add-new-entry-from-temp-md · diff

git:20260803.ce45f46 to git:20260929.e51f396

15 added, 224 removed. Audit B to A.

---
name: add-new-entry-from-temp-md
- description: "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."
- ---
-
-
- ## 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:**
-
- 1. **Classify each URL** → determine which section file (`azure.md`, `applications.md`, `models_research.md`, `best_practices.md`, `tools_extra.md`) and which current section heading it belongs to.
- 2. **Fetch descriptions** — use `code/fetch_github_description.py` for GitHub repos. For arXiv papers and blog/web links, use `fetch_webpage` to extract a one-sentence description.
- 3. **Fetch creation dates** — use `code/update_github_dates.py` for GitHub repos. For arXiv, derive the date from the ID prefix (e.g., `2602.xxxxx` → Feb 2026). For blog posts, read from the page.
- 4. **Add star badges** — use `code/add_github_stars.py` for all GitHub links.
- 5. **Apply legend symbols** — see the **Legend Format** section below. `azure.md` should not use emoji markers.
- 6. **Shorten descriptions** — keep each description to ≤15 words. One punchy sentence. Do not repeat the link name.
-
- ---
-
- ## Legend Format
-
- ### `azure.md` — dash-bullet, no emojis
-
- ```
- - [Name](url) - Description. (Mon YYYY) ![stars](...)
- ```
-
- - Do not use emoji markers in `azure.md` (no link-prefix emojis and no description-prefix emojis).
- - Date is in `(Mon YYYY)` parentheses format with no brackets.
- - Star badge goes at the end of the line, after the date.
-
- **Examples:**
-
- ```markdown
- - [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) ![**github stars**](...)
- ```
-
- ### `applications.md`, `models_research.md`, `best_practices.md` — numbered list (or dash), symbol appended to link text
-
- ```
- 1. [Name](url): Description. [Mon YYYY] ![stars](...)
- ```
- 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:**
-
- ```markdown
- 1. [Auto-Claude](https://github.com/AndyMik90/Auto-Claude): Autonomous multi-session AI coding. [Dec 2025] ![**github stars**](...)
- 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 |
-
- ---
-
- ## Section Reference
-
- Use **exact** heading names when labeling entries in `temp_entries.md`. Format: `## <filename> - <Section Name>:`.
-
- ### `azure.md`
- - Azure OpenAI & Foundry Overview
- - Orchestration Frameworks
- - Prompt Engineering & Tooling
- - Agent Frameworks
- - Model Training & Inference
- - Safety, Security & LLMOps
- - Data Processing & Memory
- - Dev Tools, MCP & Extensions
- - Copilot Product Catalog
- - Microsoft Foundry & AI Services
- - Azure AI Search
- - Agent Development
- - Microsoft 365 Agent Development
- - Learning Resources & Workshops
- - Microsoft Research
- - Sample Applications
- - Solution Accelerators
- - Code Samples & Workshops
- - Architecture Patterns & Use Cases
-
- ### `applications.md`
- - RAG (Retrieval-Augmented Generation)
- - GraphRAG
- - RAG Application
- - Vector Database & Embedding
- - Top Agent Frameworks
- - Additional Agent Framework
- - Cache
- - Data & Analytics Agents
- - Data Processing & OCR
- - Desktop AI assistant
- - Memory
- - Model Gateway
- - Model Serving & Local Runtimes
- - Observability & LLMOps
- - SDKs, Integration & ML Libraries
- - Training & Fine-tuning
- - UI & No-Code Tool
- - A2A
- - Computer use
- - Model Context Protocol (MCP)
- - Coding
- - Deep Research
- - Domain-Specific Agents
- - Skill
- - Harness
-
- > **Tip:** Do not add hand-curated entries to generated index sections such as `Popular LLM Applications (GitHub Stars >= 1000)`; update the generator skill instead.
-
- ### `models_research.md`
- - Large Language Model Landscape
- - Large Language Model Comparison
- - Taxonomy of Natural Language Processing
- - LLM Evolution and Model Timelines
- - NLP Taxonomy and Research Fields
- - Large Language Model Collection
- - Architecture Comparisons
- - Foundation Model Providers
- - Domain-Specific and Specialized LLMs
- - Multimodal Models
- - Prompt Engineering and Visual Prompts
- - Prompt Engineering
- - Adversarial Prompting
- - Prompt Tuner and Optimizer
- - Prompt Guides and Prompt Libraries
- - Visual Prompting and Visual Grounding
- - Large Language Model Training and Optimization
- - Pre-training and Data Preparation
- - Architecture and Inference Patterns
- - Architecture Variants, Attention, and Sparse Computation
- - Context and Long-Context Limits
- - Tokenization and LLM Numbers
- - Capabilities and Evaluation
- - Reasoning
- - Post-training and Fine-Tuning
- - Model Merging and Continual Adaptation
- - Parameter-Efficient Fine-Tuning
- - LoRA: Low-Rank Adaptation
- - Alignment and Preference Optimization
- - Quantization Techniques
- - Pruning and Sparsification
- - Knowledge Distillation
- - Memory Optimization
