Jason ZhouandClaude Opus 5.5
ce916f288d
* feat(web): /intent-signals landing page for the buyer-signals launch "Claude monitor buyer signals": built from /people-search, keeping its skill grid (now eight signal tiles from the launch film's real run) and the People Search Bench. New: a morning-run demo, a three-step 'your agent is the monitor' section (Claude Code /schedule, Codex from cron, diff against the last list), the $0.98 four-source receipt, and pay-per-check pricing. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(web): /intent-signals scenes from the launch film Replace the monitor steps, the receipt table and the signal tile grid with the film's own scenes, rebuilt as HTML: a radar and signal feed that judges each post, a thread → scored companies → decision makers flow with vendor waterfall, 'treg monitors…' with evidence cards per signal, and the daily hot-leads list a scheduled run grows. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(web): /intent-signals hero plays the whole film in one take The hero now runs monitor the topic → read the thread → score companies → find decision makers as one continuous, clickable demo, replacing the old hero table and the two separate scene sections. The 'treg monitors…' section goes back to the skill tile grid, each tile now a small visual scene (post, new position, news, job postings, funding, headcount chart, tech adopters, Reddit thread) instead of text rows. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(web): /intent-signals headline 'Claude for Signal Monitor' and the real Reddit mark Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * style(web): /intent-signals nav drops the Signals and Daily list links Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * fix(web): /intent-signals works at every width Tablet keeps every column and a smaller radar beside the feed; phone puts the radar above the feed and the post above its thread, folds secondary columns, wraps the prompt bar and truncates names instead of overlapping. The prompt cursor now honours [hidden], and a new prompt clears the last reply before it types. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * style(web): /intent-signals headline 'Claude for Monitor Leads Signal' in Geist Pixel Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * fix(web): /intent-signals prices from the billed run, not the film's $0.98 The launch run's LinkedIn, Reddit and X sweep billed $0.52 for 2,945 signals (4,710 with the free GitHub stargazers): $0.0002 per signal checked. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(skills): lead-signals, the /intent-signals workflow as a skill Detect, qualify, contact, hand back a ranked list; optionally keep watching on the agent's own schedule with a diff against the last list. It names signal families and the words to search the catalog with, never endpoint ids, so it stays right as the catalog grows. Served at /skills/lead-signals/SKILL.md and as the third entry in the well-known skills index, like make-ugc. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * docs(skills): lead-signals description says only when to use it; example vendors per signal Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(skills): install our skills with npx skills add superdesigndev/treg The skills CLI (skills.sh) reads skills/ and skips symlinks, so build_plugin.py now also writes real copies of the workflow skills (make-ugc, lead-signals) there, and --check keeps them in step with src/treg/web/skills. The six skills that are repo tooling carry metadata.internal so the CLI hides them. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * chore(skills): vendor-listing is repo tooling, hidden from npx skills add Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(web): /intent-signals shows the one-line skill install npx skills add superdesigndev/treg --skill lead-signals, beside the treg set-up command; both copy buttons share one handler and wrap on a phone. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(cli): treg skill bootstrap installs every public skill the registry advertises install.sh already runs it; it now also installs each skill in /.well-known/skills/index.json (make-ugc, lead-signals) into the same global agent folders as the treg skill. Best-effort, and a name from the index must be a plain slug before it becomes a directory. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * docs(skills): lead-signals sends an agent without treg to llms.txt to set up Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * feat(web): the buyer-signals page lives at /leads-signals Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * style(web): /leads-signals tools grid shows Aviato people search instead of Apollo Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * style(web): /leads-signals nav reads Leads signals Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * refactor(web): /leads-signals served by _static_page like the other launch pages The catalog's size comes from {ENDPOINTS}/{PROVIDERS} instead of typed numbers, and the page carries the shared share image. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * docs(context): the installer also installs the advertised workflow skills Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
6.6 KiB
name, description
| name | description |
|---|---|
| make-ugc | Make AI UGC videos end to end through treg. Pull the trending TikTok and Instagram videos in a vertical, extract the hook patterns, create a character with the same vibe as a presenter the user picks, generate 5-10 talking-head hook clips on Seedance 2.5 (less-restriction route), add a voiced demo clip, burn captions, and report the bill per clip. Use when the user asks for UGC ads, creator-style product videos, TikTok/Reels hooks, or an AI presenter for their product. |
Make UGC videos
One loop, five steps, every model call through treg. The recipe behind https://treg.to/ugc.
Input: the product (one line), the vertical (a few keywords), and optionally reference accounts, a
phone recording of the product, a voice-reference clip, or a character image the user already likes.
Output: a folder with hooks.md, the character image and its JSON prompt, one captioned clip per
hook, an optional voiced demo clip, and bill.md with what each step cost.
