post-scorer

---
name: post-scorer
description: >
license: MIT
---

# Post Scorer

## CRITICAL: Auto-start on load

When this skill triggers, go straight to Step 1. Do not summarise. Do not explain the scoring method. Start immediately.

## Step 1. Get the post

If the user already pasted a post in the same message, use it. Otherwise say:

> Paste the LinkedIn post you want scored.

Wait for the post.

## Step 2. Load scoring data

The scorer needs two things: the user's voice system and real performance data.

### Voice system

Read about-me.md and voice.md from the project if they exist. If missing, note it and score without voice matching.

### Performance data

Check for cached LinkedIn data in the project or outputs folder. Look for files matching *-all-posts.json or *-posts.txt.

If cached data exists, use it. If not, ask the user:

```json
[
  {
    "question": "To score your post against real data, I need your LinkedIn history. How should I get it?",
    "header": "Data source",
    "multiSelect": false,
    "options": [
      {"label": "Scrape my posts", "description": "Pull my last 100 posts from LinkedIn via Apify. Takes 1 to 2 minutes, costs about $0.50."},
      {"label": "Use Charlie Hills data", "description": "Score against Charlie Hills benchmarks (1,872 avg engagement, 500 posts analysed). Good fallback."},
      {"label": "Skip data scoring", "description": "Score against generic best practices only. Less accurate but instant."}
    ]
  }
]
```

If "Scrape my posts":
1. Ask for their LinkedIn username
2. Call Apify actor apimaestro/linkedin-profile-posts with input: { "username": "[their-username]", "total_posts": 100 }
3. Download results (do NOT use the fields parameter, it strips engagement data)
4. Save as [username]-all-posts.json in the project
5. Proceed to analysis

If "Use Charlie Hills data":
Look for cached Charlie data at **/linkedin-data/charlie-all-posts.json. If found, use it. If not, note you are using the benchmarks from this skill file (listed below).

If "Skip data scoring":
Fall back to voice-system-only scoring and general best practices.

## Step 3. Analyse the top performers

When performance data is available, run this analysis before scoring:

1. Calculate engagement score for every post: total_reactions + (comments x 3)
2. Identify the top 10% of posts by engagement score
3. From those top posts, extract:
   - Hook types that appear most often (contrarian, number-led, bold claim, personal story, question, news)
   - Average post length (word count)
   - Format distribution (text only, image, carousel, video)
   - CTA patterns (newsletter mention, comment gate, repost ask, question, none)
   - Topic clusters that over-index on engagement
   - Sentence rhythm (average sentence length, paragraph breaks per post)
4. Also note the bottom 10% patterns to identify what fails

Save these patterns as a "scoring profile" you reference for each criterion.

## Step 4. Score the post

Score across 5 criteria. Each scored 1 to 10.

### Hook strength (1 to 10)

Compare the draft's opening line to the hook types in the top 10%.
- Does it use a hook type that historically performs for this author?
- Is it specific with a number, name, or concrete detail?
- Would it stop a scroll based on what actually stops scrolls in their data?
- Score 8+ only if the hook type matches a pattern in their top 10%

### Voice match (1 to 10)

If voice.md exists:
- Does the post match tone, rhythm, sentence length from voice.md?
- Does it violate any rule in voice.md's absence patterns section (what the voice never does)?
- Does the sentence length match the average from their top performers?
If no voice files: score against the patterns extracted from their post data.

### Value density (1 to 10)

Compare to the user's top-performing posts:
- Do their best posts teach, give steps, share data, or tell stories?
- Does this draft match that value pattern?
- Is the takeaway specific enough that someone would save or share it?
- Compare word count to their top 10% average. Flag if way over or under.

### Structure and format (1 to 10)

Based on their data:
- What format (text, image, carousel) gets the most engagement for them?
- Does the draft's structure match the line break and paragraph rhythm of top posts?
- Is the post scannable on mobile?
- Does the CTA match patterns from their best performers?

### Publish readiness (1 to 10)

- Did the user actually write this or does it read like unedited AI output?
- Would this post blend naturally into their feed based on their posting history?
- Are there any red flags: banned words listed in voice.md's absence patterns, generic phrases, corporate tone?
- Is it the right length compared to their top performers?

## Step 5. Output the scorecard

Output in a code block:

```
LINKEDIN POST SCORE

Data source: [their posts / Charlie Hills benchmarks / generic]
Posts analysed: [number]
Top 10% avg engagement: [number]

Hook strength:         [X] / 10  [hook type detected]
Voice match:           [X] / 10
Value density:         [X] / 10
Structure and format:  [X] / 10  [format: text/image/carousel]
Publish readiness:     [X] / 10
----------------------------------------
TOTAL:                 [XX] / 50

VERDICT: [One sentence referencing specific data]

TOP PERFORMER COMPARISON:
Your top posts average [X] words, use [hook type] hooks,
and include [CTA pattern]. This draft [matches/differs] because [specific reason].

FIXES:
1. [Specific fix backed by data, e.g. "Your top 10% posts open with numbers. This opens with a question. Switch to a stat."]
2. [Second fix backed by data]
3. [Third fix if needed]
```

Every fix must reference the user's actual data. Not "improve the hook" but "your top 10% posts use number-led hooks (42% of hits). This draft uses a question hook (12% of hits). Lead with the stat instead."

## Step 6. Offer next steps

After the scorecard:

> Want me to rewrite the weakest section using patterns from your top posts, or ship it?

If rewrite requested, apply the fixes and output the revised post in a code block.

## Fallback benchmarks (when no data available)

Use these Charlie Hills benchmarks as the scoring baseline when the user picks "Use Charlie Hills data" and no cached file is found:

Average engagement: 1,872 (reactions + comments x 3)
Average reactions: 808
Average comments: 355
Average reposts: 61
Comment-to-reaction ratio: 44%

Top hook types: number-led (31%), bold claim (27%), contrarian (18%)
Top formats: carousel (33%), image (29%), text only (22%)
Average post length top 10%: 180 to 250 words
CTA rate: 45% mention newsletter
Comment gate rate: 5%

## Rules

- Always try to use real data before falling back to generic advice.
- Every score and every fix must reference specific data points, not subjective opinions.
- Never score higher than 8 unless the draft genuinely matches top 10% patterns.
- Be honest. A generous scorer is useless.
- If data is stale (14+ days old), suggest a refresh before scoring.
- Inform the user before running an Apify scrape (costs money).
- Never use em dashes in any output.
- British English throughout.
- Keep the scorecard compact. It needs to look good on a big screen at events.