Community Management logo

Community Management

Organization
social-media-skills
community-management

Use to build a genuine community — turning an audience into people who interact with each other, not just with the brand. Run when the user asks how to build community, foster belonging/loyalty, create superfans, start a group/Discord channel, get members talking to each other, or move beyond chasing followers/virality. Reads brand-profile/social-strategy/audience-research. Community outperforms virality, measured by relationship depth/retention, not follower count. Covers the audience-to-community distinction (member-to-member interaction), choosing the space (in-feed -> semi-owned -> owned groups/Discord), recurring rituals, building belonging, and nurturing superfans into advocates. KPIs read natively, never fabricated. Advisory: WoopSocial schedules the content but does NOT run Discord/Slack or show analytics — the human runs the community, the agent drafts. Never fake community; get consent before spotlighting. Pairs with reply-and-comment-writer and crisis-and-moderation.

Overview

Publishersocial-media-skills
Repositoryskills
Skill namecommunity-management
Stars
215
Forks
44
Bundled files
5
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.

  • 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 social-media-skills on GitHub. Read the source before you install it.

Installation

Install the Community Management 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/social-media-skills/skills/tree/main/skills/community-management
  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/social-media-skills/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/community-management .claude/skills/community-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Community Management 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 Community Management 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 Community Management 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.

Community Management

Turn an audience into a community — the strategy layer of the engagement cluster. The tactical skills handle the brand talking to people (reply-and-comment-writer = craft, engagement-routine = cadence, crisis-and-moderation = when it goes wrong). This skill builds the space where members talk to each other.

The 2026 shift is broadcasting → belonging: community outperforms virality, it's algorithm-proof (closed spaces aren't suppressed by feed changes), and it's measured by relationship depth, not follower count. A real community is a moat; an audience is rented.

Step 0 — Read the context

Load brand-profile.md, social-strategy.md, audience-research.md: who these people are, what they care about, and where they already gather.

Step 1 — Diagnose: audience or community?

The marker is member-to-member. Diagnostic: is your comment section all brand replies, or are members talking to each other? The goal is a self-sustaining space where members help each other. Then define the purpose — the clear reason this community exists (no purpose → it plateaus). See references/audience-to-community.md.

Step 2 — Choose the space (by audience + purpose, not hype)

Pick along the spectrum — in-feed comment culture → semi-owned (broadcast channels/DMs) → owned (groups/Discord/Substack/newsletter) — by where the audience already is and whether there's a clear promise to join. Don't launch a Discord because a competitor did. Max out in-feed first; graduate the engaged into an owned space you can commit to (it needs moderation from day one → crisis-and-moderation). See references/where-community-lives.md.

Step 3 — Build belonging with rituals

Run the seven moves (initiate conversations, exclusive spaces, shared identity/inside language, respond personally, feature members, surprise-and-delight, facilitate member-to-member connections) on a backbone of recurring rituals (weekly thread, AMA, challenge, member spotlight, resource drop) — consistency over scattered activity. Schedule the ritual content via scheduling-and-queue. See references/building-belonging.md.

Step 4 — Nurture superfans

Find the devoted core, nurture them (recognition, access, status/roles, collaboration), and elevate them into advocates (lurker → regular → advocate). A small core beats broad reach. See references/superfans-and-honest-scope.md.

Measure community, not vanity

Active members, returning members/retention, member-to-member interaction, response time, advocacy — not follower count. Read natively (no WoopSocial analytics); never fabricate a number. Community is a long game — judged over months.

Honest scope (always)

  • WoopSocial schedules the community content (rituals, prompts, spotlights, announcements) via scheduling-and-queue — that's its real role. It does not run Discord/Slack/groups, manage a broadcast channel or inbox, identify superfans from data, or show analytics. The human runs the space in-platform; the agent advises + drafts/schedules.
  • No fake community — no bought members, pods, or astroturfing (fake, detected, trust-destroying).
  • Consent & privacy — get permission before spotlighting a member; don't expose member data.
  • Sustainable — don't open a space you can't keep tending (boundaries → engagement-routine).

Quality bar — self-check

  • Did I diagnose audience vs community (member-to-member marker) and define a purpose?
  • Did I choose the space by audience + purpose, not hype, and max out in-feed before an owned space?
  • Did I design recurring rituals + belonging moves that get members connecting with each other?
  • Did I plan to nurture superfans into advocates, and measure by community signals (not vanity, not fabricated)?
  • Did I keep it advisory + genuine (WoopSocial schedules content; human runs the space; no fake community; consent)?

Edge cases & pushback

  • "Big following = community?" → no; the marker is member-to-member, self-sustaining interaction.
  • "Competitor has a Discord, should we?" → purpose gate + where the audience is; commitment/moderation.
  • "How do I measure it?" → community signals read natively; no vanity, no fabricated numbers.
  • "Buy members / run a pod to look active" → refuse; genuine belonging only; consent for spotlights.
  • "Run our Discord / find my top members via WoopSocial" → WoopSocial schedules content, doesn't run spaces/inbox or show analytics; human runs it, agent drafts.

Related

  • reply-and-comment-writer — the personal replies that build belonging; engagement-routine — the cadence + boundaries.
  • crisis-and-moderation — protecting/moderating the community; content-pillars — a "community" pillar.
  • scheduling-and-queue — schedule the ritual/spotlight content; profile-optimization — the community's front door.
  • audience-research, social-strategy, brand-profile — who the community is for and why.

References

  • references/audience-to-community.md — the core distinction, why it matters, purpose, and community KPIs.
  • references/where-community-lives.md — the in-feed → semi-owned → owned spectrum and choosing by audience + purpose.
  • references/building-belonging.md — the seven moves, the rituals engine, UGC/live, and how WoopSocial helps.
  • references/superfans-and-honest-scope.md — finding/nurturing/elevating superfans, and the genuine-only honest scope.

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 Community Management AI skill do?

Use to build a genuine community — turning an audience into people who interact with each other, not just with the brand. Run when the user asks how to build community, foster belonging/loyalty, create superfans, start a group/Discord channel, get members talking to each other, or move beyond chasing followers/virality. Reads brand-profile/social-strategy/audience-research. Community outperforms virality, measured by relationship depth/retention, not follower count. Covers the audience-to-community distinction (member-to-member interaction), choosing the space (in-feed -> semi-owned -> owne...

Why use Community Management on TypingMind?

Because you install it once and use it with any model. Community Management 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 Community Management in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/social-media-skills/skills/tree/main/skills/community-management. 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 Community Management?

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 Community Management?

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

Is the Community Management AI skill free?

Yes. It is published on GitHub by social-media-skills 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.

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