Dfw Content Calendar

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name: content-calendar-pro description: Generate 7-day or 30-day social media content calendars for any niche. Each post is scored for engagement quality (hook strength, specificity, platform fit, CTA clarity) — posts below threshold auto-rewrite. Supports past-performance input to adapt theme weighting toward what worked. Free tier: 7-day calendar. Premium: 30-day, multi-platform, A/B variants, CSV export, engagement tracker. version: 1.2.0 author: drivenautoplex1 price: 0 tags: - content - social-media - marketing - content-calendar - scheduling - real-estate - mortgage - crypto - saas - e-commerce - copywriting - linkedin - twitter - instagram - facebook metadata: openclaw: requires: env: - ANTHROPIC_API_KEY anyBins: - python3 primaryEnv: ANTHROPIC_API_KEY emoji: "📅" homepage: https://github.com/drivenautoplex1/openclaw-skills install: - kind: uv package: anthropic bins: []

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Content Calendar Skill

Generate a full month of social content in minutes. Paste in your niche and audience — get back a structured, ready-to-schedule calendar with varied hooks, body copy, CTAs, and hashtags.

Free vs Premium

Free tier (no API key needed):

--demo — generates a complete 7-day real estate sample calendar, zero API calls

--version — print skill version

--compliance-only — check any copy for forbidden words before posting

Free tier (API key required):

7-day calendar on any platform/niche

Structured JSON + human-readable output

Single platform per run

Premium features (same API key, no upgrade needed):

--days=30 — full 30-day calendars

--platforms=all — LinkedIn + X + Facebook + Instagram in one run

--ab-variants — 2 hook variants per post for A/B testing

--csv — export to CSV for Buffer, Hootsuite, Notion, or any scheduler

--theme — weekly theme override (e.g., "Week 2: objection busting only")

--min-score=75 — quality gate: posts scoring below threshold are auto-rewritten (max 2 passes)

--past-results=results.json — feed in engagement data from previous runs; skill adapts theme weights toward what worked

--tracker — output blank engagement tracker template alongside the calendar (paste into Notion/Sheets)

What this skill does

Generates structured content calendars for any niche with:

Variety enforcement — topics rotate so nothing repeats within 7 days

Platform-aware formatting — character limits, hashtag counts, and tone matched per platform

Content mix — market education, social proof, objection busting, community spotlight, CTAs (5-pillar rotation)

Hook-first structure — every post opens with a pattern interrupt, not a topic statement

JSON + human-readable output — paste JSON into your scheduler; read the human format to review quickly

Post-level quality scoring — every post scored 0–100 across 4 dimensions before output

Auto-rewrite loop — posts below --min-score threshold regenerated with fix instructions (max 2 passes)

Adaptive theme weighting — pass in past engagement data and the skill shifts content mix toward what converted

Supported verticals

| Niche | Example brand voice | |---|---| | Real estate | Market urgency, local expertise, lifestyle | | Mortgage / lending | Education-first, trust-building, compliance-safe | | Crypto / DeFi | Data-driven, opportunity framing, risk-aware | | SaaS / tech | Problem-solution, feature → benefit, ROI focus | | E-commerce | Product-led, social proof heavy, seasonal hooks | | Coaching / consulting | Authority positioning, transformation stories | | Healthcare / wellness | Empathy-first, outcome-focused, evidence-backed | | Any niche | Pass --niche="your niche" and --audience="your audience" |

Input contract

# Minimum — niche and audience
python3 generate_calendar.py --niche="real estate" --audience="first-time buyers"
Full options
python3 generate_calendar.py \
  --niche="mortgage broker" \
  --audience="move-up buyers" \
  --platform=linkedin \
  --days=30 \
  --tone=educational \
  --ab-variants \
  --csv \
  --output=my_calendar.json

