ldclabs

One-shot deep concept anatomy for a concept, term, idea, or phrase. Use when the user asks to dissect or deeply understand a concept through multiple lenses, especially with prompts like "解剖概念", "概念解剖", "learn concept", "explain this concept deeply", or "/learn". Produces concise org-mode output with anchors, eight analytical cuts, introspection, and compression into a formula, one-liner, and ASCII structure diagram. Do not use for multi-session teaching plans or current factual research unless the user explicitly asks for those.

Long-horizon research and implementation loop for Anda Bot. Use when a task should keep moving through registered tools such as goal, subagents_manager, cron scheduling tools, file tools, and file-backed state instead of ending after one response; especially for autonomous research, multi-hour audits, repeated experiments, stall recovery, or unattended follow-up.

Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.

Use this skill for ANY question requiring web research. Trigger on queries like "what is X", "explain X", "compare X and Y", "research X", or before content generation tasks. Provides systematic multi-angle research methodology instead of single superficial searches. Use this proactively when the user's question needs online information.

Guide users through a structured Anda Bot workflow for co-authoring documentation, proposals, PRDs, technical specs, RFCs, decision docs, and similar substantial writing tasks. Use this skill when the user wants help drafting, editing, organizing, or testing a document so it works for real readers.

Use this skill any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions "deck," "slides," "presentation," or references a .pptx filename, regardless of what they plan to do with the content afterward. If a .pptx file needs to be opened, created, or touched, use this skill.

Create, update, test, and package Anda Bot runtime skills. Use this skill when users want to create a new SKILL.md, improve an existing skill, convert a skill from another agent runtime to Anda Bot, write trigger evals, run skill behavior tests, optimize the frontmatter description, or package a reusable skill.

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

Long-term memory service for LLM agents. Provides persistent, structured memory (Cognitive Nexus) through three operations: Formation (encode conversations into memory), Recall (query memory with natural language), and Maintenance (consolidate and prune memory). Use this service when: - You need to persist facts, preferences, relationships, or events across sessions - You want to recall previous conversations, decisions, or user context - You need structured long-term memory without understanding KIP syntax - You want to trigger memory consolidation or cleanup Common trigger phrases: - "remember this", "save this for later", "don't forget" - "what did I say last time?", "recall my preferences" - "what do we know about X?", "who is X?" - "run memory maintenance", "consolidate memory"

Long-term memory service for LLM agents. Provides persistent, structured memory (Cognitive Nexus) through three operations: Formation (encode conversations into memory), Recall (query memory with natural language), and Maintenance (consolidate and prune memory). Use this service when: - You need to persist facts, preferences, relationships, or events across sessions - You want to recall previous conversations, decisions, or user context - You need structured long-term memory without understanding KIP syntax - You want to trigger memory consolidation or cleanup Common trigger phrases: - "remember this", "save this for later", "don't forget" - "what did I say last time?", "recall my preferences" - "what do we know about X?", "who is X?" - "run memory maintenance", "consolidate memory"

Persistent graph-based memory for AI agents via KIP (Knowledge Interaction Protocol). Provides retrieval-first memory operations (KQL), durable writes (KML), schema discovery (META), and memory hygiene patterns. Use whenever the agent needs to consult or update persistent memory, especially for: remembering user preferences/identity/relationships, storing conversation events, answering questions that depend on past sessions, and any task involving `execute_kip`.

Use this skill whenever you need to help users work with AndaDB in Rust programs or update AndaDB repository documentation. This includes creating databases and collections, deriving schemas, CRUD operations, B-Tree filters, BM25 full-text search, HNSW vector search, hybrid retrieval, object-store persistence, encrypted storage, KIP/Cognitive Nexus integration, and avoiding outdated ciborium-era examples. Invoke it when users mention AndaDB, AI agent memory, embedding storage, hybrid search, KIP, or Cognitive Nexus in this repository.

Use when: you need to run Python or TypeScript logic in LangShell, keep state across turns, orchestrate registered external functions, process structured data, call approved file/HTTP/database helpers, or perform multi-step agent computation inside a sandbox. Do not use for arbitrary OS shell commands, unsupported packages, direct host file/network access, or bypassing registered capabilities.