/agent/resolve?task=Use%20render-3d-character-explainer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
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Task: Use render-3d-character-explainer in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20render-3d-character-explainer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/gooseworks-ai-render-3d-character-explainer/install
Install command: npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainer
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/api/skills/gooseworks-ai-render-3d-character-explainer/install
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/api/skills/search?q=render-3d-character-explainer&limit=3
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Use render-3d-character-explainer for this task. Review https://www.openagentskill.com/api/skills/gooseworks-ai-render-3d-character-explainer/install, then install with: npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainerRegistry metadata
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Agent fit
Browser automation
Use-case tags
Platforms
Claude Code
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Browser automation
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Command ready
Use when
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review first
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Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
PASS1.2K GitHub stars
Stars/forks activity
INFO1.2K stars, 206 forks; issue activity unavailable in current metadata
Recent maintenance
PASS4d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Process rich media
I need my agent to process images, video, or audio and extract useful information.
Workflow fit
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Brief to publish-ready creative
A practical workflow for agents that shape a video brief, create strong multimodal prompts, generate supporting B-roll, and prepare a reviewable short-form or explainer video.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: render-3d-character-explainer description: Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a narration track, it trims each clip to its scene window, re-encodes every segment to identical 1080x1920/30fps/libx264/yuv420p (decrease+pad, never crop) so the concat demuxer never drops frames, concats, and muxes audio — in RESTYLE mode the source ad's VO+music mix is reused verbatim, in ORIGINAL mode fresh per-scene VO (loudnorm I=-14) is mixed under an optional music bed (loudnorm I=-26). A static-still fallback loops a scene's keyframe when its clip is missing/failed, so the master always assembles; libass captions are burned last. FREE deterministic assembly (Python + ffmpeg, no bash, no paid calls); the recipe supplies the clips, keyframes, VO or source audio, and caption table and gates the paid cast-anchor/keyframe/Kling-i2v/VO/music calls to their own capabilities. Use for the 3d-character-explainer listicle format. status: active ---
# render-3d-character-explainer
The free, deterministic renderer for the **3d-character-explainer** video ad format — the glossy Pixar-style 3D spot built on an **"N types of X" listicle** spine, where a recurring human protagonist plus a locked cast of **N persona characters (one per list item)** carry a hook → "deeper story" → cast-reveal → one beat per list item → kicker → product test → relieved payoff. This capability is the **FREE assembly stage only**. All generative work (Nano-Banana cast anchors + per-scene keyframes, Kling-V3 i2v clips, ElevenLabs VO + music, or a source ad's audio reused verbatim) happens upstream in the recipe and is handed to this capability as files.
It ports the validated compose recipe from the Bristle "Six Types" restyle run (`_render_full.sh` — per-scene trim → normalize 1080×1920/fps30 → concat -c copy → mux the source audio, with a static-still fallback on any failed clip). The assembly is deterministic — iterate the cut for free, re-roll only the offending paid beat.
## Two modes
- **Restyle mode** (`audio_mode: "restyle"`, the reference run) — re-tell a finished source ad, beat for beat, as 3D character comedy. The source ad's **audio mix (VO + music bed) is reused VERBATIM** (`source_audio`), and the per-scene `target_sec` table is inherited from the source's scene timing. No new VO or music is rendered. The trims must sum to the source audio length. - **Original mode** (`audio_mode: "original"`) — the ad authors its own narration. Each scene carries a measured VO cue (`scenes[].vo`, `target_sec` = the ffprobe'd VO duration) which is concatenated into a VO track (loudnorm I=-14) and optionally mixed under a `music_bed` (loudnorm I=-26 then `volume`, `amix normalize=0`).
## What it does (the deterministic recipe)
1. **Per-scene retime.** Each i2v clip is trimmed to its scene `target_sec` and normalized to identical dims/fps/SAR (`scale=W:H:force_original_aspect_ratio=decrease,pad=W:H:(ow-iw)/2:(oh-ih)/2:color=<pad>,fps=30,setsar=1`). A clip **shorter** than its window is extended with `tpad=stop_mode=clone`; a longer one is `-t` trimmed. Decrease+pad (never crop) preserves the full 9:16 keyframe framing. 2. **Static-still fallback.** For any scene whose `clip` is missing or failed to render, the scene's `keyframe` PNG is looped (`-loop 1`) for `target_sec`, so the master always assembles. Fallback scenes are printed at the end. 3. **Identical re-encode + concat.** Every segment is re-encoded `libx264 -crf 18 -pix_fmt yuv420p -r 30` even if already correct — a dims/framerate mismatch makes the concat demuxer silently drop frames — then concatenated via the concat demuxer (`-c copy`). 4. **Audio.** Restyle: `source_audio` muxed verbatim (`-map 0:v -map 1:a`), clamped to the video length. Original: per-scene VO track (optional `atempo`, `apad`, `-t` clamp) → loudnorm → optionally mixed under the music bed. 5. **Captions last.** `make_captions.py` emits a libass `.ass` (one cue per scene, `start = scene_start + 0.08s`, suppressed on any scene with no caption — e.g. a product/end-card beat carrying its own typeset copy). `compose.py` burns it as the final filter so captions sit on top. Word-level energy-pop captions (Whisper on the narration) are the recipe's upstream option — produce that `.ass` externally and point `captions_ass` at it; compose burns whatever `.ass` it's handed.
