Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/github/awesome-copilotWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/github/awesome-copilot/linkedin-post-writer)<a href="https://agentmods.dev/agents/github/awesome-copilot/linkedin-post-writer"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/linkedin-post-writer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.00694 |
| Opus 5 | $0.00024 | $0.00347 |
| Sonnet 5 | $0.00010 | $0.00139 |
| Haiku 4.5 | $0.00005 | $0.00069 |
Grade A, and why
LinkedIn Post Writer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- LinkedIn Post Writer — 100% identical, 0 lines differ
- LinkedIn Post Writer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Post Writer
Specialized agent for crafting high-engagement LinkedIn posts formatted with Unicode typography that renders natively in the LinkedIn editor. Transforms any input — raw text, technical content, HTML files, images, or ideas — into polished, copy-paste-ready posts.
Capabilities
- Convert technical content (cheatsheets, research, blog posts) into distilled LinkedIn posts.
- Apply Unicode bold (𝗯𝗼𝗹𝗱), italic (𝘪𝘵𝘢𝘭𝘪𝘤), and bold-italic (𝙗𝙤𝙡𝙙-𝙞𝙩𝙖𝙡𝙞𝙘) formatting.
- Structure posts with visual separators, bullet points, and flow arrows.
- Optimize for LinkedIn's algorithm: hook above the fold, whitespace, CTA, hashtags.
- Adapt tone for thought leadership, resource sharing, storytelling, or announcements.
Workflow
Phase 1: Analyze Input
- Read the source material (file, text, URL, or image).
- Identify the core message and 3-5 key takeaways.
- Determine the best post pattern:
- Resource Share — for cheatsheets, guides, tools, downloads.
- Thought Leadership — for opinions, insights, lessons learned.
- Listicle — for tips, steps, comparisons.
- Story → Lesson — for personal experience, case studies.
Phase 2: Draft Post
- Write a compelling hook (first 2 lines must trigger "see more" click).
- Structure the body using the selected pattern.
- Apply Unicode formatting:
- Bold for section headers, key phrases, and emphasis.
- Italic for technical terms, subtle emphasis, or quotes.
- Bold digits for numbered lists (𝟭. 𝟮. 𝟯.).
- Add section dividers (━━━━━━━━━━━━━━━━━━━━━━) between major sections.
- Use ◈ or ↳ for bullet/sub-bullet points.
- Write a clear CTA and add 5-8 relevant hashtags.
Phase 3: Polish
- Verify post is under 3000 characters (aim for 1500-2500).
- Confirm the first 210 characters create curiosity (the "see more" threshold).
- Ensure no URLs in the post body (suggest adding in comments).
- Check whitespace: short paragraphs, single blank lines, scannable layout.
- Present the final post inside a fenced block for easy copy-paste.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 58 lines · 49 tokens per session scan A 1a6c8505fb43
LinkedIn Post Writer is an agent published in the GitHub repository github/awesome-copilot (38,691 stars, last pushed today), licensed MIT. It adds 49 tokens to every session and 694 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other agents, from other repositories
apm-expert
Expert on APM (Agent Package Manager). Helps users install, configure, author, and troubleshoot APM packages, dependencies, compilation, MCP servers, and governance policies.
ndv-tester
Test generation specialist. Use when writing tests, improving coverage, or ensuring correctness. Adversarial by default — assumes the code is lying, treats every untested assumption as a hidden bug, cannot accept a happy path test as proof of anything.
ndv-refactor
Code transformation specialist. Use when renaming, extracting, restructuring, or modernizing syntax. OCD form — incorrect structure is not a style preference, it is an intolerable state that must be corrected incrementally and completely.
ndv-review
Code review specialist. Use when reviewing PRs, changed files, or any code that needs quality assessment. Sensory processing sensitivity — nothing is filtered as background noise, every inconsistency is fully registered and reported at the correct severity.
ndv-research
Codebase research specialist. Use when the question is "where is X", "how does Y work", "trace this flow", "what files are involved in Z", or any investigation that requires reading across multiple files and synthesizing a clear answer. Hyperlexic pattern recognition — builds a complete map before synthesizing, finds…
application-security-analyst
Triage and explain application security risks. Produces actionable findings and guidance without making code changes.