Options: | Flag | Default | Description | |---|---|---| | --niche | required | Your brand/industry (e.g. "real estate agent") | | --audience | "general" | Target reader (e.g. "first-time homebuyers") | | --platform | linkedin | linkedin / x / facebook / instagram / all | | --days | 7 | 7 or 30 | | --tone | conversational | conversational / educational / urgent / luxury / bold | | --ab-variants | off | Generate 2 hook options per post | | --csv | off | Export calendar as CSV | | --theme | auto | Weekly theme override | | --min-score | 70 | Quality gate — posts below this score auto-rewrite (0 to disable) | | --past-results | none | Path to engagement JSON from prior run — adapts content mix | | --tracker | off | Output blank engagement tracker template alongside calendar | | --demo | off | Run on built-in sample, zero API calls | | --version | off | Print version | | --format | human | human / json |

Output contract

Human-readable output:

=== 30-Day LinkedIn Calendar: Mortgage Broker → Move-Up Buyers ===
Week 1 Theme: Market Reality
--- Day 1 (Mon) | LinkedIn | Market Education ---
HOOK: Most move-up buyers don't realize they already have the equity to do this.
BODY: If you bought your home 3-5 years ago, you're sitting on an average of $87,000 in
      equity. That's your down payment for the next place — without touching savings.
      The math most people don't run: sell at today's prices, buy at today's prices,
      and the equity gap covers the difference more than you think.
CTA:  Drop your zip code below — I'll run the numbers for your neighborhood.
HASHTAGS: #RealEstate #HomeEquity #MovingUp #Homeownership
CHARS: 487
SCORE: 81/100 | Hook: 26/30 | Platform: 22/25 | Specificity: 20/25 | CTA: 13/20

JSON output (--format=json):

{
  "metadata": {
    "niche": "Mortgage Broker",
    "audience": "Move-up buyers",
    "platform": "linkedin",
    "days": 30,
    "generated_at": "2026-03-27T17:00:00Z"
  },
  "weeks": [
    {
      "week": 1,
      "theme": "Market Reality",
      "posts": [
        {
          "day": 1,
          "weekday": "Mon",
          "platform": "linkedin",
          "content_type": "market_education",
          "hook": "Most move-up buyers don't realize they already have the equity to do this.",
          "body": "...",
          "cta": "Drop your zip code below — I'll run the numbers for your neighborhood.",
          "hashtags": ["#RealEstate", "#HomeEquity", "#MovingUp", "#Homeownership"],
          "char_count": 487,
          "hook_b": null,
          "engagement_score": {
            "total": 81,
            "hook_strength": 26,
            "platform_fit": 22,
            "specificity": 20,
            "cta_clarity": 13,
            "rewrites": 0
          }
        }
      ]
    }
  ]
}

CSV output (--csv, compatible with Buffer/Hootsuite/Notion):

day,weekday,platform,hook,body,cta,hashtags,char_count
1,Mon,linkedin,"Most move-up buyers...","If you bought your home...","Drop your zip code...","#RealEstate #HomeEquity",487

Post Quality Scoring

Every generated post is scored across 4 dimensions before output:

| Dimension | Weight | What's measured | |---|---|---| | Hook strength | 30 pts | Pattern interrupt quality — does it stop the scroll? Scored on: specificity, unexpected angle, curiosity gap | | Platform fit | 25 pts | Character count, hashtag count, tone match, reading level for platform | | Specificity | 25 pts | Concrete numbers, named outcomes, real examples vs. vague generalities | | CTA clarity | 20 pts | Is the ask clear? Is there exactly one action? Is friction low? |

Total: 100 points

Posts scoring below --min-score (default: 70) are automatically rewritten with targeted fix instructions (e.g., "hook is vague — add a specific number or outcome"). Max 2 rewrite passes per post.

Each post in the output includes:

SCORE: 81/100 | Hook: 26/30 | Platform: 22/25 | Specificity: 20/25 | CTA: 13/20

Why this matters: A calendar of 30 posts where 8 have weak hooks will underperform a calendar of 30 well-scored posts. The scoring loop catches and fixes low-quality posts before you schedule them.

Adaptive Theme Weighting

Pass in results from a previous calendar with --past-results=results.json. The skill reads which content types drove the most engagement and shifts the next calendar's mix accordingly.

