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moonlight-lupin

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• 37 skills
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• 11 hours ago last updated

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   Data Tidy · moonlight-lupin bundle

   Clean up messy data into a structured, validated table — from any source (a junk-filled .xlsx/CSV, a pasted email/markdown table, a Word table, or an Outlook .msg) to a clean .xlsx plus an audit/change report. Use when the user wants to "clean up this data", "tidy this spreadsheet", "normalise this list", "structure this messy export", "dedupe this", "standardise these dates / currencies", or "turn this into a clean table". (For getting data OUT of PDFs — tables, forms, scanned documents — use data-extract, not this skill.) NOT deal-document intelligence (lease abstraction, model review, comps) — that's out of scope.
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   Data Analyse · moonlight-lupin bundle

   Analyse a dataset and deliver the insights and key metrics that matter for it — an insight brief with headline findings, trends, breakdowns, concentration, outliers and ageing, tailored to the type of data (transactions, receivables, pipeline, survey, task list, any table). Use when the user says "analyse this data", "what are the key metrics", "any insights from this spreadsheet", "summarise this export", "what's driving the numbers", "who are the top customers", or hands over a table and asks what it says. NOT data cleaning (data-tidy), NOT matching two datasets (data-reconcile), NOT a dashboard (data-visualise — natural next step); descriptive analysis only, never financial or investment advice.
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   Data Convert · moonlight-lupin bundle

   Re-express structured data to hit a DIFFERENT target — map a source (system A's export) onto another system's import CONTRACT, and/or RESHAPE its structure (wide↔long, nested JSON↔flat table, split one file into many, union many into one). Use when the user wants to "convert this to X's format", "map these columns to the import template", "get this into the format System B needs", "reshape / pivot / unpivot this", "flatten this JSON", "split this file by column", "combine these files", or "prepare this for upload/import". Writes a reusable conversion card (Markdown + embedded JSON spec) a future agent re-runs after sense-checking the source. NOT data cleaning (data-tidy), NOT matching two record sets (data-reconcile), NOT raw file-format-only conversion.
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   Data Extract · moonlight-lupin bundle

   Get STRUCTURED data OUT of documents — PDFs (incl. multi-table/scanned via local OCR), Word, PowerPoint (.pptx), and Outlook .msg — into a clean .xlsx plus an audit report. Use when the user wants to "extract data from this PDF/document", "pull the table out of this report", "get the figures from these statements/certificates", "turn these confirmations into a table", "read the fields off this form", or "extract these line items". Two modes: key-value/FORM extraction (label → value, one record per document) and TABLE extraction (list a document's tables, pick one, pull it). NOT for already-tabular data (use data-tidy) or deal-document intelligence like lease abstraction/model review (out of scope).
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   Data Reconcile · moonlight-lupin bundle

   Reconcile any two record sets (A vs B) and triage the discrepancies — match line-by-line on a shared key, or heuristically on amount + date, then classify every unreconciled item (category, materiality, probable cause, suggested action) into a reconciliation working paper (.xlsx). Use whenever the user wants to "reconcile A to B", "reconcile the bank to the ledger", "reconcile the invoice tracker to the accounts", "tie out", "match these two lists/exports", "find the differences between", "what doesn't match", "reconciliation working paper", or "discrepancy triage". Presets for common recurring reconciliations (invoice tracker vs accounting records, bank vs cashbook, fund administrator vs internal records, payments/PRF vs bank); generic for anything else. NOT budget/variance analysis, and NOT deal/asset analysis (that is out of scope).
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   Data Visualise · moonlight-lupin bundle

   Orchestrate visual output from tabular data or a data-analyse analysis.json into either (1) a brandable self-contained HTML dashboard (print/PDF / artifact) or (2) an Excel workbook of native charts for analysts. Use when the user says "build a dashboard", "visualise this", "make a chart / KPI cards / scorecard", "Excel charts", "chart this in a spreadsheet", "a one-pager of these numbers", "RAG status board", or wants a shareable visual summary. HTML path: inline SVG, no CDN. Excel path: openpyxl charts with OfficeCLI-aligned chartType names (column/bar/line/pie/doughnut/waterfall). NOT PowerPoint or letters; clean/extract first via data-tidy / data-extract; compute metrics via data-analyse when numbers must be exact.
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   Disk Cleanup · moonlight-lupin bundle

   Use when df says disk is above 80% full or the user wants to reclaim storage space on a VM.
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   Model Compare · moonlight-lupin bundle

   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:model_id specs.
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   Library RAG · moonlight-lupin bundle

   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.
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    Clips Studio · moonlight-lupin bundle