## Scripts (free — Python + ffmpeg, no bash, no paid calls)
- `scripts/make_captions.py` — emits the per-scene libass `.ass` from the SAME scene table compose reads, so caption windows stay in lockstep with the cut. Run before `compose.py` (or leave `captions_ass` unset / pointing at nothing to skip captions). - `scripts/compose.py` — the assembler: per-scene trim + identical 1080×1920/30fps re-encode (static-still fallback on missing clips) → concat → audio (restyle verbatim / original mix) → burn captions → master mp4. - `scripts/config.example.json` — the shape of the `config` the recipe binds (the brand-neutralised "Six Types" restyle values as a worked reference).
## Inputs (all via `--config` + a runtime work dir — NO hardcoded paths)
`config.json` carries: `audio_mode` (`restyle` | `original`), `scenes[]` (each `{id, clip, keyframe, target_sec, caption?, vo?, atempo?}` where `target_sec` is the source-inherited window in restyle mode or the **measured** VO window in original mode, and `keyframe` is the static-still fallback source), `source_audio` (restyle), `music_bed` + `music_volume` + `atempo` (original), `width`/`height` (default 1080×1920), `pad_color` (letterbox colour), `captions_ass`, and `caption_style`. See `config.example.json`.
## Craft rules (load-bearing — faithful to the source molecule + reference run)
- **Restyle inherits the source timing.** A restyle reuses the source ad's exact audio, scene order, and per-beat durations verbatim; only an original-mode remix authors its own VO + timing table. Merge any sub-1.5s flash scene into a neighbour upstream to avoid a dead micro-cut (the reference folded scene 7 into scene 8). - **Normalize decrease+pad, never crop** — the listicle's cast-reveal + per-persona framing must not lose edges; letterbox-pad to the canvas colour instead. Re-encode every segment to 30fps before concat, even if already correct, or the concat demuxer silently drops frames. - **Static-still fallback is mandatory** — Kling can 403 mid-run (a billing wall after a burst of successes, not a rate limit). Any failed clip loops its keyframe so the master still assembles; re-roll only the missing beat and recompose (free). - **`generate_audio` was false upstream** — Kling would otherwise invent its own dialog track; the real narration is muxed here separately. (This is the recipe's upstream call, not this capability.) - **No AI-rendered brand text** — the product-beat keyframe shows a BLANK-label box; the real wordmark/end-card copy is composited upstream, never AI-drawn. Suppress captions on any product/end-card beat (its typeset copy carries the message — two text layers at one spot are both unreadable). - **Caption `start = scene_start + 0.08s`** (avoids the caption flashing a frame before a cut).
## Requires
`watch` (QC the final master — the human protagonist reads as the SAME person every scene (wardrobe/hair/lighting held), each persona is on-model, the cast-reveal lineup matches the N list items, the product beat shows the REAL box, narration lands beat-for-beat, and duration is within ±0.1s of the summed windows). The recipe gates the paid `create-image-fal` (cast anchors + keyframes), `create-video-fal` (Kling-V3 i2v), `create-vo-elevenlabs`, and `create-music-elevenlabs` calls to their own capabilities — this capability itself makes NO paid calls.
Source provenance
Decision snapshot
1,195 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for render-3d-character-explainer, ready for a manual X post.
render-3d-character-explainer: Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types... 1.2K stars https://www.openagentskill.com/skills/gooseworks-ai-render-3d-character-explainer?ref=x
Listing + install path for render-3d-character-explainer: https://www.openagentskill.com/skills/gooseworks-ai-render-3d-character-explainer?ref=x Install: npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainer
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mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsSandbox only
Install targets
Codex install prompt
Install the "render-3d-character-explainer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-3d-character-explainer. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a narration track, it trims each clip to its scene window, re-encodes every segment to identical 1080x1920/30fps/libx264/yuv420p (decrease+pad, never crop) so the concat demuxer never drops frames, concats, and muxes audio — in RESTYLE mode the source ad's VO+music mix is reused verbatim, in ORIGINAL mode fresh per-scene VO (loudnorm I=-14) is mixed under an optional music bed (loudnorm I=-26). A static-still fallback loops a scene's keyframe when its clip is missing/failed, so the master always assembles; libass captions are burned last. FREE deterministic assembly (Python + ffmpeg, no bash, no paid calls); the recipe supplies the clips, keyframes, VO or source audio, and caption table and gates the paid cast-anc After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"gooseworks-ai-render-3d-character-explainer","task":"Install render-3d-character-explainer","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainer
Maintenance
fresh
4d since push
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
1.2K
78/100 Quality · 76/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision · The skill description claims 'no bash' but the smoke test uses bash commands for setup; this is a minor inconsistency.
