gauthier-thiry-leadfactory-free-skills

name

gauthier-thiry-leadfactory-free-skills

description

Public no-key LeadFactory workflow for Meta Ads research deliverables. Deploys up to 50 parallel sub-agents to research markets, competitors, ICPs, and ad patterns. Outputs a rich master CSV (opportunities + ICP matrix + deep search recap) plus strategy and creative brief. Use when the user wants Gauthier Thiry / LeadFactory free skills, deep market research, public Meta Ads Library competitor research, a rich Excel/CSV dataset, a strategy document, a static creative brief, or a Desktop delivery folder without private LeadFactory tools, paid API keys, database injection, client files, VSL scripts, or proprietary creative generation.

Gauthier Thiry LeadFactory Free Skills

Public, simplified LeadFactory pipeline for people who want the research and strategy layer of a Meta Ads launch without private infrastructure.

This skill deploys up to 50 parallel sub-agents across market awareness, competitor research, ICP profiling, ad pattern analysis, and opportunity synthesis. It produces a rich master CSV and a full strategy package on the user's Desktop.

Final Deliverables

~/Desktop/LeadFactory-Free-Skills-{project-slug}/
  00-input/brief.md
  01-deep-search/01-market-awareness.md
  01-deep-search/02-competitor-research.md
  01-deep-search/03-psychographic.md
  02-meta-ads-library/data.csv
  02-meta-ads-library/analysis.md
  03-strategy/strategy.md
  04-creative-brief/creative-brief.md
  05-master-output/master-research.csv       ← rich CSV (all research in one file)
  DELIVERY-MANIFEST.md

The master-research.csv is the primary output: a single enriched spreadsheet combining all research signals — market data, competitor profiles, ICP segments, white spaces, and prioritized ad angles — ready to import into Excel, Google Sheets, or Airtable.

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Non-Negotiables

• Never request or use paid API keys, service-role keys, private database credentials, CRM access, internal apps, private repos, or client folders.
• Never copy real LeadFactory client deliverables, PDFs, DOCX files, logs, state files, generated media, private emails, internal paths, or proof assets.
• Do not generate full VSL scripts, long-form sales letters, or proprietary script packs. Only create strategy, angle rationale, static creative brief, and short ad concept copy.
• Use public web research and public Meta Ads Library pages only. Do not bypass login, paywalls, rate limits, or platform restrictions.
• If public scraping fails, create a transparent fallback CSV from manually visible ad URLs, user-provided screenshots, pasted ad text, or public landing pages. Label it as fallback.
• Save final output on the user's Desktop, not inside a private LeadFactory project folder.

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Inputs

Collect or infer these fields before starting:

• project_name: brand or offer name.
• market: niche and geography.
• offer: product/service, price range if public, core promise.
• target_avatar: buyer profile.
• competitors: 3–8 public competitor brand/page names or Meta Ads Library URLs.
• traffic_goal: lead form, booking call, waitlist, audit, or lead magnet.
• tone: direct, premium, educational, founder-led, etc.

If any critical field is missing, ask one concise grouped question and continue with reasonable defaults once answered.

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Workspace

Create a Desktop folder first:

python3 skills/gauthier-thiry-leadfactory-free-skills/scripts/create_workspace.py \
  --project "Project Name"

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50-Agent Research Architecture

Launch sub-agents in parallel batches. Each agent has a focused mandate and returns structured findings that feed the master CSV.

Batch 1 — Market Foundation (5 agents)

Agent	Mandate
M1 — Market Size	TAM, SAM, SOM estimates. Growth rate, macro trends, seasonality. Public sources (Statista, Grand View, IBISWorld free previews, industry reports).
M2 — Pricing Landscape	Price bands across the market. Entry / mid / premium segments. Price anchors used in ads.
M3 — Regulatory & Compliance	Platform ad policies (Meta, TikTok) for this niche. Sensitive categories, banned claims, compliance constraints.
M4 — Trend Signals	Google Trends, TikTok trending sounds/hashtags, Reddit hot threads, rising search queries. 6-month window.
M5 — Channel Map	Which channels are saturated vs. underused: Meta, TikTok Shop, Google, YouTube, email, influencer.

