ads-info-gatherer
ads-info-gatherer
Required inputs
- One of these input modes:
online-gather:- dealer/campaign request or brief
- dealer site URL
- any available source docs or screenshots
provided-folder:- a user-supplied folder shaped like
assets/worker-pack-template/
- a user-supplied folder shaped like
- Any active workflow, brief, or client playbook that defines campaign-specific rules
Workflow
- Create a run folder:
- Working:
logs/car-ads-designer/ads-info-gatherer/<task-id>/ - Final:
artifacts/car-ads-designer/ads-info-gatherer/<task-id>/ - Start a
README.mdimmediately with the request summary, chosen task id, source URLs or provided-folder path, and current status - Keep raw evidence and working copies under the working folder, for example
sources/,evidence/,raw-assets/, andnotes/ - Reserve the final folder for the normalized source pack plus the selected downstream-ready asset copies
- Working:
- Decide the input mode.
- For
online-gather, use browser tools such as web fetch + Playwright-style page interaction to inspect the dealer site and related sources - For
provided-folder, validate the folder contents and normalize them into the same source-pack outputs
- For
- In
online-gathermode:- Go to the dealer specials page or other user-provided live source URLs
- Select the target vehicle set according to the active workflow, brief, or client instructions
- Gather retail details, disclaimers, source images, logos, and campaign rules from the live sources
- Save source links, screenshots, downloads, and extraction evidence under the working folder
- In
provided-foldermode:- Read the folder shaped like
assets/worker-pack-template/ - Use the raw sources, extracted assets, and structured notes from that folder as the input bundle
- Preserve the provided raw bundle as working evidence; if files are copied into the run, place them under the working folder rather than directly in the final pack
- Validate that the provided files are sufficient for downstream ad generation
- Read the folder shaped like
- Gather the truth set for each vehicle:
- Dealer name
- Campaign/event name and date range
- Year, model, trim
- VIN, MSRP, residual
- Lease/APR/retail terms
- Disclaimer text and expiry date
- Inventory or selection rationale if available
- Gather everything needed for later image work:
- Car model reference images
- Logo files and safe-space notes
- Event guideline notes
- Car logs/history or prior approved references
- Required size list
- Keep all fetched or provided media assets under the working folder first, even if only some of them will later be promoted into the final source pack
- Keep provenance explicit:
- Save
exact_fragmentseparately fromnormalized_value - Record where each field came from
- Mark uncertain fields with a confidence level
- For every retained media asset, record the original source plus both the working-copy path and the final-pack path when a downstream-ready copy is included
- Save
- Produce a reusable source pack for downstream skills.
- Copy only the selected downstream-critical assets into
artifacts/.../assets/ - Do not leave a downstream-required image or logo only in
logs/
- Copy only the selected downstream-critical assets into
References
- Use the active workflow, source-pack contract, or client brief for campaign-specific selection rules, OEM source priorities, and required sizes.
Assets
assets/worker-pack-template/is the packaged template for the user-supplied folder mode.
Outputs
- Working folder:
logs/car-ads-designer/ads-info-gatherer/<task-id>/README.md— live status, request summary, source list, and known gapssources/— saved URLs, downloaded pages, PDFs, or provided raw inputsevidence/— screenshots, extraction notes, OCR/text dumps, and field-level provenanceraw-assets/— all fetched or provided candidate media assets kept for traceability
campaign.json— campaign-level metadatavehicles.json— VIN-anchored vehicle truth setcopy-pack.json— approved copy/disclaimer source textassets/— selected downstream-ready copies of the images, logos, and references actually needed laterassets-manifest.json— car images, logos, prior references, source URLs, working paths, final-pack paths, intended use, and confidence notesgeneration-brief.json— what the image generator needs for clean-image creationreview-standards.json— rules later used by the reviewerREADME.md— what was gathered, what was promoted into the final pack, what is missing, and confidence notes
Write working notes, raw evidence, and candidate media assets to logs/car-ads-designer/ads-info-gatherer/<task-id>/. Copy the normalized final pack plus selected downstream-ready assets to artifacts/car-ads-designer/ads-info-gatherer/<task-id>/.
Definition of done
- Every requested vehicle has a truth record or an explicit missing-data note
- Exact vs normalized values are separated
- Downstream skills can run without re-reading the raw brief
- Raw evidence, downloads, and candidate media assets are preserved under
logs/ - The final source pack is saved under
artifacts/ - The final source pack is self-contained for downstream work and includes any assets that later skills must actually open
- The task folder includes a
README.mdthat lists the produced files and any known gaps - The same output contract is produced whether the inputs came from online gathering or a provided folder
Safety / quality checklist
- Do not invent VINs, trims, or retail facts
- Do not collapse uncertain source text into “final truth”
- Keep secrets and credentials out of saved artifacts
- Flag missing source assets instead of silently substituting unrelated ones
- Do not treat
logs/as the only home for assets that downstream skills must open later; promote those assets into the final pack as well - In online mode, do not continue past access-controlled sources until the required credentials are available
More from lingkaix/smartworkers
smart-skill-maker
Create or improve SmartWorkers-style skills from a workspace-local `skills/` source tree, then apply them to Codex with `npx skills`. Use when you want one guided tool for new skill creation, skill upgrades, or SmartWorkers skill-maintenance work while keeping repo conventions aligned.
22ads-suite-pipeline
Run the full dealer car-ad production pipeline from source pack or approved anchor design to final approved deliverables: generate clean images, review/regenerate, build SVG copy overlays, and finish consistent multi-size or multi-model suites. Use whenever the user asks to produce a full ad suite, continue from an approved ad, adapt one campaign across sizes/models, review and improve generations, or keep retail offer copy exact while moving from source pack to deliverable assets.
7fal-nano-banana-2-image-gen
Generate images from text prompts using fal.ai Nano Banana 2 (fal-ai/nano-banana-2). Use when you need fast text-to-image variants for ads, concepts, or backgrounds.
6workspace-setup
Initialize a SmartWorkers-style agent workspace with repo-root guidance, `logs/`/`temp/`/`artifacts/`, a local `skills/` source tree, ignore rules, config templates, and the required global `mise` plus `npx skills`, `skill-creator`, and `smart-skill-maker` bootstrap completed in one turn by default. Use when starting a new agent workspace, bootstrapping a fresh project folder for repeatable agent work, or standardizing README, WORKFLOW, AGENTS, and skill-management flow before adding more automation.
5fal-veo3-image-to-video
Generate videos from a reference image using fal.ai Veo 3.1 image-to-video (`fal-ai/veo3.1/image-to-video`). Use when you need short video variants (ad b-roll, product shots, storyboards) from a still image, with final outputs saved to `artifacts/` and request/response logs saved to `logs/`.
1fal-qwen-image-edit
Generate and edit images from reference images using fal.ai Qwen (`fal-ai/qwen-image-edit-2511`). Use when you need image-to-image edits/variations, background swaps, style alignment, or multiple aspect-ratio outputs (e.g., square + 16:9) from the same prompt.
1