Agent skill
add-new-entry
Workflow and tools for adding new entries from temp.md to the section files. Includes legend format, section reference, code tools, and common pitfalls. USE FOR: Adding new resources to the knowledge base. DO NOT USE FOR: Editing existing entries or restructuring sections.
Install this agent skill to your Project
npx add-skill https://github.com/kimtth/awesome-azure-openai-llm/tree/main/.agent/skills/add-new-entry
SKILL.md
Workflow: Adding New Entries from temp.md
temp.md is the raw input — an unformatted checklist of URLs and short notes. The goal is to produce temp_entries.md as a properly formatted staging file ready to paste into the target section files.
Steps in order:
- Classify each URL → determine which section file (
azure.md,applications.md,models_research.md,best_practices.md) and which section heading it belongs to. - Fetch descriptions — use
code/fetch_github_description.pyfor GitHub repos. For arXiv papers and blog/web links, usefetch_webpageto extract a one-sentence description. - Fetch creation dates — use
code/get_github_dates.pyfor GitHub repos. For arXiv, derive the date from the ID prefix (e.g.,2602.xxxxx→ Feb 2026). For blog posts, read from the page. - Add star badges — use
code/add_github_stars.pyfor all GitHub links. - Apply legend symbols — see the Legend Format section below. The format differs between
azure.mdand all other files. - Shorten descriptions — keep each description to ≤15 words. One punchy sentence. Do not repeat the link name.
Legend Format
azure.md — dash-bullet, symbol before description
- [OPTIONAL_PREFIX][Name](url) - SYMBOL_Description. (Mon YYYY) 
- The legend symbol (✍️, etc.) is placed before the description, separated by a space from the dash.
- Optional prefix emoji (🏛️ for Microsoft OSS, 🏬 for Azure-Samples) goes before the link text, outside the brackets.
- Date is in
(Mon YYYY)parentheses format with no brackets. - Star badge goes at the end of the line, after the date.
Examples:
- [Azure ML Prompt Flow](https://learn.microsoft.com/...) - ✍️Visual designer for prompt orchestration and evaluation. (Jun 2023)
- 🏬[APIM-Sample](https://github.com/Azure-Samples/APIM-Sample) - Single APIM endpoint for multiple models. (Jan 2026) 
applications.md, models_research.md, best_practices.md — numbered list (or dash), symbol appended to link text
1. [Name](url): Description. [Mon YYYY] 
or (for entries that use dash bullets in that section):
- [Name✍️](url): Description. [Mon YYYY]
- The legend symbol is appended inside the link text, immediately after the name (no space before the symbol).
- Date is in
[Mon YYYY]square-bracket format. - Star badge goes at the end of the line, after the date.
- Use numbered list (
1.) when the surrounding section uses numbered lists; dash (-) when not.
Examples:
1. [Auto-Claude](https://github.com/AndyMik90/Auto-Claude): Autonomous multi-session AI coding. [Dec 2025] 
1. [Towards AI Search Paradigm📑](https://arxiv.org/abs/2506.17188): Modular 4-agent system using DAGs for retrieval-intensive search. [Jun 2025]
- [Claude Code Security✍️](https://www.anthropic.com/news/claude-code-security): Claude Code on the web for scanning codebases. [Feb 2026]
Legend Symbols
| Symbol | Meaning |
|---|---|
| ✍️ | Blog post / documentation / web page |
| 📑 | Academic paper (arXiv) |
| 📺 | Video content |
| 🤗 | Hugging Face resource |
| 🏛️ | Microsoft official OSS (azure.md prefix only) |
| 🏬 | Azure-Samples org (azure.md prefix only) |
Section Reference
Use exact heading names when labeling entries in temp_entries.md. Format: ## <filename> - <Section Name>:.
azure.md
- Developer Tooling
- Agent Frameworks
- Agent Development
- Microsoft Copilot Products
- Azure OpenAI Samples
- Azure OpenAI Application
- Azure OpenAI Accelerator & Samples
- LLM Frameworks
- Prompt Tooling
- Microsoft Copilot Products
- Copilot Development
- Azure AI Search / Azure AI Services
- Microsoft Research
- Risk & LLMOps
applications.md
- AI Application →
### **AI Application**(contains#### Agent & Application,#### Skill,#### Coding,#### Deep Research,#### Memory,#### Gateway,#### Caching,#### Data Processing) - Agent Protocol →
### **Agent Protocol**(contains#### Model Context Protocol (MCP),#### A2A,#### Computer use) - Vector Database & Embedding →
### **Vector Database & Embedding** - RAG (Retrieval-Augmented Generation) →
## **RAG ...
Tip: The sub-sections under
AI Application(Agent & Application, Skill, Coding, Deep Research) are####headings. Use the exact name, e.g.,applications.md - Skill,applications.md - Coding,applications.md - Agent & Application,applications.md - Deep Research,applications.md - Model Context Protocol (MCP).
