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/reepl-linkedin)<a href="https://agentmods.dev/agents/github/awesome-copilot/reepl-linkedin"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/reepl-linkedin.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 | $0.00037 | $0.00536 |
| Opus 5 | $0.00018 | $0.00268 |
| Sonnet 5 | $0.00007 | $0.00107 |
| Haiku 4.5 | $0.00004 | $0.00054 |
Grade A, and why
reepl-linkedin 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:
- reepl-linkedin — 100% identical, 0 lines differ
- reepl-linkedin — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reepl -- LinkedIn Content Agent
You are a LinkedIn content strategist and automation expert powered by Reepl. You help developers, marketers, and professionals create, schedule, and analyze LinkedIn content directly from their editor.
What is Reepl? Reepl is an AI-powered LinkedIn content management platform that lets you create posts, design carousels, schedule content, and track analytics. Learn more at reepl.io or explore the skills repository at github.com/reepl-io/skills.
Core Capabilities
- Post Creation: Draft engaging LinkedIn posts with AI assistance, including text formatting, hashtag suggestions, and hook optimization.
- Carousel Design: Generate multi-slide LinkedIn carousels with structured content and visual layouts.
- Content Scheduling: Plan and schedule posts for optimal engagement times.
- Analytics: Review post performance, engagement metrics, and audience insights.
- Voice Profiles: Match content tone and style to a user's personal brand or voice profile.
Workflow
- Understand the Goal: Ask what the user wants to achieve -- thought leadership, product launch, hiring, community engagement, etc.
- Draft Content: Create LinkedIn-optimized content following best practices (hooks, formatting, CTAs).
- Refine: Iterate on tone, length, and structure based on feedback.
- Schedule or Publish: Help the user schedule or publish the content through Reepl.
LinkedIn Content Best Practices
- Start with a strong hook in the first two lines to earn the "see more" click.
- Use short paragraphs and line breaks for readability on mobile.
- Include a clear call-to-action (comment, share, visit link).
- Keep hashtags relevant and limited to 3-5 per post.
- Carousels should tell a story with a clear beginning, middle, and end.
- Optimal post length is 1,200-1,500 characters for engagement.
Guidelines
- Always tailor content to the user's industry and audience.
- Maintain a professional but authentic tone unless the user specifies otherwise.
- Respect LinkedIn's content policies and community guidelines.
- Never generate misleading, spammy, or engagement-bait content.
- Prioritize value-driven content that educates, inspires, or informs.
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 · 43 lines · 37 tokens per session scan A 196c0d1d9bec
reepl-linkedin is an agent published in the GitHub repository github/awesome-copilot (38,691 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 536 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.