Tracker format (also generated with --tracker):

{
  "period": "March 2026",
  "platform": "linkedin",
  "posts": [
    {
      "day": 1,
      "content_type": "market_education",
      "impressions": 1240,
      "engagement_rate": 0.048,
      "clicks": 22,
      "score_at_generation": 81
    }
  ]
}

What adapts:

Content mix percentages shift toward top-performing content_type values (e.g., if social_proof drove 3× the engagement, its share increases from 20% → 30% in the next run)

Weak themes are reduced proportionally

Platform formatting rules stay fixed (algorithm constraints don't adapt to your data)

Example shift:

Past results: social_proof avg engagement 6.2% vs. direct_cta 1.1%
Next run mix: social_proof 30% (+10%), direct_cta 12% (−8%)

Engagement Tracker Template

Run with --tracker to get a blank tracker alongside the calendar:

content-calendar-pro Engagement Tracker
Generated: [date] | Platform: [platform] | Niche: [niche]
Day | Weekday | Type           | Hook (first 60 chars)     | Score | Impressions | Eng% | Clicks | Notes
----|---------|----------------|---------------------------|-------|-------------|------|--------|-------
1   | Mon     | market_edu     | "Most buyers don't..."    | 81    | ___         | ___  | ___    |
2   | Tue     | social_proof   | "Client bought in..."     | 88    | ___         | ___  | ___    |
...

Paste into Notion or Google Sheets. After 30 days, export as JSON and pass to --past-results for an optimized next run.

How the skill works

Uses generate_calendar.py in this directory. Local MLX first (free, unlimited), Haiku fallback.

# Base content mix — shifts when --past-results provided
DEFAULT_CONTENT_MIX = {
    "market_education": 0.25,   # Stats, trends, myth-busting
    "social_proof":     0.20,   # Client outcomes, testimonials
    "objection_bust":   0.20,   # Handle common objections
    "community":        0.15,   # Local spotlight, brand building
    "direct_cta":       0.20,   # Urgency-driven asks
}
Adaptive mix: past_results → recalculate weights toward top performers
def adaptive_content_mix(past_results, base_mix=DEFAULT_CONTENT_MIX):
    if not past_results:
        return base_mix
    # Compute avg engagement rate per content_type from past results
    perf = {}
    for post in past_results["posts"]:
        ct = post["content_type"]
        perf.setdefault(ct, []).append(post.get("engagement_rate", 0))
    avg_perf = {ct: sum(v)/len(v) for ct, v in perf.items()}
    # Blend: 60% performance-weighted, 40% base (prevents over-pivoting)
    total_perf = sum(avg_perf.values()) or 1
    new_mix = {}
    for ct in base_mix:
        perf_weight = avg_perf.get(ct, sum(avg_perf.values()) / len(base_mix)) / total_perf
        new_mix[ct] = 0.6 * perf_weight + 0.4 * base_mix[ct]
    # Normalize to 1.0
    total = sum(new_mix.values())
    return {ct: v / total for ct, v in new_mix.items()}
WEEK_THEMES = [
    "Market Reality",       # Week 1 — establish authority with facts
    "Social Proof",         # Week 2 — build trust with outcomes
    "Education",            # Week 3 — deepen value with expertise
    "Urgency & Action",     # Week 4 — close with timing
]
PLATFORM_LIMITS = {
    "linkedin":  {"chars": 3000, "hashtags": 5,  "tone": "professional", "reading_level": "grade_10"},
    "x":         {"chars": 280,  "hashtags": 2,  "tone": "punchy",        "reading_level": "grade_8"},
    "facebook":  {"chars": 500,  "hashtags": 3,  "tone": "community",     "reading_level": "grade_8"},
    "instagram": {"chars": 300,  "hashtags": 10, "tone": "visual-first",  "reading_level": "grade_7"},
}
Scoring system — runs on every generated post
POST_SCORE_WEIGHTS = {
    "hook_strength":  30,   # Pattern interrupt quality: specificity, curiosity gap, unexpected angle
    "platform_fit":   25,   # Char count, hashtag count, tone match, reading level
    "specificity":    25,   # Concrete numbers/outcomes vs. vague generalities
    "cta_clarity":    20,   # Clear single ask, low friction
}
Auto-rewrite: posts below MIN_SCORE get regenerated with targeted fix instructions
MIN_SCORE_DEFAULT = 70
MAX_REWRITE_PASSES = 2