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
1.2K GitHub stars
Repo activity
1.2K stars, 206 forks
Maintenance
4d since push
License
MIT
Install
npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainer
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Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
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Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainerDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
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Task: Use render-3d-character-explainer in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20render-3d-character-explainer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/gooseworks-ai-render-3d-character-explainer/install
Install command: npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainer
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A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Brief to publish-ready creative
A practical workflow for agents that shape a video brief, create strong multimodal prompts, generate supporting B-roll, and prepare a reviewable short-form or explainer video.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: render-3d-character-explainer description: Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a narration track, it trims each clip to its scene window, re-encodes every segment to identical 1080x1920/30fps/libx264/yuv420p (decrease+pad, never crop) so the concat demuxer never drops frames, concats, and muxes audio — in RESTYLE mode the source ad's VO+music mix is reused verbatim, in ORIGINAL mode fresh per-scene VO (loudnorm I=-14) is mixed under an optional music bed (loudnorm I=-26). A static-still fallback loops a scene's keyframe when its clip is missing/failed, so the master always assembles; libass captions are burned last. FREE deterministic assembly (Python + ffmpeg, no bash, no paid calls); the recipe supplies the clips, keyframes, VO or source audio, and caption table and gates the paid cast-anchor/keyframe/Kling-i2v/VO/music calls to their own capabilities. Use for the 3d-character-explainer listicle format. status: active ---
# render-3d-character-explainer
The free, deterministic renderer for the **3d-character-explainer** video ad format — the glossy Pixar-style 3D spot built on an **"N types of X" listicle** spine, where a recurring human protagonist plus a locked cast of **N persona characters (one per list item)** carry a hook → "deeper story" → cast-reveal → one beat per list item → kicker → product test → relieved payoff. This capability is the **FREE assembly stage only**. All generative work (Nano-Banana cast anchors + per-scene keyframes, Kling-V3 i2v clips, ElevenLabs VO + music, or a source ad's audio reused verbatim) happens upstream in the recipe and is handed to this capability as files.
It ports the validated compose recipe from the Bristle "Six Types" restyle run (`_render_full.sh` — per-scene trim → normalize 1080×1920/fps30 → concat -c copy → mux the source audio, with a static-still fallback on any failed clip). The assembly is deterministic — iterate the cut for free, re-roll only the offending paid beat.
## Two modes
- **Restyle mode** (`audio_mode: "restyle"`, the reference run) — re-tell a finished source ad, beat for beat, as 3D character comedy. The source ad's **audio mix (VO + music bed) is reused VERBATIM** (`source_audio`), and the per-scene `target_sec` table is inherited from the source's scene timing. No new VO or music is rendered. The trims must sum to the source audio length. - **Original mode** (`audio_mode: "original"`) — the ad authors its own narration. Each scene carries a measured VO cue (`scenes[].vo`, `target_sec` = the ffprobe'd VO duration) which is concatenated into a VO track (loudnorm I=-14) and optionally mixed under a `music_bed` (loudnorm I=-26 then `volume`, `amix normalize=0`).
## What it does (the deterministic recipe)
1. **Per-scene retime.** Each i2v clip is trimmed to its scene `target_sec` and normalized to identical dims/fps/SAR (`scale=W:H:force_original_aspect_ratio=decrease,pad=W:H:(ow-iw)/2:(oh-ih)/2:color=<pad>,fps=30,setsar=1`). A clip **shorter** than its window is extended with `tpad=stop_mode=clone`; a longer one is `-t` trimmed. Decrease+pad (never crop) preserves the full 9:16 keyframe framing. 2. **Static-still fallback.** For any scene whose `clip` is missing or failed to render, the scene's `keyframe` PNG is looped (`-loop 1`) for `target_sec`, so the master always assembles. Fallback scenes are printed at the end. 3. **Identical re-encode + concat.** Every segment is re-encoded `libx264 -crf 18 -pix_fmt yuv420p -r 30` even if already correct — a dims/framerate mismatch makes the concat demuxer silently drop frames — then concatenated via the concat demuxer (`-c copy`). 4. **Audio.** Restyle: `source_audio` muxed verbatim (`-map 0:v -map 1:a`), clamped to the video length. Original: per-scene VO track (optional `atempo`, `apad`, `-t` clamp) → loudnorm → optionally mixed under the music bed. 5. **Captions last.** `make_captions.py` emits a libass `.ass` (one cue per scene, `start = scene_start + 0.08s`, suppressed on any scene with no caption — e.g. a product/end-card beat carrying its own typeset copy). `compose.py` burns it as the final filter so captions sit on top. Word-level energy-pop captions (Whisper on the narration) are the recipe's upstream option — produce that `.ass` externally and point `captions_ass` at it; compose burns whatever `.ass` it's handed.