models_research.md
- OpenAI Products →
### **OpenAI Products** - Anthropic AI Products →
### **Anthropic AI Products** - Google AI Products →
### **Google AI Products** - AGI Discussion and Social Impact (no explicit heading — look for related entries near end of file)
- Large Language Model Collection
- Reasoning
best_practices.md
- Agent Research →
### **Agent Research** - RAG Research →
### **RAG Research** - Agent Design Patterns →
### **Agent Design Patterns** - Reflection, Tool Use, Planning and Multi-agent collaboration
Code Tools Reference
All tools are in code/. Run with python code/<script>.py.
| Script | Purpose |
|---|---|
fetch_github_description.py |
Fetch GitHub repo descriptions; appends after the link colon. Skips lines that already have a description. |
get_github_dates.py |
Fetch GitHub repo creation date; appends [Mon YYYY] or (Mon YYYY). Skips lines already dated. |
add_github_stars.py |
Append star badge to lines with GitHub links. Skips duplicates. |
fetch_popular_papers.py |
Query Semantic Scholar for top-cited papers; writes section/x_popular_papers.md. |
update_citation_counts.py |
Update citation counts for ranked paper sections via Semantic Scholar. |
check_unused_files.py |
Scan markdown for file refs; move unreferenced files to files/_bak/. |
For arXiv papers and blog posts, fetch_github_description.py does not apply. Use fetch_webpage (agent tool) to retrieve a description from the URL.
Common CLI pattern:
python code/fetch_github_description.py --input temp.md --output temp_with_desc.md
python code/get_github_dates.py --input temp_with_desc.md --in-place
python code/add_github_stars.py --input temp_with_desc.md --in-place
Common Pitfalls (Lessons Learned)
-
Wrong legend placement: In
azure.mdthe symbol (✍️, etc.) precedes the description text after-. In all other files the symbol is appended to the link name inside[Name✍️]. Never mix these two formats. -
Wrong section names: Section labels in
temp_entries.mdmust match the actual heading text in the target file exactly. Check the file before assigning. Do not invent new section names. -
Missing descriptions for non-GitHub links:
fetch_github_description.pyonly works forgithub.comURLs. For arXiv, blog, and product pages, you must fetch the page and write a description manually. -
Verbose descriptions: Keep descriptions to ≤15 words. Do not repeat the name. No trailing "for use with", "that helps you", or similar filler.
-
Date format mismatch:
azure.mduses(Mon YYYY)parentheses. All other section files use[Mon YYYY]square brackets. -
emoji stripping via heredoc: Writing file content via PowerShell heredoc strips emoji characters. Use
replace_string_in_fileormulti_replace_string_in_fileto patch emoji symbols back in if they are lost. -
Star badges on non-GitHub links: Only add star badges to
github.comlinks. Blog posts, arXiv papers, and product pages must not have a star badge.
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Workflow for updating the popular LLM applications pool (section/x_llm_apps.md) using get_app_list_by_github_star.py. Covers full refresh, alternate exports, topic tuning, and common pitfalls. USE FOR: Refreshing the ranked GitHub applications list linked from applications.md. DO NOT USE FOR: Hand-curating application entries inside applications.md or adding GitHub star badges to the generated file.
update-cite-count
Guidelines for updating citation counts for papers in the section files using the `update_citation_counts.py` tool. USE FOR: Updating citation counts for papers listed in the section files to keep information current. DO NOT USE FOR: 1) Adding new papers to the section files; 2) Classifying entries into sections.
update-llm-pool
Workflow for updating the LLM landscape paper pool (section/x_llm_papers.md) using fetch_llm_papers.py. Covers full re-fetch, resume from checkpoint, and adding new topics. USE FOR: Refreshing citation counts, expanding topic coverage. DO NOT USE FOR: Adding hand-curated entries to section files (use add-new-entry), updating RAG/Agent citation sections in best_practices.md (use update-cite-count).
classify-temp-entries-to-section
Classification guidelines for entries in temp_entries.md. Each entry have its own title with the markdown file name and section name in temp_entries.md. USE FOR: Classifying new entries in temp_entries.md into *.md in the section files. DO NOT USE FOR: 1) Adding new entries to temp_entries.md; 2) Moving entries between sections.
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