Ln 64 Community Announcer logo

Ln 64 Community Announcer

Community
levnikolaevich
ln-64-community-announcer

Drafts or publishes authorized, fact-checked GitHub Discussions announcements; does not create releases.

Overview

Publisherlevnikolaevich
Repositoryclaude-code-skills
Skill nameln-64-community-announcer
Stars
565
Forks
84
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Ln 64 Community Announcer 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/levnikolaevich/claude-code-skills.git /tmp/claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-skills/plugins/delivery-suite/skills/ln-64-community-announcer .claude/skills/ln-64-community-announcer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ln 64 Community Announcer 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 Ln 64 Community Announcer 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 Ln 64 Community Announcer 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 Announcer

Goal: Create a source-backed announcement and publish it only after the user approves the full draft.

Execution contract: The checklist defines completion. Track each item internally as PENDING, PROVEN with evidence, CLEARED with evidence its condition is absent, or UNPROVEN with a gap; reading, delegation, or tool failure is not proof. Reconcile after each section. Before returning, resolve all PENDING, count only PROVEN and CLEARED, and apply verdict and approval rules to every gap. Preserve intent, scope, and existing authorization. Continue authorized work; ask only for consequential unresolved choices or required external approval. Scale depth to material risk without skipping checks. Preserve dependency and safety order; otherwise choose an appropriate verification method. Accept equivalent user or repository evidence; no other skill, named artifact, or complete lifecycle is required. Preserve source requirement and decision IDs. Bind reused evidence to relevant source versions, dirty changes, configuration, and environment; invalidate only affected claims. On continuation, reconcile task, authorization, current state, and unresolved evidence. For long work, return a compact continuation record or update an already authorized artifact; read-only skills do not persist it. Distinguish artifact readiness, verified behavior, and external-action authority. Prepare authorized work before required approval. If blocked by an instruction, cite its exact source and unresolved boundary; do not invent approval gates from caution.

Tool Routing

NeedPreferred capabilityFallback
Repository identity and permissionsAuthenticated GitHub CLI or connectorPublic GitHub API for read-only discovery
Discussion categories and publicationGitHub GraphQL APIContinue a draft with destination limitations; publication is BLOCKED without this capability
Shipped-change evidenceRemote Git history, releases, and canonical filesClean remote clone
Commands, paths, names, and countsFocused repository search and direct readsHosting API content reads
Current external claimsPrimary dated sourcesOmit the claim when it cannot be verified
Draft publicationTemporary Markdown file passed to the APISafely escaped API input with read-back verification

Prefer the hosting API over scraping rendered pages. Use the browser only to inspect presentation or a page the API cannot expose.

Do not expose repository tokens, category node IDs, or other credentials in the announcement. IDs may be used for the mutation but are not audience content.

Checklist

Establish scope and evidence

  • Resolve the target repository from the request or hosting client and determine whether the task is draft-only or includes publication.
  • Read the default branch and source evidence. Discover Discussions availability and a suitable category when accessible; missing publication access does not prevent a fact-checked draft.
  • For draft-only work, report destination limitations and continue. Before publication, require write access, repository/category IDs, and a suitable enabled Discussions category; do not silently publish elsewhere.
  • Identify the announcement subject from the user's request, a release, or a bounded commit range.
  • Read repository communication guidance when present, but do not require a community strategy file.
  • Read the current authoritative documentation, installation instructions, affected manifests, release notes, and relevant source diffs when present.
  • Inspect recent commits and up to three available comparable announcements for cadence, repeated claims, and house style; do not require prior announcements in a new or low-volume repository.
  • Pin shipped claims to the target remote commit or release, including release lines behind the default branch; never present an unpushed local diff as available.
  • Separate what shipped, what changed for users, migration needs, and future intent.

Classification and Style

  • Classify the announcement as release, breaking change, feature update, architecture change, or community news.
  • Choose the shortest style that explains the user outcome and its significance.
  • Match the project's voice while keeping claims understandable without internal repository context.

Draft

  • Write a specific title that names the outcome rather than saying only "update" or "announcement".
  • Open with the user problem or the most important shipped outcome.
  • Explain why the change was made using evidence from commits, docs, or release notes.
  • Summarize highlights in outcome language and name exact products, packages, plugins, skills, or components where useful.
  • Include verified installation or update commands when the reader must act.
  • Make breaking changes prominent and provide clear before-and-after migration steps; follow repository format rather than requiring a particular callout label.
  • Link to canonical documentation at the default branch or immutable release tag as appropriate.
  • Include a short "What's next" section only for committed or clearly labelled tentative work.
  • Thank verified external contributors by handle; omit the section for solo work.
  • End with one genuine, answerable feedback question when community input would be useful.