Batch 2 — Competitor Deep Dives (up to 20 agents, one per competitor)

For each competitor brand provided (max 20), launch one agent:

Sub-agent	Mandate
C1…C20 — Competitor {N}	Public website: hero offer, price, brand promise, ICP signals. Meta Ads Library: estimated active ad count, top angles, hook patterns, visual style, CTA. TikTok/Instagram presence. Key strengths, weaknesses, threat level (HIGH / MEDIUM / LOW).

Each competitor agent returns a structured row for data.csv plus narrative notes.

Batch 3 — ICP Segments (10 agents)

Agent	Mandate
ICP1 — Primary Persona	Demographics, psychographics, day-in-the-life, top 3 pain points, primary motivation to buy, main objection, preferred content format.
ICP2 — Secondary Persona	Alternative buyer segment — different age, context, or use case. Same fields.
ICP3 — Reddit Deep Dive	Subreddits relevant to the niche. Top threads, recurring complaints, vocabulary used ("they say X when they mean Y"), trust signals that work.
ICP4 — Review Mining	1-star and 5-star review analysis from public sources (Amazon, Trustpilot, Google Reviews, App Store). Patterns in language, specific words that recur.
ICP5 — Objection Map	Top 5 objections to purchase in this category. For each: objection text → counter-argument → ad angle that addresses it.
ICP6 — Awareness Levels	Map the market across Eugene Schwartz's 5 stages (Unaware → Most Aware). Where does the bulk of cold traffic sit? Which stage to target first?
ICP7 — TikTok Audience Signals	TikTok hashtag audiences, FYP content patterns, creator types that resonate, comment sentiment on competitor videos.
ICP8 — Seasonal Triggers	When does this ICP buy? Events, seasons, life triggers (new year, back to school, pay day, etc.) that drive purchase intent.
ICP9 — Cultural / Geo Signals	If multi-market: cultural nuances, localization needs, price sensitivity differences.
ICP10 — Micro-Segment Opportunities	Under-served sub-segments ignored by current competitors. Niche within the niche.

Batch 4 — Ad Pattern Analysis (10 agents)

Agent	Mandate
AP1 — Hook Patterns	Top 20 hooks used across competitors. Categorize by type: question, contrarian, social proof, POV, stat, listicle. Identify saturation level.
AP2 — Visual Styles	Dominant visual styles in the category: lifestyle, editorial, UGC, flat-lay, native screenshot, data-viz, advertorial. Which are over/underused?
AP3 — CTA Patterns	Most common CTAs in this market. Which map to which funnel stage. Underused CTAs.
AP4 — Format Saturation	Distribution of ad formats: feed single / story / reel / carousel / collection. White spaces in format mix.
AP5 — Price Framing	How do competitors frame price? Direct price, anchoring, cost-per-use, comparison, "less than X per day"?
AP6 — Social Proof Patterns	Review counts, star ratings, "as seen on", TikTok Shop badges, press mentions. Which proof signals are most used/trusted?
AP7 — Emotional Angles	Dominant emotional triggers: fear, aspiration, identity, belonging, savings, status. Which are over/underused?
AP8 — Anti-Patterns (what NOT to do)	Angles that are overplayed and showing ad fatigue. Visual styles that look dated. Claims likely to get flagged.
AP9 — TikTok-Native vs Meta-Polish	Gap between TikTok-native content (lo-fi, UGC) and Meta polished ads. Which performs better in this niche? Evidence?
AP10 — White Space Matrix	Combine all above: angles × formats × styles with zero incumbent use → ranked opportunity list.

