Guizang Social Cards
op7418/guizang-social-card-skill
Produces social card sets for Xiaohongshu and WeChat: carousels, Live Photo motion cards and puzzle layouts, and WeChat cover pairs, rendered from single-file HTML.
Write a street-interview ad from a complete inspected commercial interaction and current brand facts.
$ npx skills add gooseworks-ai/goose-skills --skill render-street-interview -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install gooseworks-ai/goose-skills render-street-interview --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ads/capabilities/render-street-interview .claude/skills/render-street-interview && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "render-street-interview" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-street-interview into .claude/skills/render-street-interview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "render-street-interview", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-street-interviewType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add gooseworks-ai/goose-skills --skill render-street-interview -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install gooseworks-ai/goose-skills render-street-interview --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ads/capabilities/render-street-interview .agents/skills/render-street-interview && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "render-street-interview" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-street-interview into .agents/skills/render-street-interview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "render-street-interview", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gooseworks-ai/goose-skills --skill render-street-interview -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install gooseworks-ai/goose-skills render-street-interview --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ads/capabilities/render-street-interview .cursor/skills/render-street-interview && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "render-street-interview" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-street-interview into .cursor/skills/render-street-interview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "render-street-interview", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/gooseworks-ai/goose-skills.git --path skills/ads/capabilities/render-street-interview--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add gooseworks-ai/goose-skills --skill render-street-interview -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install gooseworks-ai/goose-skills render-street-interview --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ads/capabilities/render-street-interview .gemini/skills/render-street-interview && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "render-street-interview" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-street-interview into .gemini/skills/render-street-interview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "render-street-interview", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install gooseworks-ai/goose-skills render-street-interviewInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add gooseworks-ai/goose-skills --skill render-street-interview -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ads/capabilities/render-street-interview .github/skills/render-street-interview && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "render-street-interview" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-street-interview into .github/skills/render-street-interview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "render-street-interview", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gooseworks-ai/goose-skills --skill render-street-interview -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install gooseworks-ai/goose-skills render-street-interview --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ads/capabilities/render-street-interview .opencode/skills/render-street-interview && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "render-street-interview" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-street-interview into .opencode/skills/render-street-interview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "render-street-interview", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
render-street-interviewWrite a street-interview ad from a complete inspected commercial interaction and current brand facts.
Render Street Interview is an agent skill from gooseworks-ai/goose-skills. Write a street-interview ad from a complete inspected commercial interaction and current brand facts. Render product guessing, or prepare mic-only, prepared-sample or visible-task conversation script/prompt previews. Premise, actions and ad connection vary; capture rules stay fixed. Conversation media delivery is unverified.
Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 80 other files, including scripts and reference files (for example `READINESS.md`, `REFERENCE.md` and `TAKES.md`).
It sits in Media & Creative. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.
Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
pythonFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
docs.byteplus.comfal.aiFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Render Street Interview loads about 4k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 88 tokens; SKILL.md has 2,034 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 2,034 words, ~4,007 tokens.
.claude/skills/render-street-interview/SKILL.md (or your agent's skills folder). This skill also uses 79 other files; get the full folder from GitHub.Summary. This is the renderer for the street-interview ad format (goose-studio
recipe one-shot-videos/create-street-interview-video). Product guessing retains its
visible object and reveal. Conversation supports mic-only, prepared-sample and visible-task
script/prompt previews. Choose a coherent situation and earned ad connection before
writing words. People are generated; conversation media delivery remains unverified.
Read street-script-writing before adapting a brand. The recipe fixes camera, audio, native timing and finishing rules. Its story choices govern premise, participant role, participation reason, visible task, actions, edited opening, hook, supported brand explanation and payoff. Do not force a correct-answer winner into a service conversation or reduce every brand to a routine problem followed by a logo card.
| Interaction | Current support | Person image reference | Max participants |
|---|---|---|---|
product-guess | Existing object renderer; standalone product reference required | forbidden | 4 |
mic-only | Conversation preview; service explanation without a product or device | forbidden | 1 |
product-sample | Conversation preview; prepared sample in a plain cup, no exact package reference | forbidden | 1 |
concept-challenge | Conversation preview; described visible task using non-UI props | forbidden | 1 |
Use [[composes::write-video-ad-script]] with the scoped angle bank, current facts and
selected complete commercial street interactions. scripts/prepare_script_context.py
requires a brief with offering_type=physical|service|digital and
interaction_type=product-guess|mic-only|product-sample|concept-challenge. Inspect the full
visual timeline and spoken exchange: setup, participation reason, hook, product role and
payoff. Record unseen recruitment or setup as unknown; inference is not observation.
