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Nützliche Agent Skills, nach echter Nutzung sortiert.
Vergleiche verifizierte Skills aus öffentlichen Repositories nach Aufgabe, Paketinhalt, Installationen und nachvollziehbarer Quelle.
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- 10.772
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- 1711
- zuletzt synchronisiert
- 23.09.2026
moonlight-lupindecision-log
ADR-style decision journal for agents and teams. Create numbered decision records, track superseding chains, schedule periodic reviews, and search past decisions to avoid re-litigating settled questions.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupindeep-research
Autonomous multi-step deep research engine implementing an iterative Think → Search → Extract → Synthesize → Stop loop. The LLM drives every decision: what to search, what's relevant, what's missing, and when to stop. Produces a cited, magazine-quality report with inline citations, category- specific formatting, and research stats. Trigger when the user asks for "deep research", "research report on", "comprehensive analysis of", "look into X in depth", "write a report on X", or any question needing multi-source synthesis beyond a single search. For entity vetting/dossiers use entity-research; for news digests use news-monitoring; for source-grounded Q&A use notebooklm-mode.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupindisk-cleanup
Use when df says disk is above 80% full or the user wants to reclaim storage space on a VM.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupindocument-converter
Convert between document formats: Markdown↔HTML, CSV↔JSON, YAML↔TOML, JSON↔YAML, CSV↔Markdown table, HTML→plain text, JSON→CSV (flattening nested objects), Excel→CSV, Markdown→PDF. Requires pandoc for PDF and openpyxl for Excel.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinendpoint-probe
Use when you need to discover an API's surface — poke a base URL to find its type (REST, GraphQL, SOAP, JSON-RPC), auth scheme, available endpoints, rate limits, and CORS policy. Also probes MCP servers (HTTP and stdio) to discover tools, resources, and prompts with full input schemas. Runs a Python probe script that checks well-known discovery paths (OpenAPI/Swagger, GraphQL introspection, health, .well-known), fingerprints the server, sends OPTIONS for CORS, detects auth from 401/403 responses, and guesses common REST resources. For MCP, performs JSON-RPC initialize handshake then tools/list, resources/list, prompts/list. Feed it a base URL or MCP server URL, optionally with auth credentials.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinentity-research
Deep background research on an entity — a company OR a person — into a cited dossier: identity & background, ownership & key management, adverse / negative media, public sanctions-list name-match signals, PEP indications from public research, and litigation / regulatory history. Use when the user says "research this company / person", "background check on X", "any negative press / adverse media on X", "who owns / who runs X", "is X sanctioned / sanctions check on X", "vet this vendor / counterparty / candidate / partner", or "entity search". It RESEARCHES and COMPILES with sources; it is NOT a compliance/AML/CDD determination and NOT a sanctions or PEP clearance. A sanctions-list or PEP signal is a SIGNAL to escalate to a human compliance function, never a "clear" or "block". Research a person only for a legitimate purpose and only from public information.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinfact-checker
Targeted claim verification pipeline. Given a factual assertion, search multiple independent sources, cross-check for agreement or contradiction, rate confidence (verified / likely true / disputed / unverified / outdated), and produce a cited verification report.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinfile-organizer
Use when the user wants to organize, tidy, or restructure a messy directory (Downloads, Desktop, documents folder). LLM-powered file organizer that scans content, proposes a structure, and executes moves in chunks with a mandatory user confirmation gate.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinfill-template
Bulk-fill ONE master template — a Word (.docx) letter/form or an Excel (.xlsx) form — from a data table, producing one filled file per row (mail-merge). Use whenever the user wants to "fill in this letter for each person", "mail merge", "generate letters for this list", "run this template over a spreadsheet", or hands over a template plus a list. The skill reads the master, proposes a TOKENISED version (varying parts become {{tokens}}), confirms the template and the token-to-column mapping with the user, then regenerates one output per data row — preserving the master's layout and styling exactly. Data is an .xlsx or .csv (one row per output); outputs are one named file per record. Never invents: a token with no data is written as a VISIBLE flag, never a silent blank. Runs fully local and generates files only — it does not send, post or sign. Not for extracting data OUT of documents, and not a substitute for a hand-crafted single letter.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinhermes-onboarding
Use when onboarding a new customer — configure gateway, dashboard, memory, services for production.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinimage-studio
Generate, edit and upscale AI images via fal.ai through a three-stage studio workflow — brainstorm a strong prompt with the user, prototype cheaply and iterate on feedback, then produce a finalised image. Use when the user wants image generation or editing through the local fal.ai helper workflow, including requests to "generate an image", "make an image/picture/illustration/graphic of…", "create an AI image", "edit/change this image", "make a variation", "upscale this", create a "production-ready image", "clean up / enhance a photo", "make this phone shot look professional", create imagery "for the deck/post/website/newsletter", or when they explicitly mention fal.ai or nano-banana. Do not use for Canva template designs, branded PowerPoint decks, data charts/dashboards, or flowcharts/diagrams — those are layout, data, or structure tasks, not generative imagery. Video generation is out of scope. Do not override platform-native image generation tools where the host environment requires them.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupininput-token-analysis
Use when input token spend needs explaining and fixing.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupininput-token-overheads
Use when context window is filling up too fast or input token cost is too high. Audits overhead sources.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinlibrary-rag
Semantic search over a personal library using Nemotron-3-Embed-1B embeddings + sqlite-vec. Index books, documents, any text corpus; query by meaning. Includes EPUB→Markdown conversion and MCP server for auto-available search tools.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinlog-analyzer
Parse agent log files to identify error patterns, rate limit hits, timeout clusters, tool failures, and component-level error counts. Produces a structured anomaly report. Cron-compatible — silent if no issues, alert digest if anomalies found. Also computes per-tool failure rates from a Hermes profile state.db (scripts/statefailures.py).