Prerequisites
- treg. Every catalog call, upload and poll goes through treg; the vendor keys stay server-side
and the charge lands on the team's prepaid balance. If
treg --versionfails:curl -fsSL https://treg.to/install.sh | sh treg logintreg balancebefore starting. A full run of 4 hook clips lands in the low single-digit dollars at 720p; say the estimate before each paid step. - Two sibling skills this one delegates to. Read each before its step, do not paraphrase them:
portrait-clone(the character): https://raw.githubusercontent.com/agentara/skills/refs/heads/main/skills/aigc/portrait-clone/SKILL.mdugc-talking-head-video(the clips and captions): https://raw.githubusercontent.com/superdesigndev/treg/main/.agents/skills/ugc-talking-head-video/SKILL.md with itsscripts/seedance_treg.pyandscripts/caption_burn.pybeside it.
ffmpeg, Python 3 with PIL.OPENAI_API_KEYonly if you burn captions (Whisper word timings).
Flow
Stop at the three marked points and let the user choose. Do not pick for them.
1. Find what's trending
Search by task, not vendor. treg catalog search "tiktok search videos" and
treg catalog search "instagram reels search by keyword" return the routed endpoints; call them
with the vertical's keywords, sorted by likes, last 30 days, and collect 50-150 videos. Pull
transcripts for the 10-15 most relevant (treg catalog search "tiktok video transcript"). If the
user names competitor brands, add their ads from the Meta ad library (treg catalog search "meta ad library").
Write hooks.md: one row per video with views, the first spoken line (0-3 s), when the product is
first named, and who is on camera. Then name the dominant pattern in one line. In agent and B2B
software niches it is usually: a stunt or claim, a specific number, the result, then "here's how";
the tool appears late, as the answer. Tell the user what the data said and what it cost.
Stop 1. Show 5-8 videos as candidates for the presenter vibe and for the voice. The user picks one of each (they can be the same video).
2. Create the character
If the user already has a character image, skip to step 3.
Grab a clean frame of the chosen presenter (ffmpeg -ss <t> -i src.mp4 -frames:v 1 ref.jpg) and
run portrait-clone on it. It produces a locked JSON prompt: every default the image model would
otherwise fill in is pinned, which is what stops the doll eyes and the HDR sheen. Generate the same
JSON on at least two models through treg and let the user compare:
treg call reapi.image-gen.gemini-3-pro-image --data '{"prompt": "<json>", "size": "9:16", "resolution": "2K"}'
treg call reapi.image-gen.gpt-image-2-5 --data '{"model": "gpt-image-2.5-flare", "prompt": "<json>", "size": "1024x1536"}'
Gemini 3 Pro Image has been the most realistic (phone-camera softness, real pores, imperfect teeth); GPT Image 2.5 keeps a doll pattern in the eyes. Say which is which but show both.
Stop 2. The user picks the character frame. Save it as character.jpg with its JSON.
3. Generate the hook clips
Draft 10 hooks in the pattern from step 1, each 45-75 words so it fits a 12-18 s take (the
talking-head skill's words/4 minus 1 rule). Put them in hooks.md under the table.
Stop 3. The user picks 3-5.
Cut the voice reference from the video chosen at stop 1: a 2-15 s animated stretch, mono mp3, as
ugc-talking-head-video § Voice reference describes. Then run that skill once per hook with
character.jpg, the voice clip and the script. Its runner uses the Seedance 2.5 less-restriction
route (reapi.video-gen.seedance-2-5.unrestricted), which accepts a realistic face and a voice clip
as references; the default route refuses them. Quote the per-second price from
treg catalog get reapi.video-gen.seedance-2-5.unrestricted before the first run, stay on 720p,
verify each take with the skill's checks, then caption with caption_burn.py. Failed tasks are
refunded, so a moderation error costs nothing but time.
4. The demo clip (optional)
The character does not need to be in the demo. If the user recorded the product on their phone, add a voiceover and captions:
- With a cloned voice, if the user has a Fish Audio (or other TTS) account: clone from the same voice-reference clip, read the demo script, and tell them it runs on their key, not treg.
- Without one, skip the clone: the demo plays under the hook clip with captions only, or with the hook clip's own audio continuing. Say which you did.
Stitch demo and captions with ffmpeg and the skill's caption_burn.py, keeping 9:16 and 720p.
5. Put it together
For each picked hook: hook clip, then the demo clip if there is one, concatenated with ffmpeg
(-c copy when the encodes match, re-encode otherwise). Name the files by hook. Write bill.md
from treg calls (or the prices you quoted): the trend pull, the character runs, each clip, and
the total divided by the number of finished clips. Hand over the folder and say what you could not
verify yourself: voice likeness and lip-sync are judged by ear and eye, not by the transcript checks.
Rules
- Every generation and reference upload goes through
treg callandtreg host. Never hand a vendor a paste-host link, and never hold a vendor key for this loop. - State the price before each paid step. When you call an endpoint directly rather than through the runner, send treg's max-cost header so one task cannot exceed the price you quoted.
- Rerun one thing at a time; there is no seed, and a rerun can regress something else.
- The three stops are the product. A run that skips them makes clips nobody asked for.