Self-evolving multi-agent "newsroom" that turns your documents into a cross-linked knowledge wiki — writer ≠ reviewer, local-first, no API keys, a structured alternative to RAG.
Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + Browser agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add alfadur7/llm-wiki-newsroom
Maintenance
fresh
1d since push
Risk
Needs review
Permission surface may require sandboxing
GitHub quality
57
76/100 quality · 78/100 trust
Coverage tags
Review notes
Permission surface may require sandboxing · Quality score needs review
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewInstall readiness, security metadata, maintenance, and adoption risk.
Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
57 GitHub stars
Repo activity
57 stars, 10 forks
Maintenance
1d since push
License
MIT
Install
npx skills add alfadur7/llm-wiki-newsroom
Install safety
standard package or runtime install path
Permission surface
shell or command execution, filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add alfadur7/llm-wiki-newsroomDo not use when
Alternative
53.0K stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K stars
npx skills add assafelovic/gpt-researcher
Alternative
19.1K stars
npx skills add dzhng/deep-research
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill may drive a browser or interact with web pages.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
Install targets
Copy the registry command or an agent-specific install prompt for Codex, Claude Code, and Cursor.
Use the registry command when your workflow supports the OpenAgentSkill installer.
$ npx skills add alfadur7/llm-wiki-newsroomAgent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Resolve JSON
/api/agent/resolve?task=Use%20Llm%20Wiki%20Newsroom%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20Llm%20Wiki%20Newsroom%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/alfadur7-llm-wiki-newsroom/install
Agent should check
Copy prompt
Task: Use Llm Wiki Newsroom in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20Llm%20Wiki%20Newsroom%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/alfadur7-llm-wiki-newsroom/install
Install command: npx skills add alfadur7/llm-wiki-newsroom
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/alfadur7-llm-wiki-newsroom/install
LLM text format
/api/skills/alfadur7-llm-wiki-newsroom/install?format=text
Find alternatives
/api/skills/search?q=Llm%20Wiki%20Newsroom&limit=3
Agent prompt
Use Llm Wiki Newsroom for this task. Review https://www.openagentskill.com/api/skills/alfadur7-llm-wiki-newsroom/install, then install with: npx skills add alfadur7/llm-wiki-newsroomRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/alfadur7-llm-wiki-newsroom
LLM text
/api/registry/manifest/alfadur7-llm-wiki-newsroom?format=text
Install alias
/api/registry/install/alfadur7-llm-wiki-newsroom
Recommend
/api/registry/recommend?task=Use%20Llm%20Wiki%20Newsroom%20in%20an%20agent%20workflow&limit=3
Agent fit
RAG and knowledge
Use-case tags
Platforms
Python, Claude Code, Browser agents
Audit report
Review install readiness, maintenance, trust, quality, and metadata warnings before adding this skill to an agent workflow.
Agent decision cockpit
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
RAG and knowledge
Trust label
Strong shortlist
Install path
Command ready
Use when
Evidence
Review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
CHECK57 GitHub stars
Stars/forks activity
CHECK57 stars, 10 forks; issue activity unavailable in current metadata
Recent maintenance
PASS1d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Investigate faster
I need my agent to research a topic, compare sources, and produce a concise report.
Parse messy files
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Workflow fit
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Find, compare, and synthesize
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Alternative shortlist
Similar skills in this category, ranked with the same readiness and quality signals.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models. The goal of this repo is to provide the simplest implementation of a deep research agent - e.g. an agent that can refine its research direction overtime and deep dive into a topic.
# LLM Wiki Newsroom
[](LICENSE)
**A multi-agent AI knowledge base run by a five-role "newsroom" — open-source, local-first, no API keys, no vendor lock-in.** Drop articles, documents, and PDFs into the `raw/` folder, type a single command, and the newsroom — powered by an agent like Claude Code — reads them, extracts entities, concepts, and relationships, and organizes everything into a fully cross-referenced wiki, a structured and persistent alternative to RAG. Unlike most takes on the idea, the agent that *writes* a page is never the one that *reviews* it, and the authoring guidelines evolve themselves over time. Every new document you add also enriches the existing pages. This repo ships with a small example corpus — the debate over what "open source" means for AI — under `wiki/`, but the framework is domain-agnostic.
> Most knowledge tools leave the *finding* to you. This project **makes the AI read and understand** your collected documents first, then organizes them into a wiki — with cross-references between pages, automatic flagging of conflicting claims, and per-topic synthesis built in from the start, so later retrieval is fast.
> **See the output before installing** — the example corpus shipped in this repo is published as a browsable **[GitHub Wiki](https://github.com/alfadur7/llm-wiki-newsroom/wiki)** (no clone needed). It's a rendered static snapshot of the `wiki/` folder; the interactive graph below runs locally.
<sub>The interactive knowledge graph (`graph/graph.html`) — every page a node, every wikilink an edge, color-coded by auto-detected cluster, with a live physics layout and filter/search built in. Shown here on a larger private deployment (~2,300 nodes) to convey how it scales; **this repo ships a d
Frameworks & Tools
Decision snapshot
recent repository activity
Audit snapshot
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the audit before production use.
Growth loop
Scenario-led draft for Llm Wiki Newsroom, ready for a manual X post.
Most coding agents don't fail from lack of model power. They fail when repo context disappears. Llm Wiki Newsroom gives coding agents a repeatable way to plan, patch, review, or ship. 57 stars https://www.openagentskill.com/skills/alfadur7-llm-wiki-newsroom?ref=x #AIAgents
Listing + install path for Llm Wiki Newsroom: https://www.openagentskill.com/skills/alfadur7-llm-wiki-newsroom?ref=x Install: npx skills add alfadur7/llm-wiki-newsroom
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This community indexed listing is attributed to alfadur7 but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/alfadur7-llm-wiki-newsroom)
[](https://www.openagentskill.com/skills/alfadur7-llm-wiki-newsroom)
[](https://www.openagentskill.com/skills/alfadur7-llm-wiki-newsroom/audit)
[](https://www.openagentskill.com/skills/alfadur7-llm-wiki-newsroom)alfadur7
@alfadur7
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Sandbox only
Last30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
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