Seed snippets are leads. Radio and editorial exchanges cannot fill a street-ad gap.
Keep private observations project-scoped and transfer mechanics, not source-brand claims.
Pass participants too: the number of people interviewed on screen, not counting the
interviewer (default 4 for product-guess, 1 for conversation). Its route output is binding
for any project built on this format, including custom ones. unsupported-route means stop
and offer the listed alternatives; it is not permission to go custom. custom is one of those
alternatives only when the customer picks it, and a custom video still keeps every route
constraint: people in text, no person image references, one take, deep focus, local lettering.
Save a situation brief before dialogue. Write the user's requested count, then map words
and actions to ordered shots. An edited participant answer or silent action/reaction may
open the ad. cfg.question mirrors the first actual interviewer question, spoken once.
Keep both spoken voices and 3–8 shots in 6–15 seconds, at no more than 2.5 spoken words/s;
leave time for actions. New configs describe interaction.type, visible_setup,
participant_reason and optional props; missing interaction defaults to mic-only.
No forced greeting/consent speech, invented use history or instant product efficacy.
conversation currently runs config validation and prompt previews. single_gen.py --yes
refuses that mode until a rendered pilot is validated. Natural speech, audio and camera
performance remain unverified. The existing product-guess render path is preserved.
All conversation subtypes refuse product/scene reference bindings and phone, screen or UI
demonstrations. Dry-run success is not a performed sample, challenge or finished video.
Read the bundled model notes before generation.
If a required guide cannot be fetched or opened, stop before spending and name it.
REFERENCE.md holds the format's historical Critical knowledge entries and
the rejected takes behind them. Read it before changing the prompt scaffold or a gate;
its older experiments do not override the current recipe or this entry.
Use the project take-ledger guidance before reusing a seed. Keep each
brand's observed successes and limitations in its own project; a seed is not a quality guarantee.
The paid generation and finishing commands below are for product-guess. Conversation
supports brandkit.py validation and single_gen.py dry runs only.
Run everything from the project the video belongs to. Brand-asset paths in the configs
(logo, product photo, end-card sting) resolve against that folder, or $STREET_INTERVIEW_ROOT.
The run folder is --run <dir> (default projects/street-interview/), with working/ for
intermediates and output/ for deliverables.
python scripts/selftest.py # free: the format and the lint hold
python scripts/single_gen.py --brand <slug> # dry run: price + the full prompt
python scripts/single_gen.py --brand <slug> --seed <n> --yes # PAID: one take (~$3.64 at 12 s, 720p)
python scripts/build_episode.py --episode <name> # free: grade, re-cut, captions, end card
python scripts/check-cut.py --episode <render>.episode.json # free: the ship gatebrands/<slug>.json holds the product and its reference photo, the
street, the question, the cast and their lines, props, captions, logo and end card. Copy
brands/demo-tallgrass-oat.json. No brand appears in format_spec.py.episodes/<name>.json) joins three takes into a ~25-30 s cut. It names the takes,
any whole shots to drop (drop_shots, each pair a real shot's start and end), the brand layer
and, optionally, brand_layer.end_card_music, a short sting played under the end card.scripts/media_proxy.py), never a local key.
On a poll timeout, resume with media_proxy.resume_fal(request_id). Never resubmit, since a
dropped poll has already been billed.single_gen.py --yes exit 3: surface the reason, do not retry. The take's input digest
covers every input by content (prompt, settings, seed, and the sha256 of the product image
and the scene reference), so the unchanged request is refused before anything is uploaded,
and a changed image, prompt or seed is sent as a new request.The final Seedance prompt is built from the project brief and shared shot instructions. BytePlus recommends at most 1,000 English words because lengthy prompts may miss details. This is quality guidance. The current Fal schema declares no maximum prompt length; that does not prove unlimited acceptance.
single_gen.py prints a non-blocking advisory above that guideline. The old 1,200-word
refusal is removed: its source-run observation did not prove a precise boundary. Keep the
exact approved dialogue and required clauses; do not trim them, reduce the cast or add a paid
retry just to meet a count. Missing clauses, invalid inputs and existing spend approval still
block generation. The finished-cut gate reports length as advice, without failing on it.
