Agent skill

update-app-count

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.

View SKILL.md on GitHub Repository
Stars 398
Forks 50

Install this agent skill to your Project

npx add-skill https://github.com/kimtth/awesome-azure-openai-llm/tree/main/.agent/skills/update-app-count

SKILL.md

Overview

The pool file section/x_llm_apps.md is a generated ranked list of GitHub repositories related to LLM apps, agents, chat UIs, workflow builders, and similar application-layer projects.

It is generated by code/get_app_list_by_github_star.py using GitHub topic search, deduplicated across multiple topics, and sorted by GitHub star count descending.

The section ###### Popular LLM Applications (Ranked by github star count ≥1000) in section/applications.md links to this file with a one-line description only. Do not paste the generated entries directly into applications.md.

Script Reference

Script: code/get_app_list_by_github_star.py
Python env: .venv\Scripts\python.exe

Key CLI Arguments

Argument Default Purpose
--output section/x_llm_apps.md Output file path. Extension controls format: .md, .json, .csv
--min-stars 1000 Minimum GitHub star threshold
--topics curated list GitHub topics to query and merge
--token GITHUB_TOKEN env var GitHub PAT for higher rate limits
--show 30 Number of repos printed to console
--timeout 20 Per-request timeout in seconds
--max-retries 4 Max retries per request
--backoff 1.0 Initial retry backoff
--sleep 1.0 Delay between successful page requests
--include-archived off Include archived repositories

Workflow

1. Full refresh of the markdown pool

Use this for the normal update path.

powershell
.venv\Scripts\python.exe code/get_app_list_by_github_star.py
  • Rewrites section/x_llm_apps.md.
  • Uses --min-stars 1000 by default to match the section title.
  • Excludes archived repos unless --include-archived is passed.
  • Writes compact numbered entries instead of badge-heavy markdown.

2. Authenticated refresh to avoid GitHub API limits

powershell
.venv\Scripts\python.exe code/get_app_list_by_github_star.py --token %GITHUB_TOKEN%

Use a GitHub PAT when doing a full refresh across many topics. Unauthenticated search is heavily rate-limited.

3. Generate a stricter ranking

powershell
.venv\Scripts\python.exe code/get_app_list_by_github_star.py --min-stars 2000

Use this when you want a tighter list. If you change the threshold materially, update the descriptive text in section/applications.md so the label stays truthful.

4. Export raw data for review

powershell
.venv\Scripts\python.exe code/get_app_list_by_github_star.py --output files/x_llm_apps.json
.venv\Scripts\python.exe code/get_app_list_by_github_star.py --output files/x_llm_apps.csv

Use JSON or CSV when you want to inspect or post-process the ranked repo pool before regenerating markdown.

5. Tune the topic set

powershell
.venv\Scripts\python.exe code/get_app_list_by_github_star.py --topics llm agent rag chatbot ai-workflow
  • Topics are GitHub repository topics, not free-text queries.
  • Results are deduplicated by full_name after all topic passes complete.
  • If the topic set changes substantially, regenerate the markdown pool rather than editing it by hand.

6. Topic-specific doc update

If the output is intentionally narrowed to a subset such as gemini claude azure-openai copilot assistant, keep the ranked entries as generated, then update the document metadata and the linking description in section/applications.md to reflect the narrowed scope.

Output Format

Each entry in section/x_llm_apps.md follows this compact format:

markdown
1. [owner/repo](https://github.com/owner/repo): Short GitHub description. [Mon YYYY] (GitHub stars: 12,345)
  • Entries are sorted by GitHub stars descending.
  • The date is the repository creation month, formatted as [Mon YYYY].
  • The star count is plain text in parentheses.
  • Do not append realtime shields or badges in this generated file.

The file header includes:

  • generated timestamp
  • GitHub Search API source note
  • searched topic list
  • total repository count

Common Pitfalls

  1. Using the wrong output target: The generated ranking belongs in section/x_llm_apps.md, not inline inside section/applications.md.

  2. Adding GitHub star badges: This file intentionally uses static text like (GitHub stars: 12,345). Do not run add_github_stars.py on it.

  3. Forgetting the star threshold contract: The linked section title says ≥1000. If you generate with a different threshold, either restore 1000 or update the section label and description.

  4. Unauthenticated rate limits: Full runs over many topics can stall or fail without a token. Prefer GITHUB_TOKEN for routine refreshes.

  5. Assuming GitHub topics are comprehensive: Some strong repos do not declare useful topics and may be missed. Expand --topics or curate separately if coverage is insufficient.

  6. Archived repos polluting the ranking: Archived repos are excluded by default. Only include them deliberately.

  7. Hand-editing generated entries: Manual changes will be lost on the next run. Adjust the script inputs or post-process separately instead.

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

kimtth/awesome-azure-openai-llm

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.

398 50
Explore
kimtth/awesome-azure-openai-llm

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.

398 50
Explore
kimtth/awesome-azure-openai-llm

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).

398 50
Explore
kimtth/awesome-azure-openai-llm

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.

398 50
Explore
davila7/claude-code-templates

verl-rl-training

Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.

23,776 2,298
Explore
davila7/claude-code-templates

openrlhf-training

High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.

23,776 2,298
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results