Kjell Tore Guttormsen
e38f71f418
Fem grep-lokaliserte gjeldsposter fra STATE FUNN, pluss to anti-erosjons-lints
som gjør to av dem terminale i stedet for gjenoppdaget en gang til.
1. .claude-plugin/plugin.json description — v4.0.0-arvet release-notat med
historiske tellinger («→ 19 agents», «→ 27 commands», «→ 29 commands»)
erstattet av en beskrivelse av hva pluginen ER. Dette er teksten /plugin
viser. Ingen tellinger i teksten: de drifter, og CHANGELOG.md eier historikken.
2. CHANGELOG hadde TO «## [0.6.0]»-overskrifter (2026-07-17 = ekte, 2026-02-07 i
halen). Halens datert om til «## [0.6.0-legacy]». Sjette gangs gjenoppdagelse
— derfor lint, se punkt 6.
3. De-niche: 7 «thought leader»-treff utenfor commands/ rettet — README (mot
kommandoens egen frontmatter-ordlyd), skills/linkedin-networking (2:
FUNN-listen navnga bare :9, men :185 var samme defekt), analytics-interpreter,
analytics-tools-guide, collaborations-guide, linkedin-visual-style.
BEVISST BEHOLDT: de 5 blocklist-linjene, de to negative eksemplene
(opportunity-generation, det ironiske sitatet i video-strategy-guide), og
network-builder:562 — et WebSearch-uttrykk som treffer hvordan ANDRE
merker seg selv, allerede paret med «Creator».
4. Hardkodet «KTG» i generisk operatør-tiltale fjernet. FUNN navnga tre linjer i
language-reviewer; klassen var 21 linjer over 4 agenter, newsletter.md,
edition-state-templaten og specifics-bank sitt renderte NN-kilder.md-artefakt.
BEVISST BEHOLDT som proveniens: CHANGELOG, tests/fixtures/* (de ER KTGs
faktiske redaktørrunde, kalibreringsfasiten), adopter-notatene som ærlig
flagger KTG-interne verktøy (contract-gate, specifics-bank), «KTG
cover-directive» og «answers KTG's question» — attribusjon, ikke tiltale.
5. Step 9 persisterte tre nøkler templaten aldri deklarerte: randsoneGate (N9),
hookForge + personaSweep.deling (N24). Nå deklarert i BÅDE artikkel-shapen og
_doc. Additivt, schemaVersion forblir 1. _doc.hookForge bærer N22-invarianten
videre: en variant har målt lengde / krok-type / kroppsspor og ALDRI en
predikert engagement-score.
6. To nye ubetingede lints (Section 16aa), begge mutasjonstestet mot de FAKTISKE
filene, ikke bare mot self-testen:
- CHANGELOG-dublettvakt (+ in-memory non-vakuitets-self-test som skiller en
ekte dublett fra en bevisst «-legacy»-overskrift)
- Step 9-nøkkeldeklarasjon (shape + _doc)
Mutasjon 1 (gjeninnfør dubletten) → rødt. Mutasjon 2 (fjern hookForge fra
shapen) → rødt. Mutasjon 3 (fjern _doc.randsoneGate, fila fortsatt gyldig
JSON) → rødt. Restaurert → grønt.
Ti suiter grønne: test-runner 300 → 303 (floor 281 → 284) · trends 300 ·
analytics 202 · hooks 191 · brain 134 · editions 72 · render 63 ·
specifics-bank 45 · tests 35 · contract-gate 33.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017vMLTF7J13jJmgqwwJyivt
256 lines
8.2 KiB
Markdown
256 lines
8.2 KiB
Markdown
# Analytics Tools Guide: Finding YOUR Edge
|
|
|
|
The mechanics in the main skill represent baseline knowledge - what works on average. Your edge comes from discovering what works specifically for YOUR audience, YOUR content, and YOUR domain.
|
|
|
|
---
|
|
|
|
## The Critical Distinction
|
|
|
|
- **Generic advice:** "Post at 8am on Wednesdays" (average across all users)
|
|
- **YOUR pattern:** "My audience engages most at 2pm on Tuesdays and 7am on Fridays" (specific to you)
|
|
|
|
Generic advice gets you to baseline. YOUR patterns get you to exceptional.
|
|
|
|
---
|
|
|
|
## Free Tools to Discover YOUR Patterns
|
|
|
|
### 1. LinkedIn Native Analytics (Essential - Start Here)
|
|
|
|
**Access:** Your profile → Analytics & tools → Analytics
|
|
|
|
#### What to Track Weekly (15 minutes)
|
|
|
|
**Post Performance:**
|
|
- Which posts got highest engagement (likes, comments, shares)?
|
|
- Which topics performed best?