Batch 5 — Synthesis (5 agents)

Agent	Mandate
S1 — Opportunity Scoring	Score each white space (0–10) on: audience size, competition density, ad fatigue level, format accessibility, compliance risk.
S2 — Positioning Statement	Draft 3 positioning options for the brand. For each: one-liner, key differentiator, primary ICP, primary channel.
S3 — Angle Priority Matrix	Rank the top 10 ad angles by: expected CTR tier, awareness stage fit, format fit, and ease of execution.
S4 — 30-Day Test Plan	Recommend Phase 1 (days 1–10): 3 angles × 2 formats = 6 creatives, $X daily budget, kill criteria. Phase 2 scaling logic.
S5 — Master CSV Assembly	Aggregate all agent outputs into master-research.csv following the schema below.

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Master CSV Schema — master-research.csv

One file, multiple record_type values so it imports cleanly into Excel / Google Sheets / Airtable.

Column definitions

Column	Description
record_type	MARKET / COMPETITOR / ICP / AD_ANGLE / OPPORTUNITY
id	Unique row ID (e.g. MKT-001, COMP-003, ICP-002, ANG-007, OPP-004)
name	Entity name (market segment, competitor brand, persona name, angle name, opportunity label)
category	Sub-classification (e.g. for COMPETITOR: direct / indirect / aspirational)
summary	1–2 sentence description
price_range_usd	Price range or N/A
primary_channel	Main distribution channel (Meta / TikTok / Google / Email / etc.)
icp_fit	Which ICP segment(s) this maps to
awareness_stage	Schwartz stage: 1=Unaware … 5=Most Aware
hook_example	A concrete hook line for this angle/competitor/opportunity
visual_style	Dominant or recommended visual style
format	Best ad format(s) for this row
cta	Recommended CTA
strength	Key strength (for competitors) or why this angle works
weakness	Key weakness or risk
threat_level	HIGH / MEDIUM / LOW / N/A
saturation_score	0–10 — how saturated is this angle/competitor/format in the market
opportunity_score	0–10 — overall opportunity rating
priority

Example rows

record_type,id,name,category,summary,price_range_usd,primary_channel,icp_fit,awareness_stage,hook_example,visual_style,format,cta,strength,weakness,threat_level,saturation_score,opportunity_score,priority,source,notes,agent
MARKET,MKT-001,Linen co-ord sets US,DTC fashion,"$200–420M SAM, growing 18% YoY, driven by clean-girl TikTok aesthetics",58–180,TikTok+Meta,Women 24–35,2,,"UGC lifestyle",,,,,,,,4,8,P1,Statista 2025 / TikTok trends,,M1
COMPETITOR,COMP-001,Quince,direct,"Premium linen basics, $60–120, strong Meta presence, weak TikTok Shop",60–120,Meta+Email,Women 28–42,3,"Premium linen. Not $180.","Editorial lifestyle",3:4 feed,SHOP NOW,"Price positioning, strong reviews","No TikTok Shop native presence",HIGH,7,6,P2,meta.com/ads/library,,C2
ICP,ICP-001,Maya,"primary persona","28yo, millennial, seeks effortless style, $58 feels acceptable if quality is proven",,TikTok+Meta,,"2–3","This is what I wear when I don't want to think.","UGC selfie",9:16,"SHOP THE SET","Responds to social proof + fabric quality","Needs proof before price commitment",,,9,P1,Reddit r/femalefashionadvice,,ICP1
AD_ANGLE,ANG-001,Cost-per-wear,value framing,"$0.29/wear reframes $58 as exceptional value vs fast fashion",,,ICP-001,3–4,"$0.29 per wear. 200 wears. The set that earns its place.","Data-viz bold stat",1:1,"SHOP THE SET","Directly counters price objection","Requires some numeracy from viewer",,3,9,P1,Competitor ad analysis,,AP5
OPPORTUNITY,OPP-001,TikTok-native UGC + iMessage format,white space,"Zero incumbents use native social-proof formats (iMessage/TikTok review) for linen co-ords",,TikTok+Meta,ICP-001,2,"ok where is that linen set from?? 😅","iMessage native screenshot",3:4,"shop link in bio",,"Requires no brand recognition to thumb-stop",,0,10,P1,"Meta Ad Library sweep — 0 results",,AP10