Fact Check

  • Verify claims about availability and measured effects against the actual release, deployment, or outcome evidence; distinguish announced plans from available capabilities.
  • Verify every command against current authoritative documentation and any stable package, plugin, or marketplace identifier it uses.
  • Verify every named product, package, plugin, skill, component, file path, category, version, tag, and link against the remote repository.
  • Recompute every count from the target commit; avoid counts when they add no user value.
  • Confirm feature descriptions against the actual changed source, documentation, or manifest, not commit-title shorthand.
  • Confirm removed capabilities and compatibility limits are stated explicitly.
  • Do not invent adoption, download, search-volume, performance, compatibility, or roadmap claims.
  • Label estimates, interpretations, and future intent instead of presenting them as shipped facts.
  • Remove generic hype, repeated conclusions, excessive headings, canned transitions, and symmetrical filler lists.
  • Re-read the draft as a new user and ensure it explains both the value and any required action.

Approval and Publication

  • Present the exact title and full Markdown body to the user before creating external state.
  • Include the evidence range and selected category, or the unresolved destination limitation, with the draft.
  • For draft-only work, complete the self-check and report DRAFT READY without waiting; clear publication-only items with this scope reason. For publication, require explicit approval of the exact copy and destination; reuse unchanged approval already given in the session. A request to announce does not approve unseen final copy.
  • If the user changes any substantive claim or instruction, fact-check the revised draft again.
  • Write the approved body to a temporary file to preserve formatting and avoid shell interpolation errors.
  • Only for authorized publication, verify write access and destination, then publish through the GitHub Discussions GraphQL mutation using discovered repository and category IDs. Clear publication-only criteria when the request ends at a draft.
  • Read the discussion back and verify title, body, category, and URL. After an uncertain response, inspect the returned ID or matching destination/content before retrying; stop if identity remains ambiguous rather than risk a duplicate.
  • Remove the temporary draft safely after verification.
  • Report the discussion URL and any manual action, such as pinning, that the API cannot perform.
  • Do not create tags, releases, issues, comments, or cross-posts unless explicitly requested.

Verdict

  • DRAFT READY — fact-checked copy is complete; publication is either outside scope or awaiting exact-copy approval.
  • PUBLISHED — the approved discussion exists and was read back successfully.
  • BLOCKED — source evidence prevents a trustworthy draft, or a requested publication cannot proceed because its permissions, category, or verification is unavailable. Distinguish a completed draft from blocked publication.

Self-Check

  • Reconcile before returning. Check item-level evidence, requirement coverage, contradictions, scope, verdict, and applicable cleanup. Correct the report or authorized artifacts. Reuse valid evidence; do not automatically rescan the repository or rerun successful commands. Repeat checks only for relevant changes, failures, or unresolved evidence. Disclose remaining gaps.

Output Contract

Report in the user's language, in this order; retain all five fields and state each fact once. Small results may use one line per field; omit empty tables and do not copy linked artifacts:

  1. Result: Skill-specific verdict and supported outcome.
  2. Scope: Reviewed/changed scope, exclusions, baseline, and material assumptions.
  3. Evidence: Skill-specific fields below; distinguish facts, inferences, and unverified claims. Link artifacts; use tables when useful.
  4. Verification: Checks/results, unavailable evidence, and applicable cleanup/external state.
  5. Completion: Checklist: X/Y complete; Incomplete: None or each UNPROVEN item's reason, outcome impact, and exact next action; residual risks and required decisions.

Skill-specific evidence: Announcement classification, evidence range, destination/category or limitation, exact title, full fact-checked Markdown draft before approval, and fact-check summary. After publication, give the discussion URL and read-back verification while retaining approved copy in the response history. Report manual actions such as pinning; a mismatch with approved copy is BLOCKED and requires approval before editing/deleting external content.

Frequently asked questions

What does the Ln 64 Community Announcer AI skill do?

Drafts or publishes authorized, fact-checked GitHub Discussions announcements; does not create releases.

Why use Ln 64 Community Announcer on TypingMind?

Because you install it once and use it with any model. Ln 64 Community Announcer 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 Ln 64 Community Announcer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/levnikolaevich/claude-code-skills/tree/master/plugins/delivery-suite/skills/ln-64-community-announcer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ln 64 Community Announcer?

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 Ln 64 Community Announcer?

As many as you like. As long as a model supports skills, you can use Ln 64 Community Announcer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ln 64 Community Announcer AI skill free?

Yes. It is published on GitHub by levnikolaevich 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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