|
|
- Which formats worked (story vs. framework vs. data)?
|
|
- What length generated most engagement?
|
|
- Which hooks stopped the scroll?
|
|
|
|
**Timing Patterns:**
|
|
- When did YOUR best-performing posts go live?
|
|
- What day of week shows highest engagement FOR YOU?
|
|
- What time of day gets fastest first-hour response?
|
|
|
|
**Audience Demographics:**
|
|
- Who is actually engaging? (Industry, seniority, location)
|
|
- Is this your intended audience or a different cohort?
|
|
- What titles/roles engage most?
|
|
- Where are they geographically?
|
|
|
|
**Follower Growth:**
|
|
- Which posts drove follower spikes?
|
|
- Are you gaining followers from target audience?
|
|
- What topics attract new followers vs. existing audience?
|
|
|
|
#### Action: Create a Simple Tracking Doc
|
|
|
|
After each post, note:
|
|
- Topic, format, hook type, length
|
|
- Post time and day
|
|
- Engagement after 1 hour, 24 hours, 1 week
|
|
- Comments quality (superficial vs. substantive)
|
|
- Any patterns you notice
|
|
|
|
After 10 posts, you'll see YOUR patterns emerge. After 30 posts, you'll know exactly what works for YOUR audience.
|
|
|
|
---
|
|
|
|
### 2. Google Trends + Exploding Topics (Weekly Scan)
|
|
|
|
**Purpose:** Catch emerging topics in your domain BEFORE they're mainstream.
|
|
|
|
#### Google Trends (trends.google.com)
|
|
|
|
- Search for topics in your expertise area
|
|
- Look for "Rising" queries (interest growing rapidly)
|
|
- Filter by region if your audience is location-specific
|
|
- Compare related terms to see what's gaining vs. declining
|
|
|
|
#### Exploding Topics (explodingtopics.com - free tier)
|
|
|
|
- Shows topics with exponential growth in search volume
|
|
- Filter by category relevant to your domain
|
|
- Catch signals 3-6 months before they're saturated
|
|
|
|
#### How to Use
|
|
|
|
- Weekly 15-minute scan of your core topics
|
|
- When you spot rising trend, create content WHILE it's still fresh
|
|
- You're now ahead of the documentation curve
|
|
- This is how you stay above average
|
|
|
|
**Example:**
|
|
If you notice "AI agents" search volume growing 400% month-over-month, create content NOW. By the time it's in mainstream LinkedIn advice (6 months later), you've already established authority.
|
|
|
|
---
|
|
|
|
### 3. Reddit + Niche Communities (Weekly Engagement)
|
|
|
|
#### Why This Matters
|
|
|
|
LinkedIn content is filtered and polished. Reddit discussions are raw and unfiltered. The real problems, frustrations, and questions live in niche subreddits BEFORE they become LinkedIn posts.
|
|
|
|
#### Strategy
|
|
|
|
- Find 3-5 subreddits in your domain (e.g., r/artificial, r/MachineLearning, r/DevOps)
|
|
- Lurk daily, post rarely
|
|
- Watch for recurring questions, debates, frustrations
|
|
- These become your content ideas
|
|
|
|
#### What You're Mining
|
|
|
|
- Problems people actually have (not problems you think they have)
|
|
- Language people actually use (not industry jargon)
|
|
- Debates with strong opinions (contrarian angles)
|
|
- Questions that get asked repeatedly (unmet need)
|
|
|
|
#### Content Creation from Reddit
|
|
|
|
1. Spot recurring frustration in subreddit
|
|
2. Develop your perspective on it (based on your expertise)
|
|
3. Create LinkedIn post addressing it
|
|
4. You're solving a real problem before it's "average advice"
|
|
|
|
**Examples:**
|
|
- r/datascience discusses "model deployment frustration" weekly
|
|
- You write: "Why 80% of ML models never reach production (and what to do about it)"
|
|
- You're addressing real pain point, not generic "AI is transforming business"
|
|
|
|
---
|
|
|
|
### 4. Personal Knowledge System (Daily Practice)
|
|
|
|
**Purpose:** Connect non-obvious dots that create unique insights.
|
|
|
|
**Free option:** Obsidian (obsidian.md)
|
|
**Paid option:** Notion ($10/month)
|
|
|
|
#### How It Generates Exceptional Content
|
|
|
|
Most content is obvious because it draws from single sources. Exceptional content connects ideas from disparate domains.
