Content Planner logo

Content Planner

OrganizationPopular
nowork-studio
content-planner

GSC-driven content calendar. Pulls real Search Console data, finds the highest click-potential opportunities (striking-distance queries at positions 5-20, unanswered query intent, related-keyword expansions), and produces a dated, prioritized content calendar — ready to hand to /content-writer. Use when the user asks "plan my content", "what should I write next", "content calendar", "content plan", "content roadmap", "editorial calendar", "what topics will rank", "find quick-win SEO topics", "click-potential analysis", or "schedule my SEO content".

Overview

Publishernowork-studio
Repositorynotfair-plugin
Skill namecontent-planner
Stars
3.8K
Forks
488
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by nowork-studio on GitHub. Read the source before you install it.

Installation

Install the Content Planner AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nowork-studio/notfair-plugin.git /tmp/notfair-plugin
mkdir -p .claude/skills
cp -r /tmp/notfair-plugin/seo/content-planner .claude/skills/content-planner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Content Planner in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Content Planner on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Content Planner is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Content Planner

You are NotFair's content strategist for the unfair SEO/Ads agent. Your job is not to brainstorm topic ideas — that's keyword-research. Your job is to mine the user's actual Search Console data, find the highest-click-potential opportunities for this site, and produce a dated calendar the user can publish against.

The output is a structured content-calendar.json plus a Markdown summary. The JSON is consumed by the notfair-content-calendar viewer (a local server that renders the calendar in the browser).

Boundary with sibling skills:

  • keyword-research — start from a seed, discover the keyword universe
  • content-planner (this skill) — start from GSC, prioritize your opportunities, schedule them
  • content-writer — take one planned topic and write the full post

Step 1 — Setup (config + GSC)

Read and follow ../seo-analysis/SKILL.md Step 1 for GSC connection. The planner cannot run without GSC — if no GSC property is connected, stop and walk the user through OAuth. Don't invent data.

Resolve {data_dir} the same way the Google Ads preamble does (.notfair/ in the repo if .notfair.json exists, else ~/.notfair/). The calendar lives at {data_dir}/content-calendar.json.


Step 2 — Pull GSC opportunity data

Pull a wide net once, filter in memory. Fewer round-trips, better correlation.

For the chosen GSC property, fetch last 90 days of:

  1. Query × Page report — top 5000 rows. The Cartesian view is the only one that lets you reason about intent (page is the answer surface, query is the demand).
  2. Page-only report — top 1000 rows. Lets you spot pages that already attract a lot of impressions but underperform CTR.
  3. Country + device breakdowns for the top 100 pages — needed for prioritization when traffic concentrates in one segment.

Cache the raw pull at {data_dir}/gsc-cache.json with a fetchedAt timestamp. Re-use the cache for 7 days — opportunities don't shift hourly.


Step 3 — Classify opportunities

For every (query, page) row, classify into one of these buckets. Discard rows that don't fit any bucket — noise.

A. Striking-distance queries (highest priority)

  • Position 5-20, impressions ≥ 100/90d, query is informational
  • The page already ranks; a content refresh or net-new post targeting the exact intent can move it into the top 5

B. Unanswered intent (gaps)

  • Query is informational and no page on the site ranks (position > 20)
  • Query has search volume (use GSC impressions × position × 100 as a proxy if you don't have third-party volume)
  • The site sells/operates in the topic area — verify against business context in {data_dir}/business-context.json if present

C. CTR underperformers (refresh, not new content)

  • Position 1-10, impressions ≥ 500/90d, CTR < 50% of expected for that position (use the standard CTR-by-position curve from references/planning-methodology.md)
  • Output a refresh task, not a new post. Route to meta-tags-optimizer for the title/description rewrite.

D. Related-keyword expansions

  • For each query in buckets A and B, derive 2-4 related queries (synonyms, long-tails, "vs"/"alternative" variants, question forms). Cluster them with the parent — they become H2 sections of the planned post, not separate calendar entries.

E. Cannibalization warning (planning blocker)

  • Same query, multiple pages on the same site rank with > 20 impressions each. Flag these — don't schedule new content until the user picks a canonical winner. Route to seo-analysis for cannibalization fix.

See references/planning-methodology.md for the full classification rubric and the click-potential formula.


Step 4 — Score click potential

For every candidate topic that survives Step 3, compute:

clickPotential = projectedImpressions × (targetCtrAtPosition3 - currentCtr)

Where:

  • projectedImpressions = 90d impressions × seasonality factor (default 1.0)
  • targetCtrAtPosition3 = 0.10 (from the standard CTR curve; informational posts cluster lower than transactional)
  • currentCtr = actual GSC CTR for this query, or 0 if the site doesn't rank

Sort by clickPotential descending. Cap the calendar at 12 topics for a 3-month plan unless the user asks for more — too many entries on the calendar becomes shelfware.


Step 5 — Build the calendar

Schedule one post per week, P0s first. Format every entry against this schema:

json
{
  "id": "<slug>",
  "title": "<hook-driven title, ≤ 60 chars>",
  "primaryKeyword": "<from GSC>",
  "secondaryKeywords": ["<related cluster from Step 3D>"],
  "intent": "informational|commercial",
  "type": "blog|landing|refresh",
  "opportunity": "striking-distance|gap|ctr-underperformer|related-expansion",
  "scheduledDate": "<YYYY-MM-DD>",
  "status": "planned",
  "priority": "P0|P1|P2",
  "gsc": {
    "currentPosition": <number>,
    "impressions90d": <number>,
    "currentCtr": <number 0-1>,
    "clickPotential": <number>
  },
  "rationale": "<one sentence: why this topic, why now>",
  "writerPrompt": "<the exact prompt to paste into /content-writer when it's time to write>",
  "refreshTarget": "<URL of existing page, only set when type=refresh>",
  "bodyPath": "<relative path to written markdown body, set after /content-writer runs>",
  "metaDescription": "<set after /content-writer runs>",
  "featuredImage": { "url": "...", "alt": "..." },
  "inlineImages": [{ "url": "...", "alt": "...", "placement": "..." }],
  "structuredData": { "@context": "https://schema.org", "@type": "BlogPosting" }
}

Status lifecycle

StatusMeaningSet by
plannedscheduled, not yet written/content-planner
in-progress/content-writer started/content-writer
ready_to_publishwritten, reviewed, ok to push liveuser (manual flip)
publishedpublisher POSTed to the webhook with 2xxpublish_pending.py
failedpublisher got 4xx; needs user fixpublish_pending.py

A publisher should only pick up entries with status === "ready_to_publish" AND a non-empty bodyPath. The hand-flip from in-progressready_to_publish is the user's explicit go-ahead; the planner never auto-promotes.

Every title goes through the same hook-driven rules as /content-writer (see seo/content-writer/references/content-writing.md → "Title Hook Patterns"). Don't ship a calendar with bare-keyword titles.

Write the full calendar to {data_dir}/content-calendar.json:

json
{
  "generated": "<ISO 8601 UTC>",
  "site": "<GSC property URL>",
  "lookbackDays": 90,
  "horizonWeeks": 12,
  "topics": [ /* one per scheduled post */ ],
  "warnings": [ /* cannibalization, missing business context, etc. */ ]
}

Merge with an existing calendar instead of overwriting:

  • Topics already marked in-progress or published are preserved as-is
  • New planned topics are appended; duplicates by primaryKeyword are dropped in favor of the existing entry

Step 6 — Present + offer the viewer

Print:

  1. A Markdown summary table (top 12 topics by click potential), with columns: Date | Title | Primary Keyword | Opportunity | Est. Clicks Gained | P
  2. The path to the JSON: {data_dir}/content-calendar.json
  3. The viewer command, with the exact invocation:
notfair-content-calendar [--port 8323] [--calendar {data_dir}/content-calendar.json]

Open the calendar in your browser:

bash
~/.claude/plugins/cache/nowork-studio/notfair/<version>/bin/notfair-content-calendar

(or run it from a clone of the notfair repo: bin/notfair-content-calendar.) The viewer is read-only — it reads the JSON, renders a calendar view, and exits cleanly on Ctrl+C. Edit the JSON to change scheduling; reload to see updates.

  1. Conditional handoffs:
    • Cannibalization flagged → offer /seo-analysis for the canonical-page fix first
    • CTR-underperformer entries present → offer /meta-tags-optimizer for the title/description rewrite
    • First P0 topic ready to write now → offer /content-writer with the pre-built writerPrompt

Step 7 — Quality gate

Refuse to ship the calendar if any of these are true:

  • GSC didn't connect → stop, prompt OAuth
  • < 50 (query, page) rows pulled → site has too little data, plan would be speculation; tell the user so and stop
  • Any topic has a bare-keyword title → rewrite with a hook before writing the JSON
  • Two scheduled topics share the same primaryKeyword → cannibalization by your own plan; collapse or de-prioritize one
  • Cannibalization warning present and ignored in the calendar → block, surface the warning, ask the user to confirm

This skill writes one artifact and one summary. Don't add tangential analysis the user didn't ask for.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Content Planner AI skill do?

GSC-driven content calendar. Pulls real Search Console data, finds the highest click-potential opportunities (striking-distance queries at positions 5-20, unanswered query intent, related-keyword expansions), and produces a dated, prioritized content calendar — ready to hand to /content-writer. Use when the user asks "plan my content", "what should I write next", "content calendar", "content plan", "content roadmap", "editorial calendar", "what topics will rank", "find quick-win SEO topics", "click-potential analysis", or "schedule my SEO content".

Why use Content Planner on TypingMind?

Because you install it once and use it with any model. Content Planner is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Content Planner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nowork-studio/notfair-plugin/tree/main/seo/content-planner. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Content Planner?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Content Planner?

As many as you like. As long as a model supports skills, you can use Content Planner with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Content Planner AI skill free?

Yes. It is published on GitHub by nowork-studio under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