Review actual video adherence through the normal gate and full watch/listen pass.
single_gen.py lints it before any spend, and
check-cut.py imports the same clause list, so a clause cannot be dropped silently.A product-guess take is about $3.64 (12 s at 720p, $0.3034/s); a 30 s episode is three takes, about $10.92. Grading, re-cut, captions, looks and every gate are free. Staging prices move, so price the first call of a run and quote from that. Conversation prompt previews send no media call; no conversation media price is verified.
The local finishing scripts use the current Python interpreter and carry --run into child commands. The default grade (--strength 0) needs no colour-reference file. A positive strength requires the real reference.
Set approved colours in brand_layer.palette: accent, text, and background, each #RRGGBB or three RGB integers. Optional brand_layer.fonts keys are black, bold, and regular (paths relative to the project). Without overrides, fonts resolve on macOS, Windows or Linux. End-card rows keep the approved 86px spacing and 66px type. Shrink only an individual line whose visible text cannot fit the safe area; shorten copy if that line still cannot fit. Transparent text-image padding does not set the line spacing.
The subway series bar stays visible through caption gaps. It is also in the caption-free control so the gate measures captions separately from persistent branding.
For a new prompt, use generation.prompt_version: 3 (or --prompt-version 3). It keeps version 2's repairs (no duplicate articles, a top-edge rule for non-can packaging) and keeps the street in focus behind the people: never blurred and never bokeh (see REFERENCE #8). Version 3 is draft until a 720p take measures inside the real-footage detail band; its paid validation take needs its own approval. The bundled demo config stays on version 2 until then. The manifest records the version for the gate. Historical prompts default to version 1, and versions 1 and 2 retain their hashes. Use a new approved seed for a new prompt; do not overwrite an approved take.
Check local finishing prerequisites before buying a take: ffmpeg/ffprobe, PIL, NumPy,
fonts, and local Whisper with its base model available. The existing episode transcription
helper can download an absent Whisper model; prepare it separately before spend. This fix does
not call another video or voice model. A reused take may supply previously measured original
word times instead of transcribing again.
Keep the original take and generation manifest. Measure its internal cuts, caption source spans
and a genuinely speech-free ambience window in brand_layer; these remain source seconds.
Remove dead air locally, then add the approved brand layer and end card:
python scripts/recut.py <original-take.mp4> <run>/working/interview-recut.mp4 --brand <slug>
python scripts/build_looks.py --brand <slug> --run <run> --looks <look> \
--edit-map <run>/working/interview-recut.plan.json
python scripts/check-cut.py --brand <slug> --run <run> --look <look> \
--edit-map <run>/working/interview-recut.plan.json --take <original-take.mp4> --falsify
# Repeat the same gate without --falsify, then watch and listen to the entire finished file.recut.py emits the source-span .plan.json beside its edited output. build_looks.py consumes
that file, measures source words with the existing free local Whisper helper and saves
interview-recut.plan.words.json. To reuse saved timing, pass --word-times <file> to finishing
and the gate. Its JSON names the original source and contains ordered [start, end, word]
rows. It must come from actual source audio, not estimates from the script. Missing transcription
or incompatible timing stops finishing; there is no fallback to the original caption schedule.
Recut masters and caption-free controls have separate -recut- names. The original takes,
controls and episode outputs stay intact. Ambience is extracted from the measured quiet window
of the original source even if that window was dropped; only that WAV is looped. If the recut
already applied its continuous bed, finishing does not mix it a second time. The end card always
uses original room tone. The same mapped cuts clamp captions with a half-open end boundary so
one person's last words do not appear on the next shot's first frame.
build.py remains an intermediate grade-and-recut helper. To finish its already graded output,
use its printed map with build_looks.py --pregraded; do not grade it twice. A changed spoken
line, story, paid retry or generation count still needs its existing review and approval. Run the
normal finished-file gate and full watch/listen review; a synthetic timing test does not approve
faces or the original customer's ad.
Free regression checks:
python -m unittest discover -s tests -p 'test_*.py' -vSix generation keys on the brand config, all off by default, so every recorded prompt and its
hash are unchanged. They were paid for on Olipop and Graza (REFERENCE.md items 37-42); start a new
brand from brands/olipop-ep3-a.json (a can) or brands/graza-ep1-a2.json (a bottle).
| key | what it does |
|---|---|
handover_grammar | every person's first shot opens on the interviewer passing the product |
natural_grammar | the product is held like a drink someone was just handed, not presented |
real_grammar | chest-height relaxed hold, one consistent interviewer, nothing printed carried |
level_camera | removes the glance down from speaking shots, which is what tips the product |
no_signage | no shops, signs or lettering behind the cast (names park-corner landmarks) |
can_sealed_grammar | existing flag; holds with the keys above, so the can stays closed |
Episode files take extra_head_s (seconds of run-up kept at the head of each take, so the
handover is not trimmed as silence) and brand_layer.caption_centre_y. Transcribe every take with
word timestamps before assembly: a fast take can repeat, slur or invent a line.