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinmedia-analyzer
Technique-focused media analysis. Detects rhetorical tools (loaded language, cherry-picking, source selection bias, framing, omission, emotional appeals, false balance) in articles and produces a structured analysis report. Identifies specific techniques and bias-signal intensity without labeling political positions.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinmodel-compare
Blind side-by-side multi-model comparison. Send one prompt to 2-4 models simultaneously, present responses anonymously (Model A / B / C / D), let the user pick a winner, then reveal identities and show which model won. Supports custom evaluation criteria, synthesis of responses, and vote history logging. Trigger when the user says "compare models", "test these models", "which model is better for", "A/B test", "blind comparison", "model evaluation", or wants to see how different AI models handle the same prompt. Can also be used for prompt engineering — testing how different models interpret the same instructions. Supports reasoning-effort A/B via provider:@effort:modelid specs.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinnews-monitoring
Recurring topic/news monitoring with web search, multi-language sources, digest formatting, and automated delivery via Hermes cron jobs. Covers search strategy, source selection, Chinese-language platforms, and digest templates.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinnotebooklm-mode
Source-grounded research pipeline inspired by NotebookLM. Collect verbatim source extracts into a temporary workspace, then answer questions and create deliverables grounded in those sources. Two grounding modes: strict (vault-only) and augmented (vault + labeled [background] knowledge). Trigger when the user asks to research a topic from sources, wants NotebookLM-style grounded answers, asks to build a resource vault, or says "notebooklm mode".
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinpeople-enrichment
Enrich and search People Data Labs (PDL) Person and Company data, writing results to .xlsx. Five operations: enrich named PEOPLE into a profile, employment history and LinkedIn URL; IDENTIFY an ambiguous person as several scored candidates; SEARCH people by criteria (company, title, location); enrich COMPANIES into firmographics (industry, size, employees, HQ, founded, LinkedIn); and SEARCH companies by criteria. Use when the user has a list of names or companies and wants their job, employer, work history, LinkedIn URLs or company profiles looked up, or wants to find people or companies matching criteria. Trigger on phrases like "enrich these contacts", "find their LinkedIn", "look up these people", "who works at X", "find directors at Y", "enrich these companies", or "get firmographics for". Do not scrape LinkedIn directly — this uses a licensed data aggregator (PDL) instead, for legal and reliability reasons.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinpexels-stock-photos
Search and download free real-world stock photos from the Pexels API. Use when the user wants a REAL photo — "find a photo of X", "search for pictures of Y", "stock image of Z", "I need a photo for this slide/article/presentation". Searches the Pexels library (millions of photos) and downloads images in the chosen size and orientation. Do NOT use for AI-generated art ("generate an image of", "create a picture of", "make me an illustration") — those go to the host's image generation tool or an AI image skill. Key distinction: "photo" = real stock photography = Pexels; "generate/create/make an image" = AI art = image generation tools. Also do not use for editing existing images, screenshots, diagrams, or data charts.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinscheduled-summary
Cron-driven cross-session digest. Aggregates session activity, cron job outputs, memory changes, and tool usage stats into a compact summary for delivery via messaging platforms. Surfaces outstanding tasks and cross-session context that's invisible on chat platforms.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinskill-maintainer
Track upstream drift and sync adapted skill libraries
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.
moonlight-lupinskill-retrieval
- BM25-based skill retrieval plugin for Hermes Agent. Replaces the full skill list in the system prompt with a names-only compact view (~2K tokens) and injects top-K relevant skill descriptions per turn via BM25 retrieval (~300 tokens). Saves ~9K tokens/turn. Use when system prompt token overhead from skills is a concern, or when skill discovery quality matters.
- Installationen
- 1
- GitHub Stars
- 62
- Aktualisiert
- 7. Sept.