## Scripts (free — Python + ffmpeg, no bash, no paid calls)
- `scripts/make_captions.py` — emits the per-scene libass `.ass` from the SAME scene table compose reads, so caption windows stay in lockstep with the cut. Run before `compose.py` (or leave `captions_ass` unset / pointing at nothing to skip captions). - `scripts/compose.py` — the assembler: per-scene trim + identical 1080×1920/30fps re-encode (static-still fallback on missing clips) → concat → audio (restyle verbatim / original mix) → burn captions → master mp4. - `scripts/config.example.json` — the shape of the `config` the recipe binds (the brand-neutralised "Six Types" restyle values as a worked reference).
## Inputs (all via `--config` + a runtime work dir — NO hardcoded paths)
`config.json` carries: `audio_mode` (`restyle` | `original`), `scenes[]` (each `{id, clip, keyframe, target_sec, caption?, vo?, atempo?}` where `target_sec` is the source-inherited window in restyle mode or the **measured** VO window in original mode, and `keyframe` is the static-still fallback source), `source_audio` (restyle), `music_bed` + `music_volume` + `atempo` (original), `width`/`height` (default 1080×1920), `pad_color` (letterbox colour), `captions_ass`, and `caption_style`. See `config.example.json`.
## Craft rules (load-bearing — faithful to the source molecule + reference run)
- **Restyle inherits the source timing.** A restyle reuses the source ad's exact audio, scene order, and per-beat durations verbatim; only an original-mode remix authors its own VO + timing table. Merge any sub-1.5s flash scene into a neighbour upstream to avoid a dead micro-cut (the reference folded scene 7 into scene 8). - **Normalize decrease+pad, never crop** — the listicle's cast-reveal + per-persona framing must not lose edges; letterbox-pad to the canvas colour instead. Re-encode every segment to 30fps before concat, even if already correct, or the concat demuxer silently drops frames. - **Static-still fallback is mandatory** — Kling can 403 mid-run (a billing wall after a burst of successes, not a rate limit). Any failed clip loops its keyframe so the master still assembles; re-roll only the missing beat and recompose (free). - **`generate_audio` was false upstream** — Kling would otherwise invent its own dialog track; the real narration is muxed here separately. (This is the recipe's upstream call, not this capability.) - **No AI-rendered brand text** — the product-beat keyframe shows a BLANK-label box; the real wordmark/end-card copy is composited upstream, never AI-drawn. Suppress captions on any product/end-card beat (its typeset copy carries the message — two text layers at one spot are both unreadable). - **Caption `start = scene_start + 0.08s`** (avoids the caption flashing a frame before a cut).
## Requires
`watch` (QC the final master — the human protagonist reads as the SAME person every scene (wardrobe/hair/lighting held), each persona is on-model, the cast-reveal lineup matches the N list items, the product beat shows the REAL box, narration lands beat-for-beat, and duration is within ±0.1s of the summed windows). The recipe gates the paid `create-image-fal` (cast anchors + keyframes), `create-video-fal` (Kling-V3 i2v), `create-vo-elevenlabs`, and `create-music-elevenlabs` calls to their own capabilities — this capability itself makes NO paid calls.
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Scenario-led draft for render-3d-character-explainer, ready for a manual X post.
render-3d-character-explainer: Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types... 1.2K stars https://www.openagentskill.com/skills/gooseworks-ai-render-3d-character-explainer?ref=x
Listing + install path for render-3d-character-explainer: https://www.openagentskill.com/skills/gooseworks-ai-render-3d-character-explainer?ref=x Install: npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainer
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mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
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Run autonomous deep research over web and local sources
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Install the "render-3d-character-explainer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-3d-character-explainer. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a narration track, it trims each clip to its scene window, re-encodes every segment to identical 1080x1920/30fps/libx264/yuv420p (decrease+pad, never crop) so the concat demuxer never drops frames, concats, and muxes audio — in RESTYLE mode the source ad's VO+music mix is reused verbatim, in ORIGINAL mode fresh per-scene VO (loudnorm I=-14) is mixed under an optional music bed (loudnorm I=-26). A static-still fallback loops a scene's keyframe when its clip is missing/failed, so the master always assembles; libass captions are burned last. FREE deterministic assembly (Python + ffmpeg, no bash, no paid calls); the recipe supplies the clips, keyframes, VO or source audio, and caption table and gates the paid cast-anc After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"gooseworks-ai-render-3d-character-explainer","task":"Install render-3d-character-explainer","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
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Claude Code
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PASS4d since push
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Review before install
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Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
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Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Process rich media
I need my agent to process images, video, or audio and extract useful information.
