content-trust

name	content-trust
description	Diagnoses why content isn't converting and fixes it using the Trust Architecture framework. Use proactively when a founder asks why their content gets engagement but no customers, why traffic doesn't convert, why deals stall mid-funnel, or how to improve their content strategy. Also trigger for "my LinkedIn posts get likes but no leads", "my website traffic doesn't convert", "how do I build credibility faster", or any question about content ROI.
used_by	["gtm-strategist","market-analyst","visual-storyteller"]

Content Trust — Founder OS

Diagnoses content-to-pipeline gaps using the Trust Architecture framework. Most content problems aren't volume problems — they're trust stage problems.

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The Core Insight: Trust Gap = Revenue Gap

Most companies have a predictable imbalance:

Trust Stage	Typical Content Mix	What It Creates
Recognition (brand awareness)	~55% of content	Traffic, impressions
Credibility (proof you know what you're talking about)	~10%	Authority signals
Risk Reduction (addressing fears)	~5%	Conversion
Commitment (clear next steps)	~30%	CTAs, demos, signups

The math: 55% of effort on Recognition, 5% on Risk Reduction = traffic that never converts.

The fix isn't more content. It's reallocation — shift Recognition budget to Credibility, shift Commitment budget to Risk Reduction.

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The Trust Architecture (4 Stages)

Stage 1: Recognition

Does the audience know this brand/founder exists?

Content that builds it: Consistent posting, volume, broad topic coverage, social presence When you have enough: They recognize your name when it appears in their feed When you're over-invested here: High impressions, low meaningful engagement, no DMs

Stage 2: Credibility

Does the audience believe you know what you're talking about?

Content that builds it: Data-backed insights, original research, case studies with numbers, contrarian takes with evidence, frameworks others cite Signals you're gaining it: Comments like "I've been thinking this for years", screenshots being shared, being cited by others When you're under-invested here: Traffic but no MQL conversion (B2B SaaS), engagement but no inbound DMs

Stage 3: Risk Reduction

Has content addressed the fears stopping people from acting?

Content that builds it: Customer success stories that address specific fears, objection-handling content, "what if it doesn't work" proof, transparent pricing/process, founder credibility signals Signals you need more: Product page traffic but <2% conversion, deals that stall mid-funnel, "let me think about it" responses

Common failure patterns by business type:

• B2B SaaS: deals stall mid-funnel → low Risk Reduction
• Solopreneur/consultant: engagement but zero inbound DMs → low Risk Reduction
• Creator/info product: product page visits but <2% conversion → low Risk Reduction
• Agency: leads come in but proposals don't close → low Risk Reduction

Stage 4: Commitment

Does content make the next step clear and easy?

Content that builds it: CTAs, demos, offers, lead magnets, clear value exchange When you're over-invested here: Lots of CTAs, low conversion → people aren't ready (trust stages 2-3 are weak) The trap: Most content teams over-invest here, then conclude "the CTAs aren't working" when the real problem is the audience hasn't reached Credibility or Risk Reduction yet

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The 4 Diagnostic Prompts

Diagnostic 1: Content Landscape Map

Run this first. Understand what you're actually producing.

You are a content strategist auditing my content distribution.

MY CONTENT [list your last 10-20 posts/emails/articles]:
[paste titles, formats, topics]

Categorize each piece by:
1. Trust stage it primarily serves (Recognition / Credibility / Risk Reduction / Commitment)
2. Format (insight, data, story, CTA, educational, promotional, other)
3. Audience awareness level (unaware / problem-aware / solution-aware / product-aware)

Then calculate:
- % of content at each trust stage
- Gap vs ideal distribution for a [B2B SaaS / consultant / creator / agency] at [my stage]
- The single biggest imbalance

Output as a table plus 3-sentence diagnosis.

Diagnostic 2: Differentiation Scoring

Score each piece 1–5 on how ownable it is.

Score this content piece 1-5 on differentiation:

1 = Generic: Could have been written by anyone in my space
2 = Slightly specific: References my niche but no unique angle
3 = Distinctive: My framing, but idea exists elsewhere
4 = Ownable: Specific data, story, or angle only I have
5 = Category-defining: Creates a new way of seeing the problem

CONTENT: [paste the piece]
MY CONTEXT: [company, stage, ICP]

Score it, then rewrite any section scoring below 3 to reach a 4 or 5.
What specific data, story, or angle would I need to add?

Diagnostic 3: Trust Movement Audit

Map exactly which stage each piece moves the audience to.