- - AI Adoption, Impact, and Society
- - AGI, Society, and Long-Term Impact
- - Trust, Safety, and Security
- - Business Adoption and Use Cases
- - Model Roadmaps and Products
- - OpenAI Products
- - Anthropic AI Products
- - Google AI Products
- - Survey on Large Language Models
- - Additional Topics: A Survey of LLMs
- - LLM Research (Ranked by cite count >=150)
- - Learning Resources, Implementations, and Regional Materials
- - Build LLMs from Scratch
- - Japanese and Korean-Language Materials
- - General Learning and Supplementary Materials
-
- ### `best_practices.md`
- - The Problem with RAG
- - RAG Solution Design
- - Agent Research → `### **Agent Research**`
- - RAG Research → `### **RAG Research**`
- - Agent Design Patterns → `### **Agent Design Patterns**`
- - Tool Use
- - Tool Use: LLM to Master APIs
- - Proposals & Glossary
-
- ### `tools_extra.md`
- - LLM for Robotics
- - Awesome demo
- - Datasets for LLM Training
- - Evaluating Large Language Models
- - LLM Evalution Benchmarks
- - Evaluation Metrics
- - LLMOps: Large Language Model Operations
-
+ description: "Format new entries from temp.md into temp_entries.md, ready to insert into the section files. USE FOR: adding new resources to the knowledge base. DO NOT USE FOR: editing existing entries or restructuring sections."
---
- ## Code Tools Reference
-
- All tools are in `code/`. Run with `python code/<script>.py`.
+ `temp.md` is a raw checklist of URLs and notes. Produce `temp_entries.md`, a staging file grouped under `## <filename> - <Section Name>:` headings. Mark processed items in `temp.md` as `[x]`.
- | Script | Purpose |
- |--------|---------|
- | `fetch_github_description.py` | Fetch GitHub repo descriptions; appends after the link colon. Skips lines that already have a description. |
- | `update_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 review-only RAG/agent paper candidates; not part of normal entry insertion. |
- | `fetch_llm_papers.py` | Generate or refresh the separate LLM-landscape paper pool; use `fetch-llm-papers` rather than hand-editing its output. |
- | `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/`. |
+ ## Steps
- **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.
+ 1. **Classify** each item into a file (`azure.md`, `applications.md`, `models_research.md`, `best_practices.md`, `tools_extra.md`) and an existing section. Copy the heading text exactly from the target file (`Select-String '^#' section/<file>`). Do not invent sections or add entries to generated files (`x_llm_apps.md`, `x_llm_papers.md`, `x_popular_papers.md`).
+ 2. **Describe**: for GitHub repos run `code/fetch_github_description.py`; for arXiv, blogs, and product pages read the page and write one sentence (15 words or fewer, do not repeat the name).
+ 3. **Date**: for GitHub repos run `code/update_github_dates.py`; for arXiv derive from the ID prefix (`2602.x` = Feb 2026); for blogs use the page date.
+ 4. **Stars**: run `code/add_github_stars.py` on `github.com` links only.
- **Common CLI pattern:**
```powershell
python code/fetch_github_description.py --input temp.md --output temp_with_desc.md
python code/update_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)
-
- 1. **Wrong legend placement:** In `azure.md`, do not use emoji markers at all. In all other files, the symbol is appended to the link name inside `[Name]`. Never mix these two formats.
-
- 2. **Wrong section names:** Section labels in `temp_entries.md` must match the actual heading text in the target file exactly. Check the file before assigning. Do not invent new section names.
-
- 3. **Missing descriptions for non-GitHub links:** `fetch_github_description.py` only works for `github.com` URLs. For arXiv, blog, and product pages, you must fetch the page and write a description manually.
-
- 4. **Verbose descriptions:** Keep descriptions to ≤15 words. Do not repeat the name. No trailing "for use with", "that helps you", or similar filler.
-
- 5. **Date format mismatch:** `azure.md` uses `(Mon YYYY)` parentheses. All other section files use `[Mon YYYY]` square brackets.
+ ## Format
- 6. **Emoji preservation:** Ensure edits preserve existing legend symbols such as 📑, 📺, and 🤗.
+ | File | Line |
+ |------|------|
+ | `azure.md` | `- [Name](url) - Description. (Mon YYYY) ![stars](...)` (no emoji markers) |
+ | others | `1. [Name📑](url): Description. [Mon YYYY] ![stars](...)` (use `-` if the section uses dashes) |
- 7. **Star badges on non-GitHub links:** Only add star badges to `github.com` links. Blog posts, arXiv papers, and product pages must not have a star badge.
+ - Symbol goes inside the link text right after the name: 📑 paper, 📺 video, 🤗 Hugging Face, none for blogs/docs.
+ - Star badge goes last, and only on `github.com` links.
+ - Preserve existing symbols when editing.