Workflow fit
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Brief to publish-ready creative
A practical workflow for agents that shape a video brief, create strong multimodal prompts, generate supporting B-roll, and prepare a reviewable short-form or explainer video.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: render-3d-character-explainer description: Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a narration track, it trims each clip to its scene window, re-encodes every segment to identical 1080x1920/30fps/libx264/yuv420p (decrease+pad, never crop) so the concat demuxer never drops frames, concats, and muxes audio — in RESTYLE mode the source ad's VO+music mix is reused verbatim, in ORIGINAL mode fresh per-scene VO (loudnorm I=-14) is mixed under an optional music bed (loudnorm I=-26). A static-still fallback loops a scene's keyframe when its clip is missing/failed, so the master always assembles; libass captions are burned last. FREE deterministic assembly (Python + ffmpeg, no bash, no paid calls); the recipe supplies the clips, keyframes, VO or source audio, and caption table and gates the paid cast-anchor/keyframe/Kling-i2v/VO/music calls to their own capabilities. Use for the 3d-character-explainer listicle format. status: active ---
# render-3d-character-explainer
The free, deterministic renderer for the **3d-character-explainer** video ad format — the glossy Pixar-style 3D spot built on an **"N types of X" listicle** spine, where a recurring human protagonist plus a locked cast of **N persona characters (one per list item)** carry a hook → "deeper story" → cast-reveal → one beat per list item → kicker → product test → relieved payoff. This capability is the **FREE assembly stage only**. All generative work (Nano-Banana cast anchors + per-scene keyframes, Kling-V3 i2v clips, ElevenLabs VO + music, or a source ad's audio reused verbatim) happens upstream in the recipe and is handed to this capability as files.
It ports the validated compose recipe from the Bristle "Six Types" restyle run (`_render_full.sh` — per-scene trim → normalize 1080×1920/fps30 → concat -c copy → mux the source audio, with a static-still fallback on any failed clip). The assembly is deterministic — iterate the cut for free, re-roll only the offending paid beat.
## Two modes
- **Restyle mode** (`audio_mode: "restyle"`, the reference run) — re-tell a finished source ad, beat for beat, as 3D character comedy. The source ad's **audio mix (VO + music bed) is reused VERBATIM** (`source_audio`), and the per-scene `target_sec` table is inherited from the source's scene timing. No new VO or music is rendered. The trims must sum to the source audio length. - **Original mode** (`audio_mode: "original"`) — the ad authors its own narration. Each scene carries a measured VO cue (`scenes[].vo`, `target_sec` = the ffprobe'd VO duration) which is concatenated into a VO track (loudnorm I=-14) and optionally mixed under a `music_bed` (loudnorm I=-26 then `volume`, `amix normalize=0`).
## What it does (the deterministic recipe)
1. **Per-scene retime.** Each i2v clip is trimmed to its scene `target_sec` and normalized to identical dims/fps/SAR (`scale=W:H:force_original_aspect_ratio=decrease,pad=W:H:(ow-iw)/2:(oh-ih)/2:color=<pad>,fps=30,setsar=1`). A clip **shorter** than its window is extended with `tpad=stop_mode=clone`; a longer one is `-t` trimmed. Decrease+pad (never crop) preserves the full 9:16 keyframe framing. 2. **Static-still fallback.** For any scene whose `clip` is missing or failed to render, the scene's `keyframe` PNG is looped (`-loop 1`) for `target_sec`, so the master always assembles. Fallback scenes are printed at the end. 3. **Identical re-encode + concat.** Every segment is re-encoded `libx264 -crf 18 -pix_fmt yuv420p -r 30` even if already correct — a dims/framerate mismatch makes the concat demuxer silently drop frames — then concatenated via the concat demuxer (`-c copy`). 4. **Audio.** Restyle: `source_audio` muxed verbatim (`-map 0:v -map 1:a`), clamped to the video length. Original: per-scene VO track (optional `atempo`, `apad`, `-t` clamp) → loudnorm → optionally mixed under the music bed. 5. **Captions last.** `make_captions.py` emits a libass `.ass` (one cue per scene, `start = scene_start + 0.08s`, suppressed on any scene with no caption — e.g. a product/end-card beat carrying its own typeset copy). `compose.py` burns it as the final filter so captions sit on top. Word-level energy-pop captions (Whisper on the narration) are the recipe's upstream option — produce that `.ass` externally and point `captions_ass` at it; compose burns whatever `.ass` it's handed.