|
|
|
|
#### System
|
|
|
|
1. Capture insights from your work daily (what you learned, observed, struggled with)
|
|
2. Tag by theme/topic
|
|
3. Review weekly to spot connections
|
|
4. Non-obvious connections = unique perspectives
|
|
|
|
#### Example of Unique Connection
|
|
|
|
- Note from AI project: "Stakeholders resist AI because it feels opaque"
|
|
- Note from cooking: "People trust recipes with step-by-step photos"
|
|
- Connection: "Why AI adoption needs 'recipe thinking' - making the black box transparent through step-by-step explanation"
|
|
|
|
This insight didn't exist in "AI best practices." It came from connecting two unrelated domains. That's exceptional content.
|
|
|
|
#### Weekly Practice
|
|
|
|
- 10 minutes daily: Capture 2-3 observations from your work
|
|
- 30 minutes weekly: Review notes, spot connections, generate post ideas
|
|
- This systematic practice generates 10-20 unique content angles per month
|
|
|
|
---
|
|
|
|
### 5. Structured Experimentation (Ongoing)
|
|
|
|
#### The Difference Between Average and Exceptional
|
|
|
|
- **Average:** Follow documented best practices
|
|
- **Exceptional:** Test hypotheses to discover what works next
|
|
|
|
#### Experimentation Framework
|
|
|
|
**Hypothesis:** "My audience engages more with vulnerability-based hooks than data-based hooks"
|
|
|
|
**Test:** Create 2 posts on same topic, different hooks
|
|
- Post A: "I failed at implementing AI. Here's what I learned."
|
|
- Post B: "73% of AI projects fail. Here's why."
|
|
|
|
**Measure:** First-hour engagement, comment quality, saves
|
|
|
|
**Learn:** Document which worked and why
|
|
|
|
**Iterate:** Apply learning to next test
|
|
|
|
#### What to Test
|
|
|
|
- Hook types (vulnerability vs. data vs. contrarian vs. question)
|
|
- Content structure (story vs. framework vs. list)
|
|
- Length (1,200-1,800 characters optimal range)
|
|
- Posting times (your 8am vs. 2pm vs. 6pm)
|
|
- Topic angles (tactical vs. strategic vs. philosophical)
|
|
- CTA types (question vs. invitation vs. challenge)
|
|
|
|
#### Track in Simple Spreadsheet
|
|
|
|
| Post Topic | Hypothesis | Variables | Results | Learning |
|
|
|------------|-----------|-----------|---------|----------|
|
|
| AI adoption | Vulnerability hooks work better | Hook type A vs B | A: 45 eng, B: 23 eng | Vulnerability wins for this audience |
|
|
|
|
After 10 experiments, you know YOUR audience better than any generic advice can tell you.
|
|
|
|
---
|
|
|
|
## Integration: From Tools to Edge
|
|
|
|
### Month 1-3: Establish Baseline
|
|
|
|
- Post consistently (3x/week minimum)
|
|
- Track everything in LinkedIn Analytics
|
|
- Note YOUR patterns
|
|
- Build knowledge capture habit
|
|
|
|
### Month 4-6: Discover YOUR Edge
|
|
|
|
- Identify YOUR best-performing topics/formats/times
|
|
- Begin structured experimentation
|
|
- Mine Reddit/communities for real problems
|
|
- Connect dots in knowledge system
|
|
|
|
### Month 7+: Operate at Edge
|
|
|
|
- Post based on YOUR data, not generic advice
|
|
- Catch emerging trends before they're mainstream
|
|
- Create content from unique connections
|
|
- Test new hypotheses continuously
|
|
|
|
---
|
|
|
|
## The Compounding Effect
|
|
|
|
- Month 1: You're learning mechanics (baseline)
|
|
- Month 3: You understand YOUR patterns (above average)
|
|
- Month 6: You're discovering insights from practice (exceptional)
|
|
- Month 12: You're systematically generating unique perspectives (recognized expert)
|
|
|
|
---
|
|
|
|
## Remember
|
|
|
|
These tools don't make you exceptional. They reveal the patterns and signals that help you develop YOUR unique insights. The actual edge comes from:
|
|
- Your real work and experience
|
|
- Your unique combination of expertise
|
|
- Your authentic perspective
|
|
- Your willingness to experiment
|
|
|
|
Use these tools to avoid reinventing known patterns while you discover unknown ones.
|
|
|
|
---
|
|
|
|
## Tool Investment Guidance
|
|
|
|
### Start Free (Months 1-3)
|
|
|
|
- LinkedIn Analytics (essential)
|
|
- Google Trends (weekly)
|
|
- Reddit (weekly)
|
|
- Obsidian (daily notes)
|
|
|
|
### Consider Paid (After 3+ months consistent posting)
|
|
|
|
- Shield or Taplio (~€50/month) for deeper analytics
|
|
- Focus on ONE paid tool maximum
|
|
- Most value comes from free tools + consistent usage, not expensive software
|