narrative-health-audit

name	narrative-health-audit
description	When a Web3 project's content engagement is declining (>20% drop in 30 days), community can't explain what you do, or different team members give different elevator pitches, run a 12-step audit to identify narrative anti-patterns (Tech Trap, Wrong Hero, Hook Failure, Inconsistency) and produce a prioritized fix list. Use as a quarterly health check or pre-launch diagnostic.
composition_level	atom
extraction-lens	capability
source_attribution	Matt Bond (Hivemind Library)
license	pending-consent
status	candidate

Narrative Health Audit

When to use

• Content engagement declining (>20% drop over 30 days)
• Community can't clearly explain what you do
• Different team members give different elevator pitches
• High awareness but low conversion
• Negative sentiment rising in community channels
• Preparing major launch / rebrand
• Quarterly narrative health check (preventive)

When NOT to use

• Pre-product with no messaging yet (nothing to audit)
• Active crisis requiring immediate response (fix first, audit later)
• Less than 30 days of content history (insufficient data)

Inputs required

Before running, collect:

• Last 90 days of published content (tweets, blogs, announcements)
• Engagement metrics per piece (likes, comments, shares, CTR)
• Community feedback / complaints (Discord, Telegram, support tickets)
• Competitor positioning and messaging
• Current team elevator pitches (record 3 different team members)
• User research or survey data, if available

Time required: 4-6 hours for thorough audit.

Core procedure (12 steps)

1. Collect all content from the past 90 days into a single document.
2. Run the Grandmother Test on the top 10 pieces by reach: would someone outside crypto understand within 15 seconds?
3. Count we/our vs you/your ratio. Healthy = 1:3 or more "you."
4. Check narrative consistency: can you summarize the core message in one sentence that applies across all channels?
5. Identify the top 3 anti-patterns using the checklist in reference.md.
6. Score each piece for the four core anti-patterns:
   • Tech Trap — specs before benefits
   • Wrong Hero — company as hero instead of audience
   • Hook Failure — weak opening
   • Inconsistency — contradicts other content
7. Map engagement data to anti-pattern presence (correlation test). Pieces low on anti-patterns should outperform.
8. Document what your best-performing content did differently — extract the positive pattern.
9. Create a prioritized fix list, highest-impact first.
10. Draft corrected versions of the worst-performing pieces as templates.
11. Schedule team calibration session to align on the corrected narrative framework.
12. Implement a 30-day test of the corrected approach. Re-measure.

Owners and required participants

• Primary owner: Head of Marketing / Communications
• Required: Founder/CEO (for narrative alignment), Community Manager (for feedback), Product Lead (for feature framing)
• Optional: Content creator, Social media lead

Output

Return:

1. The top 3 anti-patterns identified
2. Engagement-vs-anti-pattern correlation table
3. Best-performing content's positive pattern
4. Prioritized fix list (highest impact first)
5. 3-5 corrected versions of worst-performing pieces (as templates)
6. 30-day test plan with re-measurement criteria

Critical insight

Most teams fall into 2-3 anti-patterns simultaneously. The Tech Trap + Wrong Hero combo is the most common and most deadly. Fix those two first; other improvements often follow naturally.

Failure modes / risks

• Paralysis by analysis — auditing forever without fixing. Set a 7-day deadline from start to fix-list.
• Defensive team dynamics — members protecting "their" content instead of focusing on effectiveness. Frame as "what's working vs not" rather than "who was wrong."
• Over-correction — swinging too far (going from tech-heavy to zero substance).
• Public pivot whiplash — changing narrative too abruptly without explanation confuses community. Telegraph the shift.
• Cherry-picking data — ignoring qualitative signals that contradict quantitative metrics.
• Founder ego block — CEO refuses to acknowledge their pet narrative isn't working.
• Assuming vs validating — fixing based on assumptions rather than testing with real users.

Regional notes

• Tech Trap is especially common in technical / Asian markets
• Ideology Framing is more prevalent in Western markets

Adjust the audit weight per anti-pattern based on your audience geography.

Supporting files

• reference.md — Quick diagnostic checklist (10-item pre-publish gate) + full anti-pattern list with examples + best-performing content positive pattern