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Parallel Research Plan

Phase 1 — Launch all 50 agents simultaneously

Batch 1 (M1–M5):    Market foundation — 5 agents
Batch 2 (C1–C20):   One agent per competitor — up to 20 agents
Batch 3 (ICP1–10):  ICP segments — 10 agents
Batch 4 (AP1–10):   Ad pattern analysis — 10 agents

All 45 agents run in parallel. Each writes its structured findings back as JSON or structured markdown.

Phase 2 — Synthesis (after Batch 1–4 complete)

Batch 5 (S1–S5):    Synthesis — 5 agents

S5 (Master CSV Assembly) runs last: aggregates all agent outputs into master-research.csv.

Phase 3 — Strategy & Creative Brief

6. Strategy document from DeepSearch reports + master CSV.
7. Static creative brief from strategy, psychographics, and competitor ad patterns.
8. Final manifest and public-safety audit.

If sub-agents are unavailable, run the same tasks sequentially but keep files separated.

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DeepSearch

Read references/deep-search-prompts.md only when writing the three research reports.

Each report must:

• Be in French unless the user asks otherwise.
• Use public sources with URLs and access dates.
• Separate facts from inferences.
• Include direct implications for Meta Ads angles.
• Avoid private LeadFactory or client examples.

Save:

• 01-deep-search/01-market-awareness.md
• 01-deep-search/02-competitor-research.md
• 01-deep-search/03-psychographic.md

---

Public Meta Ads Library Research

Read references/meta-ads-library-workflow.md and references/competitor-ads-framework.md before extracting ads.

Goal:

• Build 02-meta-ads-library/data.csv with one row per public ad or public ad-like asset.
• Build 02-meta-ads-library/analysis.md with patterns, saturated angles, white spaces, hooks, CTAs, and recommendations.

Use no-key sources:

• Meta Ads Library public search pages.
• Public landing pages linked from ads.
• Public social posts if the ad page cannot be accessed.
• User-provided screenshots, URLs, or pasted text.

CSV columns are fixed. Use the template in assets/templates/meta_ads_data_template.csv.

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Strategy And Creative Brief

Read references/strategy-and-creative-brief.md and references/meta-ad-copy-checklist.md before writing final strategy assets.

Create:

• 03-strategy/strategy.md: positioning, awareness level, test plan, campaign structure, angle matrix, risks, next actions.
• 04-creative-brief/creative-brief.md: target audience, visual direction, static creative concepts, copy direction, examples of on-image text, and CTA.

Keep this public/free:

• Provide strategic frameworks and creative directions.
• Do not include complete VSL scripts.
• Do not include internal LeadFactory performance thresholds, automation scripts, or client proof.

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Public-Safety Audit

Before saying the folder is ready, run:

python3 skills/gauthier-thiry-leadfactory-free-skills/scripts/audit_public_safety.py \
  ~/Desktop/LeadFactory-Free-Skills-{project-slug}

If any issue is flagged, remove or rewrite the file before delivery.

Final quality gate:

• No API keys, tokens, environment files, private databases, CRM, or admin URLs.
• No real client names unless the user explicitly supplied them for their own public use.
• No absolute private local paths.
• No private emails, invoices, contracts, logs, generated client media, PDF/DOCX exports, or ZIPs.
• No VSL script files or full ad script packs.
• All sources are public and cited.

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Final Response

Return:

• Desktop folder path.
• List of created deliverables.
• Number of sub-agents launched and completed.
• Row count of master-research.csv (breakdown by record_type).
• Note whether Meta Ads Library extraction was direct or fallback.
• Result of audit_public_safety.py.