## Scripts (free — Python + ffmpeg, no bash, no paid calls)
- `scripts/make_captions.py` — emits the per-scene libass `.ass` from the SAME scene table compose reads, so caption windows stay in lockstep with the cut. Run before `compose.py` (or leave `captions_ass` unset / pointing at nothing to skip captions). - `scripts/compose.py` — the assembler: per-scene trim + identical 1080×1920/30fps re-encode (static-still fallback on missing clips) → concat → audio (restyle verbatim / original mix) → burn captions → master mp4. - `scripts/config.example.json` — the shape of the `config` the recipe binds (the brand-neutralised "Six Types" restyle values as a worked reference).
## Inputs (all via `--config` + a runtime work dir — NO hardcoded paths)
`config.json` carries: `audio_mode` (`restyle` | `original`), `scenes[]` (each `{id, clip, keyframe, target_sec, caption?, vo?, atempo?}` where `target_sec` is the source-inherited window in restyle mode or the **measured** VO window in original mode, and `keyframe` is the static-still fallback source), `source_audio` (restyle), `music_bed` + `music_volume` + `atempo` (original), `width`/`height` (default 1080×1920), `pad_color` (letterbox colour), `captions_ass`, and `caption_style`. See `config.example.json`.
## Craft rules (load-bearing — faithful to the source molecule + reference run)
- **Restyle inherits the source timing.** A restyle reuses the source ad's exact audio, scene order, and per-beat durations verbatim; only an original-mode remix authors its own VO + timing table. Merge any sub-1.5s flash scene into a neighbour upstream to avoid a dead micro-cut (the reference folded scene 7 into scene 8). - **Normalize decrease+pad, never crop** — the listicle's cast-reveal + per-persona framing must not lose edges; letterbox-pad to the canvas colour instead. Re-encode every segment to 30fps before concat, even if already correct, or the concat demuxer silently drops frames. - **Static-still fallback is mandatory** — Kling can 403 mid-run (a billing wall after a burst of successes, not a rate limit). Any failed clip loops its keyframe so the master still assembles; re-roll only the missing beat and recompose (free). - **`generate_audio` was false upstream** — Kling would otherwise invent its own dialog track; the real narration is muxed here separately. (This is the recipe's upstream call, not this capability.) - **No AI-rendered brand text** — the product-beat keyframe shows a BLANK-label box; the real wordmark/end-card copy is composited upstream, never AI-drawn. Suppress captions on any product/end-card beat (its typeset copy carries the message — two text layers at one spot are both unreadable). - **Caption `start = scene_start + 0.08s`** (avoids the caption flashing a frame before a cut).
## Requires
`watch` (QC the final master — the human protagonist reads as the SAME person every scene (wardrobe/hair/lighting held), each persona is on-model, the cast-reveal lineup matches the N list items, the product beat shows the REAL box, narration lands beat-for-beat, and duration is within ±0.1s of the summed windows). The recipe gates the paid `create-image-fal` (cast anchors + keyframes), `create-video-fal` (Kling-V3 i2v), `create-vo-elevenlabs`, and `create-music-elevenlabs` calls to their own capabilities — this capability itself makes NO paid calls.
Source provenance
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1,195 GitHub stars
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No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
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Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for render-3d-character-explainer, ready for a manual X post.
render-3d-character-explainer: Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types... 1.2K stars https://www.openagentskill.com/skills/gooseworks-ai-render-3d-character-explainer?ref=x
Listing + install path for render-3d-character-explainer: https://www.openagentskill.com/skills/gooseworks-ai-render-3d-character-explainer?ref=x Install: npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainer
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[](https://www.openagentskill.com/skills/gooseworks-ai-render-3d-character-explainer?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)gooseworks-ai
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mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
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Codex install prompt
Install the "render-3d-character-explainer" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-3d-character-explainer. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a narration track, it trims each clip to its scene window, re-encodes every segment to identical 1080x1920/30fps/libx264/yuv420p (decrease+pad, never crop) so the concat demuxer never drops frames, concats, and muxes audio — in RESTYLE mode the source ad's VO+music mix is reused verbatim, in ORIGINAL mode fresh per-scene VO (loudnorm I=-14) is mixed under an optional music bed (loudnorm I=-26). A static-still fallback loops a scene's keyframe when its clip is missing/failed, so the master always assembles; libass captions are burned last. FREE deterministic assembly (Python + ffmpeg, no bash, no paid calls); the recipe supplies the clips, keyframes, VO or source audio, and caption table and gates the paid cast-anc After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"gooseworks-ai-render-3d-character-explainer","task":"Install render-3d-character-explainer","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
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Ready
npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainer
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fresh
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Financial research output is not financial advice; require human review before any live investment decision
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Financial research output is not financial advice; require human review before any live investment decision · The skill description claims 'no bash' but the smoke test uses bash commands for setup; this is a minor inconsistency.
