Agent skill

Render Street Interview

by gooseworks-ai in gooseworks-ai/goose-skills

Write a street-interview ad from a complete inspected commercial interaction and current brand facts.

MITAuto-check passedMedia & Creative

Install Render Street Interview

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill render-street-interview -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills render-street-interview --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-src

Use ~/.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/

Facts

Skill name
render-street-interview
GitHub stars
1.2k
Token cost
~4k tokens
SKILL.md length
2,034 words
Files
80 (incl. scripts, references)
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

Write a street-interview ad from a complete inspected commercial interaction and current brand facts.

  • Media & Creative work in your project
  • SKILL.md covers render-street-interview, Script first: choose the…, Run and Prompt length, plus 6 more sections
  • Calls python

What it does

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.

When your agent uses it

  • Media & Creative work in your project

Example prompts

  • “/render-street-interview”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    Ships 1 file in scripts/, which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.byteplus.com
    • fal.ai

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

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.

Always · name and description, kept in context so the agent knows when to use it
~88
When it runs · the whole SKILL.md, loaded when a task matches
~4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~11k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 2,034 words, ~4,007 tokens.

Download SKILL.mdSave it as .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.
name
render-street-interview
description
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.
status
draft
version
2.0.1
updated
2026-10-06

Human version

render-street-interview

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.


Agent version

Script first: choose the execution

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.

InteractionCurrent supportPerson image referenceMax participants
product-guessExisting object renderer; standalone product reference requiredforbidden4
mic-onlyConversation preview; service explanation without a product or deviceforbidden1
product-sampleConversation preview; prepared sample in a plain cup, no exact package referenceforbidden1
concept-challengeConversation preview; described visible task using non-UI propsforbidden1

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.

Run

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.

bash
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 gate
  • Product-guess brand data: brands/<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.
  • An episode (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.
  • Paid calls go through the GooseWorks proxy (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.
  • A provider policy rejection (likeness of a real person, content policy) makes 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.

Prompt length

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.

Guarantees

  • The prompt carries every format clause; single_gen.py lints it before any spend, and check-cut.py imports the same clause list, so a clause cannot be dropped silently.
  • Captions follow the edited picture. An episode derives word timing from its finished assembly. A single-take recut measures words on the original take, aligns spelling to the approved script and moves only kept whole words through the same source-span map as the video and shot boundaries. Dropped speech is not captioned; moved or repeated spans move or repeat their captions. A boundary through a word stops finishing: widen the kept span and rebuild. Never reuse raw-take caption timestamps on an edited video.
  • The gate measures the finished file: shot lengths, splices on real cuts, caption timing and safe zone, ambience floor per shot, loudness and true peak, every scripted line audible, and detail and black point. It prints what it can NOT assess (faces, comprehension, how it sounds) on every run. Watch the cut end to end, and do not publish a FAIL.

Cost

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.

Show full SKILL.md (883 more words)Show less

Local finishing

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.

Single-take recuts

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:

bash
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:

bash
python -m unittest discover -s tests -p 'test_*.py' -v

Opt-in hold and handover settings

Six 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).

keywhat it does
handover_grammarevery person's first shot opens on the interviewer passing the product
natural_grammarthe product is held like a drink someone was just handed, not presented
real_grammarchest-height relaxed hold, one consistent interviewer, nothing printed carried
level_cameraremoves the glance down from speaking shots, which is what tips the product
no_signageno shops, signs or lettering behind the cast (names park-corner landmarks)
can_sealed_grammarexisting 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.

Known limits

  • Seedance refuses some photoreal faces (its likeness gate). Faces here come from the prompt, not a reference photo; see REFERENCE.md.
  • A multi-take episode can't make three generations be the same corner. A scene-reference still from take A (--scene-ref) couples later takes to its mic, street and light. Check by eye.
  • Ambience can differ between takes. The gate's check G fails a shot whose street bed sits within a few dB of the speech; that needs a re-take, not a mix.

Provenance

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

Files

SKILL.md and 79 other files (scripts, references) in skills/ads/capabilities/render-street-interview of gooseworks-ai/goose-skills.

  • SKILL.md
  • READINESS.md
  • REFERENCE.md
  • TAKES.md
  • brands/demo-tallgrass-oat.json
  • brands/graza-ep1-a2.json
  • brands/graza-ep1-b.json
  • brands/graza-ep1-c.json
  • brands/liquid-death-4816.json
  • brands/liquid-death-4817.json
  • brands/liquid-death-4818.json
  • brands/liquid-death-4819.json
  • brands/liquid-death-4820.json
  • brands/liquid-death-4821.json
  • brands/liquid-death-4822.json
  • brands/liquid-death-4823.json
  • brands/liquid-death-4824.json
  • brands/liquid-death-4827.json
  • brands/liquid-death-4828.json
  • brands/liquid-death-4829.json
  • … and 60 more

Open the folder on GitHubat commit c650c6d

Compare with similar skills

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.