    Use when the user wants to "make a video/clip", "generate a video from text", "animate this image/photo", "create a marketing reel / social video / teaser", "do a 3D / parallax move", "pan/zoom/orbit a shot", or mentions fal.ai, Kling, Veo or Seedance for video. Staged fal.ai workflow — brainstorm, draft cheaply, produce the final at quality. Three modes: text-to-video, animate (still to motion), camera-move (push-in/pan/orbit). Not for still images (image-studio), slide decks, or charts. Honesty discipline: never depict real identifiable subjects via text-to-video.
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    Image Studio · moonlight-lupin bundle

    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.
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    Fact Checker · moonlight-lupin bundle

    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.
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    Log Analyzer · moonlight-lupin bundle

    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/state_failures.py).
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    Deep Research · moonlight-lupin bundle

    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.
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    Skill Retrieval · moonlight-lupin bundle

    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.
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    Task Brief · moonlight-lupin bundle

    Compile a task brief BEFORE starting substantial work — pin down the goal (what "done" looks like), the context (everything known that bears on the task), the constraints (guardrails, with the organisation's standing rules included automatically) and the tooling (which installed skill or tool will do the work) — confirm it with the user, then execute against it. Use whenever the user says "brief this task", "brief this first", "make sure you understand before you start", "scope this properly", "compile a brief", "what do you need from me", "improve my prompt", "help me get better output", or hands over a SUBSTANTIAL task — multi-step, multi-document, or producing a deliverable someone else will read — as a thin one-line request. Asks at most 2-3 clarifying questions, and only where the answer would change the output; otherwise it states its assumptions in the brief and proceeds. Not for quick one-step asks — just answer those directly.
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    Endpoint Probe · moonlight-lupin bundle

    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.
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    Media Analyzer · moonlight-lupin bundle

    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.
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    Source Tracker · moonlight-lupin bundle

    Persistent citation database for multi-session research. Add URLs as they're cited, dedup variants, tag by topic, check link health, and export bibliographies in Markdown/BibTeX/CSV/JSON.
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    Entity Research · moonlight-lupin bundle

    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.
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    News Monitoring · moonlight-lupin bundle

    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.
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    Notebooklm Mode · moonlight-lupin bundle

    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".
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    Decision Log · moonlight-lupin bundle

    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.
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    Skill Maintainer · moonlight-lupin bundle

    Track upstream drift and sync adapted skill libraries
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    Fill Template · moonlight-lupin bundle

    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.
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    People Enrichment · moonlight-lupin bundle

    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.
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    Hermes Onboarding · moonlight-lupin bundle

    Use when onboarding a new customer — configure gateway, dashboard, memory, services for production.
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    File Organizer · moonlight-lupin bundle

    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.
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    Pexels Stock Photos · moonlight-lupin bundle

    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.
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    Travel Itinerary · moonlight-lupin bundle

    Create, update, sanitize, route, and export structured business-trip itineraries from booking confirmations, emails, tickets, PDFs, screenshots, calendar invites, or notes. Use for day-by-day itinerary generation, privacy-safe sharing, route links, and Markdown/PDF/DOCX/Google Doc/ICS exports.
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    Website Scraping · moonlight-lupin bundle

    Generic playbook for extracting structured data from any website — hotel prices, flight fares, e-commerce listings, real estate, jobs, competitor product catalogues, anything where the goal is to turn one or more URLs into clean records on disk. Use this skill whenever the user mentions scraping, harvesting, extracting, or pulling data from a website; names a specific site or competitor they want data from; asks how to handle JavaScript-rendered pages, Cloudflare blocks, bot detection, Turnstile challenges, or Playwright; wants to monitor prices over time; needs to automate a copy-paste research task; or says things like "just get the data from X" or "I need a script that grabs N from site Y". Covers recon, picking the lightest extraction tool that works, surviving anti-bot defences, and writing clean JSONL output with a run manifest. Scope is scraping only — landing data in a database, scheduling, and downstream pipeline work are explicitly out of scope and live in other tools.
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    Input Token Analysis · moonlight-lupin bundle

    Use when input token spend needs explaining and fixing.
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    Scheduled Summary · moonlight-lupin bundle

    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.
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    Input Token Overheads · moonlight-lupin bundle

    Use when context window is filling up too fast or input token cost is too high. Audits overhead sources.
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    Document Converter · moonlight-lupin bundle

    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.
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    Youtube Topic Research · moonlight-lupin bundle

    Find and summarize YouTube videos for topics where visual explanation, demos, tutorials, talks, walkthroughs, or screen recordings are useful. Can run standalone, or export transcript-backed video source notes into notebooklm-mode vaults for grounded research.
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    Claude Plugin Converter · moonlight-lupin bundle

    Convert Claude Code plugins into self-contained Hermes plugins — discovery analysis then full conversion
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