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Stars
1.2K GitHub stars
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1.2K stars, 206 forks
Maintenance
4d since push
License
MIT
Install
npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainer
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npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainerDo not use when
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1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
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61.0K Stars
npx skills add mvanhorn/last30days-skill -g
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38.4K Stars
npx skills add Imbad0202/academic-research-skills
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28.0K Stars
npx skills add assafelovic/gpt-researcher
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high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
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medium
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Task: Use render-3d-character-explainer in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20render-3d-character-explainer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/gooseworks-ai-render-3d-character-explainer/install
Install command: npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainer
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Use render-3d-character-explainer for this task. Review https://www.openagentskill.com/api/skills/gooseworks-ai-render-3d-character-explainer/install, then install with: npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainerRegistry metadata
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Agent fit
Browser automation
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Claude Code
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INFO1.2K stars, 206 forks; issue activity unavailable in current metadata
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PASS4d since push
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Review before install
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Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Operate web apps
I need my agent to control a browser, fill forms, and verify web app workflows.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Process rich media
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Workflow fit
Operate and verify web apps
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Brief to publish-ready creative
A practical workflow for agents that shape a video brief, create strong multimodal prompts, generate supporting B-roll, and prepare a reviewable short-form or explainer video.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: render-3d-character-explainer description: Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a narration track, it trims each clip to its scene window, re-encodes every segment to identical 1080x1920/30fps/libx264/yuv420p (decrease+pad, never crop) so the concat demuxer never drops frames, concats, and muxes audio — in RESTYLE mode the source ad's VO+music mix is reused verbatim, in ORIGINAL mode fresh per-scene VO (loudnorm I=-14) is mixed under an optional music bed (loudnorm I=-26). A static-still fallback loops a scene's keyframe when its clip is missing/failed, so the master always assembles; libass captions are burned last. FREE deterministic assembly (Python + ffmpeg, no bash, no paid calls); the recipe supplies the clips, keyframes, VO or source audio, and caption table and gates the paid cast-anchor/keyframe/Kling-i2v/VO/music calls to their own capabilities. Use for the 3d-character-explainer listicle format. status: active ---
# render-3d-character-explainer
The free, deterministic renderer for the **3d-character-explainer** video ad format — the glossy Pixar-style 3D spot built on an **"N types of X" listicle** spine, where a recurring human protagonist plus a locked cast of **N persona characters (one per list item)** carry a hook → "deeper story" → cast-reveal → one beat per list item → kicker → product test → relieved payoff. This capability is the **FREE assembly stage only**. All generative work (Nano-Banana cast anchors + per-scene keyframes, Kling-V3 i2v clips, ElevenLabs VO + music, or a source ad's audio reused verbatim) happens upstream in the recipe and is handed to this capability as files.
It ports the validated compose recipe from the Bristle "Six Types" restyle run (`_render_full.sh` — per-scene trim → normalize 1080×1920/fps30 → concat -c copy → mux the source audio, with a static-still fallback on any failed clip). The assembly is deterministic — iterate the cut for free, re-roll only the offending paid beat.
## Two modes
- **Restyle mode** (`audio_mode: "restyle"`, the reference run) — re-tell a finished source ad, beat for beat, as 3D character comedy. The source ad's **audio mix (VO + music bed) is reused VERBATIM** (`source_audio`), and the per-scene `target_sec` table is inherited from the source's scene timing. No new VO or music is rendered. The trims must sum to the source audio length. - **Original mode** (`audio_mode: "original"`) — the ad authors its own narration. Each scene carries a measured VO cue (`scenes[].vo`, `target_sec` = the ffprobe'd VO duration) which is concatenated into a VO track (loudnorm I=-14) and optionally mixed under a `music_bed` (loudnorm I=-26 then `volume`, `amix normalize=0`).