Render Street Interview compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Render Street Interview this skillgooseworks-ai/goose-skills1.2k—~4kAutomated safety check: PassMIT
Guizang Social Cardsop7418/guizang-social-card-skill7.4k1 repos~7.8kAutomated safety check: PassAGPL-3.0
Weekly Changelog Videoheygen-com/hyperframes60k—~3.3kAutomated safety check: PassApache-2.0
Anthropic Brand Stylinganthropics/skills180k30 repos~559Automated safety check: PassApache-2.0
MoneyPrinterTurbo Video Generatorharry0703/MoneyPrinterTurbo129k—~2.1kAutomated safety check: WarnMIT
HyperFrames Media Useheygen-com/hyperframes60k—~2.4kAutomated safety check: PassApache-2.0

Similar skills

  • 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.

    7.4k GitHub starsUsed in 1 repo~7.8k tokens
    Media & CreativeAuto-check passed
  • Weekly Changelog Video

    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.

    60k GitHub stars~3.3k tokensUpdated today
    Media & CreativeAuto-check passed
  • Anthropic Brand Styling

    anthropics/skills

    Official

    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.

    180k GitHub starsUsed in 30 repos~559 tokens
    Media & CreativeAuto-check passed
  • MoneyPrinterTurbo Video Generator

    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.

    129k GitHub stars~2.1k tokensUpdated today
    Media & CreativeAuto-check: warnings
  • HyperFrames Media Use

    heygen-com/hyperframes

    Finds, generates and edits media for HyperFrames video projects: music, sound effects, images, icons, logos, voiceovers, captions and color grades.

    60k GitHub stars~2.4k tokensUpdated today
    Media & CreativeAuto-check passed
  • Holo Card Studio

    EverettFish/holo-card-studio

    Create collectible holographic foil cards and two-image lenticular flip cards with AI-generated full-color ukiyo-e and colored sumi-e anime artwork, layered Blender scenes, renders, GLB export, and…

    1.9k GitHub stars~1.4k tokensUpdated 18 days ago
    Media & CreativeAuto-check passed

More from gooseworks-ai/goose-skills

All 273 skills in this repo
  • Reddit Post Finder

    gooseworks-ai/goose-skills

    Scrape and search Reddit posts using Apify. An agent skill from gooseworks-ai/goose-skills.

    1.2k GitHub starsUsed in 1 repo~1.2k tokens
    Auto-check passed
  • Create Image Fal

    gooseworks-ai/goose-skills

    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.

    1.2k GitHub stars~1.3k tokensUpdated 2 days ago
    Auto-check passed
  • Render Hook Replacement

    gooseworks-ai/goose-skills

    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.

    1.2k GitHub stars~2.3k tokensUpdated 2 days ago
    Auto-check passed
  • Blog Feed Monitor

    gooseworks-ai/goose-skills

    Scrape blog posts via RSS feeds (free, no API key) with Apify fallback for JS-heavy sites.

    1.2k GitHub starsUsed in 1 repo~578 tokens
    Auto-check passed
  • Competitor Post Engagers

    gooseworks-ai/goose-skills

    Find leads by scraping engagers from a competitor's top LinkedIn posts.

    1.2k GitHub starsUsed in 1 repo~1.8k tokens
    Auto-check: notes
  • Render Chatgpt Chat

    gooseworks-ai/goose-skills

    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…

    1.2k GitHub stars~2.3k tokensUpdated 2 days ago
    Auto-check passed

Questions about Render Street Interview

What does Render Street Interview do?

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.

When should I use Render Street Interview?

Render Street Interview fits situations like: media & Creative work in your project.

How do I install Render Street Interview in Claude Code?

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.

How do I install Render Street Interview in Codex?

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.

Can I use Render Street Interview in Cursor, Gemini CLI or GitHub Copilot?

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.

What does Render Street Interview need to run?

Going by SKILL.md and its folder, Render Street Interview needs the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Render Street Interview access the network?

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.

Is Render Street Interview safe to install?

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.

What licence does Render Street Interview use?

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.

How many tokens does Render Street Interview use?

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.

What are the alternatives to Render Street Interview?

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.

Who maintains Render Street Interview?

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.