Orchestrator that runs first for lead generation requests. Gathers business context via website analysis or questions, identifies competitors, builds ICP, and routes to signal skills with pre-filled inputs.
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This is the entry point for all lead generation requests. Before any signal skill runs, this skill ensures the agent has enough business context to configure every downstream skill correctly.
Scrape the website (homepage, pricing page, about page, docs if available) and extract:
After extracting, present a summary to the user and ask them to confirm or correct.
Ask these questions one conversational block at a time. Do NOT dump all questions at once.
Block 1 — The Basics:
Block 2 — The Market (ask after Block 1 is answered):
Block 3 — Sales Context (ask after Block 2 is answered):
Once you have the business context, research to fill gaps the user didn't provide:
Present your research findings to the user for confirmation before proceeding.
After Phases 1 and 2, you should have all of this:
SHARED CONTEXT
==============
Product: [one-liner description]
Category: [market category]
Website: [URL or "none"]
ICP:
Role: [e.g., Backend engineers, DevOps leads, Engineering managers]
Company size: [e.g., 50-500 employees]
Industry: [e.g., SaaS, fintech, healthtech — or "any"]
Tech stack: [e.g., Kubernetes, Python, AWS]
Competitors: [list with GitHub repos, PH slugs, career page slugs where found]
Technology keywords: [list of 10-20 relevant terms]
Problem statements: [3-5 problems the product solves, as they'd appear in job posts or forum discussions]
GitHub repos to scan: [3-8 repos]
Subreddits: [5-10 relevant subreddits]
Job search queries: [3-5 job title searches]
Greenhouse/Lever slugs: [company career page slugs]
Product Hunt slugs: [competitor PH slugs]
Conference/event names: [if identified]
Present this to the user as a formatted summary. Ask them to confirm, add, or remove items.
Based on the context, recommend which signal skills to run. Use this priority order:
Present the recommendation as a numbered plan with costs. Ask the user which sources they want to run — all of them, a subset, or just start with the free ones.
Once the user picks their sources, begin executing them in the recommended order. For each skill:
/github-repo-signals, /job-signals)Never jump straight to a signal skill without first understanding the business. Even if the user says "scan this GitHub repo", take 30 seconds to understand what they sell and who they sell to — it makes the output analysis 10x more useful.
Don't ask all questions at once. Conversational blocks. If the user gives a website, you may not need to ask anything at all.
Research fills gaps. If the user says "our competitors are X and Y", still research to find Z they may have missed. But present findings for confirmation — don't assume.
Cost transparency. Always tell the user which sources are free and which cost money before running anything.
Reuse context. Once the shared context is built, every downstream skill should inherit it. The user should never be asked the same question twice.
Start small, scale up. Default recommendation: start with free sources, review results, then decide on paid sources. Don't push users to spend money upfront.
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Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
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Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
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For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
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The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
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Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).
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Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
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