## What it does (the deterministic recipe)
1. **Per-scene retime.** Each i2v clip is trimmed to its scene `target_sec` and normalized to identical dims/fps/SAR (`scale=W:H:force_original_aspect_ratio=decrease,pad=W:H:(ow-iw)/2:(oh-ih)/2:color=<pad>,fps=30,setsar=1`). A clip **shorter** than its window is extended with `tpad=stop_mode=clone`; a longer one is `-t` trimmed. Decrease+pad (never crop) preserves the full 9:16 keyframe framing. 2. **Static-still fallback.** For any scene whose `clip` is missing or failed to render, the scene's `keyframe` PNG is looped (`-loop 1`) for `target_sec`, so the master always assembles. Fallback scenes are printed at the end. 3. **Identical re-encode + concat.** Every segment is re-encoded `libx264 -crf 18 -pix_fmt yuv420p -r 30` even if already correct — a dims/framerate mismatch makes the concat demuxer silently drop frames — then concatenated via the concat demuxer (`-c copy`). 4. **Audio.** Restyle: `source_audio` muxed verbatim (`-map 0:v -map 1:a`), clamped to the video length. Original: per-scene VO track (optional `atempo`, `apad`, `-t` clamp) → loudnorm → optionally mixed under the music bed. 5. **Captions last.** `make_captions.py` emits a libass `.ass` (one cue per scene, `start = scene_start + 0.08s`, suppressed on any scene with no caption — e.g. a product/end-card beat carrying its own typeset copy). `compose.py` burns it as the final filter so captions sit on top. Word-level energy-pop captions (Whisper on the narration) are the recipe's upstream option — produce that `.ass` externally and point `captions_ass` at it; compose burns whatever `.ass` it's handed.
## Scripts (free — Python + ffmpeg, no bash, no paid calls)
- `scripts/make_captions.py` — emits the per-scene libass `.ass` from the SAME scene table compose reads, so caption windows stay in lockstep with the cut. Run before `compose.py` (or leave `captions_ass` unset / pointing at nothing to skip captions). - `scripts/compose.py` — the assembler: per-scene trim + identical 1080×1920/30fps re-encode (static-still fallback on missing clips) → concat → audio (restyle verbatim / original mix) → burn captions → master mp4. - `scripts/config.example.json` — the shape of the `config` the recipe binds (the brand-neutralised "Six Types" restyle values as a worked reference).
## Inputs (all via `--config` + a runtime work dir — NO hardcoded paths)
`config.json` carries: `audio_mode` (`restyle` | `original`), `scenes[]` (each `{id, clip, keyframe, target_sec, caption?, vo?, atempo?}` where `target_sec` is the source-inherited window in restyle mode or the **measured** VO window in original mode, and `keyframe` is the static-still fallback source), `source_audio` (restyle), `music_bed` + `music_volume` + `atempo` (original), `width`/`height` (default 1080×1920), `pad_color` (letterbox colour), `captions_ass`, and `caption_style`. See `config.example.json`.
## Craft rules (load-bearing — faithful to the source molecule + reference run)
- **Restyle inherits the source timing.** A restyle reuses the source ad's exact audio, scene order, and per-beat durations verbatim; only an original-mode remix authors its own VO + timing table. Merge any sub-1.5s flash scene into a neighbour upstream to avoid a dead micro-cut (the reference folded scene 7 into scene 8). - **Normalize decrease+pad, never crop** — the listicle's cast-reveal + per-persona framing must not lose edges; letterbox-pad to the canvas colour instead. Re-encode every segment to 30fps before concat, even if already correct, or the concat demuxer silently drops frames. - **Static-still fallback is mandatory** — Kling can 403 mid-run (a billing wall after a burst of successes, not a rate limit). Any failed clip loops its keyframe so the master still assembles; re-roll only the missing beat and recompose (free). - **`generate_audio` was false upstream** — Kling would otherwise invent its own dialog track; the real narration is muxed here separately. (This is the recipe's upstream call, not this capability.) - **No AI-rendered brand text** — the product-beat keyframe shows a BLANK-label box; the real wordmark/end-card copy is composited upstream, never AI-drawn. Suppress captions on any product/end-card beat (its typeset copy carries the message — two text layers at one spot are both unreadable). - **Caption `start = scene_start + 0.08s`** (avoids the caption flashing a frame before a cut).
## Requires
`watch` (QC the final master — the human protagonist reads as the SAME person every scene (wardrobe/hair/lighting held), each persona is on-model, the cast-reveal lineup matches the N list items, the product beat shows the REAL box, narration lands beat-for-beat, and duration is within ±0.1s of the summed windows). The recipe gates the paid `create-image-fal` (cast anchors + keyframes), `create-video-fal` (Kling-V3 i2v), `create-vo-elevenlabs`, and `create-music-elevenlabs` calls to their own capabilities — this capability itself makes NO paid calls.
Source provenance
Decision snapshot
1,195 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for render-3d-character-explainer, ready for a manual X post.
render-3d-character-explainer: Assemble a glossy 3D-character animated-explainer video ad (~77s, 9:16) built on an "N types... 1.2K stars https://www.openagentskill.com/skills/gooseworks-ai-render-3d-character-explainer?ref=x
Listing + install path for render-3d-character-explainer: https://www.openagentskill.com/skills/gooseworks-ai-render-3d-character-explainer?ref=x Install: npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainer
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@gooseworks-ai
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mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsPermission surface
shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness