Organic Growth logo

Organic Growth

Organization
runxhq
organic-growth

Turn bounded search, analytics, site, and market evidence into an evidence-bound SEO and answer-engine growth brief with prioritized actions and a measurement plan, without claiming execution or guaranteed visibility.

Overview

Publisherrunxhq
Repositoryrunx
Skill nameorganic-growth
Stars
105
Forks
109
Bundled files
5
LicenseApache-2.0
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.

  • 5 bundled files

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

  • Open source

    Published by runxhq on GitHub. Read the source before you install it.

Installation

Install the Organic Growth 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.
    https://github.com/runxhq/runx/tree/main/skills/organic-growth
  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/runxhq/runx.git /tmp/runx
mkdir -p .claude/skills
cp -r /tmp/runx/skills/organic-growth .claude/skills/organic-growth
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Organic Growth 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 Organic Growth 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 Organic Growth 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.

Organic Growth

Use this skill when an operator needs to decide what to improve on a site for organic discovery: technical search health, content demand, landing-page performance, conversion quality, or the clarity and citeability that can help a page appear in answer engines.

The output is a decision packet, not a bag of SEO tips. It separates what the evidence shows from what the analyst infers, ranks the next actions by expected impact, confidence, and effort, and says how each action should be measured. No action is executed by this skill.

Evidence comes first

The default analyze runner accepts a bounded objective, the site, optional business constraints, and any combination of:

  • search-performance evidence, such as a sealed google-search-console performance packet;
  • analytics evidence, such as a sealed google-analytics report packet;
  • site or market evidence with a SHA-256 source/content digest, source reference, kind, and bounded extracted summary.

Google is optional. The canonical workflow depends on evidence contracts, not a provider, connector, credential store, or hosted tenant. A local crawler, self-hosted analytics service, another search provider, third-party connector, or Runx-hosted connector can supply evidence when its packet has a stable digest and useful provenance. Connector ids, credential handles, URLs, and tenant configuration never belong in this skill.

The admission step rejects invalid packet schemas, failed validation, missing digests, and unbounded evidence. It gives the analysis agent the rows, metadata, caveats, and business context needed for judgment, capped to an explicit analysis view. The complete source packet remains bound by digest; the model must not pretend a bounded view is exhaustive.

Every material claim and recommended action must cite one or more admitted source digests. Runx's native evidence verifier rejects invented references, changed objective/site identity, empty claims, missing decision fields, and unsupported external-effect claims. A failure becomes needs_more_evidence, not a polished guess.

Reading the combined signal

Search evidence explains discovery: queries, pages, impressions, clicks, CTR, position, freshness, and coverage. Analytics explains behavior and business outcomes under the property's measurement model. Site evidence explains what is actually published, crawlable, structured, internally linked, and supportable.

Use their disagreement. Rising impressions with weak CTR can justify inspecting snippet fit, intent, brand recognition, or SERP composition. Strong clicks with weak engagement can justify checking landing-page promise, speed, navigation, or measurement. Analytics decline without a matching Search Console decline can point toward tagging, channel classification, consent, conversion configuration, or non-Google traffic. These are hypotheses until the relevant evidence is collected.

Compare identical date windows, page identities, search types, dimensions, timezone assumptions, and definitions. Preserve incomplete Search Console data, GA4 thresholding, sampling, (other)-row loss, pagination, and imported-source status as caveats. Do not convert correlation into causation.

SEO and answer-engine work

Technical SEO recommendations require site evidence. Check indexability, canonicalization, robots rules, sitemap membership, status codes, rendering, structured data eligibility, internal discovery paths, and page duplication before prescribing a fix. Search Console URL inspection is a point observation, not authority to mutate the site or request indexing.

Content recommendations should connect a demonstrated audience need to a page, business outcome, and information gap. Avoid producing pages merely because a keyword exists. Consolidation, deletion, template repair, internal linking, or a better primary answer may beat another article.

For answer-engine or "GEO" work, optimize the underlying source quality: unambiguous entities, direct answers, original evidence, clear authorship and dates, machine-readable structure, stable URLs, strong internal context, and claims that another system can verify. There is no reliable switch for AI citations and no basis for guaranteeing inclusion. Treat answer-engine visibility as an observed channel that needs its own evidence, not as a mystical ranking score.

Action packet

A ready packet contains:

  • an executive summary and bounded analysis scope;
  • evidence-bound observations and explicit hypotheses;
  • prioritized opportunities;
  • recommended actions with rationale, source digests, owner type, effort, confidence, expected signal, stop condition, and verification method;
  • a dedicated answer-engine review where evidence supports it;
  • a measurement plan with baselines, leading and business outcomes, comparison windows, and rollback or reconsideration triggers;
  • risks, missing evidence, and the authoritative source inventory.

The action list remains external_status: not_executed. Route implementation to the owning repository or business workflow. Content changes may move through a governed content pipeline; code and template work belongs in the site's repository workflow; publication and provider mutations keep their own approval and readback gates.

A useful operating loop

Run a narrow baseline, choose one evidence-bound action, make the change through its owner, wait for an appropriate observation window, then rerun the same queries. Preserve the original packet digests. Change several variables together only when the operator accepts that attribution will be weaker.

Stop when no source is admitted, page/property identity cannot be aligned, the date windows are incomparable, privacy or pagination caveats defeat the claim, the requested recommendation needs technical/content evidence that is absent, the agent cites a source outside the admitted set, or the objective asks for guaranteed ranking, mass low-value content, deceptive markup, or unapproved publication.

The deterministic module only admits and bounds organic-growth domain evidence. It does not hash evidence, call providers, or implement generic verification. Runx owns native digest provenance, evidence verification, agent receipts, and all downstream effect gates.

Agent task contract

organic-growth-analyze

Return growth_candidate with decision: ready, the exact objective and site_url, executive_summary, observations, hypotheses, opportunities, recommended_actions, geo_review, measurement_plan, claims, risks, missing_evidence, and external_status: not_executed.

Each claim requires a non-empty claim and admitted source_digests. Every recommended action requires non-empty source_digests plus a rationale, priority, confidence, effort, expected_signal, verification, and stop_condition. Mark statements as observed, inferred, or hypothesized. Preserve source caveats, do not invent metrics or causes, and do not claim a change, publication, provider call, ranking result, or AI citation occurred.

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 Organic Growth AI skill do?

Turn bounded search, analytics, site, and market evidence into an evidence-bound SEO and answer-engine growth brief with prioritized actions and a measurement plan, without claiming execution or guaranteed visibility.

Why use Organic Growth on TypingMind?

Because you install it once and use it with any model. Organic Growth 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 Organic Growth in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/runxhq/runx/tree/main/skills/organic-growth. 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 Organic Growth?

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 Organic Growth?

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

Is the Organic Growth AI skill free?

Yes. It is published on GitHub by runxhq under the Apache-2.0 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.

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