REFERENCE.md.--scene-ref) couples later takes to its mic, street and light. Check by eye.Ported from goose-studio skills/molecules/create-street-interview-video on 2026-10-02,
including the episode-2 v3 fixes (hook caption, the mispronounced line, the silent end card).
The port changed only path resolution and routed the paid calls through the proxy.
selftest.py passes from an empty folder, and episode 2 v3 rebuilds identically
(11 shots, 22.64 s).
© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 79 other files (scripts, references) in skills/ads/capabilities/render-street-interview of gooseworks-ai/goose-skills.
Open the folder on GitHubat commit c650c6d
Render Street Interview next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Render Street Interview this skillgooseworks-ai/goose-skills | 1.2k | — | ~4k | Automated safety check: Pass | MIT | |
| Guizang Social Cardsop7418/guizang-social-card-skill | 7.4k | 1 repos | ~7.8k | Automated safety check: Pass | AGPL-3.0 | |
| Weekly Changelog Videoheygen-com/hyperframes | 60k | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | |
| Anthropic Brand Stylinganthropics/skills | 180k | 30 repos | ~559 | Automated safety check: Pass | Apache-2.0 | |
| MoneyPrinterTurbo Video Generatorharry0703/MoneyPrinterTurbo | 129k | — | ~2.1k | Automated safety check: Warn | MIT | |
| HyperFrames Media Useheygen-com/hyperframes | 60k | — | ~2.4k | Automated safety check: Pass | Apache-2.0 |
op7418/guizang-social-card-skill
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heygen-com/hyperframes
Turns a weekly changelog markdown file into a branded HyperFrames video with voiceover, animated mock-UI scenes and captions, using fonts, background and scripts bundled in the skill.
anthropics/skills
Applies Anthropic's brand colors and fonts to artifacts such as PowerPoint slides, using fixed hex values for text and accents, Poppins headings and Lora body text.
harry0703/MoneyPrinterTurbo
Installs and runs MoneyPrinterTurbo to turn a topic or script into a finished short video with voice-over, subtitles, stock footage and music.
heygen-com/hyperframes
Finds, generates and edits media for HyperFrames video projects: music, sound effects, images, icons, logos, voiceovers, captions and color grades.
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Generate or edit an image via any FAL image model (nano-banana edit, gpt-image, flux, ...), ROUTED THROUGH THE fal-proxy so it bills the Ads agent.
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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.
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Assemble a ChatGPT chat-reveal video ad from a thread + timeline JSON — one continuous Playwright recording of a ChatGPT mobile chat (user types with the iOS keyboard up → taps send → keyboard…
Categories
Write a street-interview ad from a complete inspected commercial interaction and current brand facts. Render Street Interview is an agent skill from gooseworks-ai/goose-skills. Write a street-interview ad from a complete inspected commercial interaction and current brand facts.
Render Street Interview fits situations like: media & Creative work in your project.
Run `npx skills add gooseworks-ai/goose-skills --skill render-street-interview -a claude-code`. Or copy the skill folder (skills/ads/capabilities/render-street-interview in gooseworks-ai/goose-skills) into .claude/skills/render-street-interview in your project. Claude Code loads it when a task matches its description.
Run `npx skills add gooseworks-ai/goose-skills --skill render-street-interview -a codex`. Or copy the skill folder (skills/ads/capabilities/render-street-interview in gooseworks-ai/goose-skills) into .agents/skills/render-street-interview in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gooseworks-ai/goose-skills --skill render-street-interview -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/render-street-interview, .gemini/skills/render-street-interview, .github/skills/render-street-interview and .opencode/skills/render-street-interview in your project.
Going by SKILL.md and its folder, Render Street Interview needs the command-line tools its instructions call (python). Our summary lists: Python 3.
SKILL.md names 2 domains. As links in the text: docs.byteplus.com and fal.ai. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Render Street Interview is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.3k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Render Street Interview: Guizang Social Cards (op7418/guizang-social-card-skill, 7.4k stars), Weekly Changelog Video (heygen-com/hyperframes, 60k stars), Anthropic Brand Styling (anthropics/skills, 180k stars) and MoneyPrinterTurbo Video Generator (harry0703/MoneyPrinterTurbo, 129k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.
Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.