For each of these content pieces, identify:
1. Which trust stage it targets
2. What belief it needs to change in the reader
3. Whether it succeeds at that belief change
4. What's missing that would make it more effective

PIECES: [paste 5-10 recent pieces]

Prioritize the 2 pieces with the biggest gap between intent and execution.
Rewrite one of them to fully achieve its trust stage goal.

Diagnostic 4: Engagement Quality Analysis

Separate genuine interest from passive scrolling.

Here are my engagement metrics:
[likes, comments, shares, saves, DMs, replies, clicks, conversions]

BUSINESS CONTEXT: [ICP, offer, price point]

Classify each engagement type:
- Active interest (saves, shares, DMs, substantive comments)
- Passive acknowledgment (likes, emoji reactions, generic "great post")
- Intent signals (link clicks, profile visits after post, DMs asking about offer)

Calculate:
- Active interest rate (target: >3% of impressions)
- Intent signal rate (target: >0.5% of impressions)

What does this pattern tell me about which trust stage is weakest?
What 1 content change this week would most improve intent signals?

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The Trust Reallocation Playbook

If Credibility is your gap (most common for early founders)

Replace: Generic "thought leadership" posts and industry news takes With:

• Original data you collected (even from 20 customer calls)
• Frameworks you use that produce measurable results
• Counterintuitive takes backed by evidence
• Case studies with specific numbers ("we increased X from Y to Z by doing...")

The test: Could someone else have written this post? If yes, it builds Recognition not Credibility.

If Risk Reduction is your gap (most common for consultants, agencies, high-ticket)

Replace: More CTAs and promotional content With:

• "What working with us actually looks like" content
• Customer stories that address specific fears (not generic testimonials)
• "What happens if it doesn't work" transparent answers
• Process and methodology content that removes uncertainty

The test: Does this content make someone feel safer saying yes? If not, it doesn't reduce risk.

If both are gaps (most common for early-stage founders)

Week 1–4: Focus exclusively on Credibility content Week 5–8: Add Risk Reduction content Week 9+: Re-introduce Commitment content with the trust foundation in place

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The Comment Engine OS (LinkedIn Distribution)

Comments are 3–4× more efficient than posting for early audience building.

4 Comment Types

Type	Length	When to Use
Expertise Add	40–80 words	Add data/insight the author missed — positions you as peer
Respectful Challenger	30–60 words	Add tension with nuance — "this works, but only when..."
Story Bridge	40–70 words	Personal anecdote as pattern interrupt
Question Architect	20–40 words	Ask a question so good the author wants to write a post answering it

Key data (LinkedIn algorithm 2025)

• Comments >15 words carry ~2× algorithmic weight
• 45% less engagement on AI-generated vs human-written posts
• Only 7.1% of LinkedIn's 1B users post regularly → massive organic reach opportunity
• 3–4× more inbound conversations from commenting vs posting, at 40% less time

3-Step Expertise Add Workflow

Step 1: Route by post type

• Insight/opinion → add contradicting data or supporting evidence
• Data/research → add context or methodology question
• Prediction → add case study that confirms or complicates

Step 2: Draft the comment

• Expert mode: data point + implication + brief connection to your work
• Sharp question mode: question that implies your expertise without stating it

Step 3: Voice check

• Bar test: "Would I say this to someone at a conference bar?" If no, rewrite.
• AI red flag check: remove "resonate", "unpack", "landscape", "leverage", "delve", "navigate", "it's worth noting"

Comment Audit Prompt

Review these LinkedIn comments I've drafted:
[paste 5-10 comments]

For each:
1. Classify type (Expertise Add / Challenger / Story Bridge / Question Architect)
2. Check for AI language markers
3. Rate genuine value-add for author's audience (1-5)
4. Rewrite any scoring below 4

Then: what pattern do these comments establish about my POV/expertise?
Is there a consistent angle I should amplify?

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Content Trust Stack (Combined Workflow)

Run this monthly:

1. Week 1: Diagnostic 1 (landscape map) → identify biggest trust stage gap
2. Week 2: Diagnostic 2 (differentiation scoring) on last month's top 5 posts
3. Week 3: Rewrite 2 posts to hit trust stage gap + higher differentiation score
4. Week 4: Diagnostic 4 (engagement quality) → validate whether intent signals improved

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Integration with Other Skills

• Content Trust feeds → skills/public/distribution-engine/SKILL.md (credibility content = give-first ladder fuel)
• Content Trust feeds → skills/public/gtm-strategy/SKILL.md (risk reduction content = C.A.R.E. barrier removal)
• Run after → skills/public/problem-validation/SKILL.md (customer language → credibility content that resonates)