Core generation + scoring prompt:

CALENDAR_SYSTEM = """You are a social media strategist and direct-response copywriter.
Generate {days} days of {platform} content for: {niche} targeting {audience}.
Rules:
Hook first: every post opens with a pattern interrupt — a question, stat, or bold claim
No topic repeats within 7 days
Match platform tone: {platform_tone}
Content mix: {content_mix_instructions}
Week themes: {week_themes}
Keep under {char_limit} characters per post
Reading level target: {reading_level}
{tone_instruction}
Never use: [forbidden words for niche]
Output ONLY valid JSON matching the schema provided."""
SCORER_SYSTEM = """Score this social media post 0-100 across 4 dimensions.
Return JSON: {{"hook_strength": N, "platform_fit": N, "specificity": N, "cta_clarity": N, "total": N, "fix_notes": "..."}}
Weights: hook_strength/30, platform_fit/25, specificity/25, cta_clarity/20.
fix_notes = specific instructions to fix the lowest-scoring dimension (one sentence, actionable).
Platform: {platform}. Char limit: {char_limit}."""
REWRITER_SYSTEM = """Rewrite this post. The issue is: {fix_notes}. Keep: same topic, same CTA intent, same platform.
Change: fix the specific issue identified. Output only the rewritten post JSON."""

Usage patterns

Patrick's 2-hour content schedule method:

# Step 1: Generate 30-day calendar with quality gate + tracker
python3 generate_calendar.py --niche="mortgage broker" --audience="move-up buyers" \
  --platform=linkedin --days=30 --csv --tracker --output=calendar_march.csv
Step 2: Review scored posts in CSV — all posts already meet quality bar
Step 3: Fill in tracker as you post, export results.json after 30 days
Step 4: Next month — optimized calendar based on what worked
python3 generate_calendar.py --niche="mortgage broker" --audience="move-up buyers" \
  --platform=linkedin --days=30 --past-results=march_results.json --csv

Raise the quality bar:

# Default min-score is 70 — set 80 for premium content
python3 generate_calendar.py --niche="SaaS startup" --audience="SMB owners" \
  --days=30 --min-score=80
Posts below 80 get up to 2 rewrite passes — slower but higher quality

Multi-platform week:

# All 4 platforms, 7 days, A/B hooks
python3 generate_calendar.py --niche="real estate" --audience="first-time buyers" \
  --platform=all --days=7 --ab-variants

Crypto trading content:

python3 generate_calendar.py --niche="crypto trader + DeFi analyst" \
  --audience="retail investors" --platform=x --days=30 --tone=bold

Pipeline (JSON → scheduler automation):

python3 generate_calendar.py --niche="SaaS startup" --audience="SMB owners" \
  --format=json | jq '.weeks[0].posts[] | {day, hook, cta}'

Integration with agent infrastructure

# Via Telegram
@openclaw content-calendar "30-day LinkedIn calendar for mortgage broker targeting move-up buyers"
@openclaw content-calendar "7-day X calendar, crypto niche, bold tone"
Via Claude Code
openclaw run content-calendar --niche="real estate" --days=30 --platform=linkedin

Content pillar reference

| Pillar | % of calendar | What it does | Example hook | |---|---|---|---| | Market Education | 25% | Build authority with facts | "Most buyers don't know this costs them $340/month" | | Social Proof | 20% | Build trust with outcomes | "Client bought in [City] with $0 out of pocket. Here's how." | | Objection Busting | 20% | Remove buying resistance | "Waiting for rates to drop? Here's what that's actually costing you." | | Community | 15% | Brand affinity, local presence | "Best kept secret neighborhood in [City] right now" | | Direct CTA | 20% | Drive action | "Drop your zip code — I'll pull what's moving in your area today." |