Discover and call configured MCP server tools through the mcporter command line. Inspect the inputs each tool accepts, invoke external services, and create SKILL.md reference guides with usage examples for newly added servers.
Manage communication channels by identifying senders, maintaining trusted contacts, and setting the administrator's primary notification destination. It also covers first contact on a new channel and contacting the administrator across channels, with rules for handling unknown senders.
Manage GitHub pull requests, which propose code changes, and issues through a central MCP server. Create and update records, add comments, request reviewers, check changed files and automated checks, and merge pull requests.
Retrieve shared reference files and send work in progress using local shared paths. The skill also guides missing-file checks and distinguishes manual file transfers from synchronization already handled by task acceptance and submission.
Plan work across several AI agents by defining task owners, deliverables, dependencies, and acceptance criteria. Choose a finite workflow or repeated improvement cycles, then review results before allowing dependent tasks to proceed.
Coordinate shared files using local team and global input paths. The skill explains project refreshes, task file ownership, and missing-file troubleshooting, while identifying task directory transfers already handled by delegation and result checking.
Keep Hermes Worker configuration and shared task files synchronized with MinIO, a central storage service. The skill covers immediate downloads, manual result uploads, and which configuration changes take effect on the next message or require a restart.
Keep decisions, preferences, and project context in daily notes and curated memory files. Search those records before answering questions about past work, so useful information remains available across separate conversations.
Send notifications and documents through DingTalk using enterprise bots or Webhook bots, which receive messages through a dedicated URL. Manage multiple bots and choose text, Markdown, links, or cards for individual and group conversations, depending on bot type.
Retrieve past facts, preferences, and decisions from ReMe with references to saved files. Check service health, pending memory writes, and consolidation schedules, or organize existing notes into long-term knowledge when requested.
Turn financial research requests into Tushare data retrieval, cleaning, comparisons, and concise summaries. Study Chinese stocks, funds, financial statements, market flows, and economic indicators, or export structured files for further analysis.
Search the public web through Serper for current information and sources unavailable in local files or project APIs. Refine queries, check important claims against linked pages, and summarize findings with source titles and links.
Set up and operate ReMe through its command-line interface to retain context across conversations. Search saved history, record facts and decisions, ingest documents, and consolidate notes into long-term memory stored in a local workspace.
Retrieve past conversations, preferences, project history, and decisions stored in ReMe from Claude Code. Read relevant records with source paths, save explicitly requested facts, and investigate plugin or memory service connection problems.
Generate text, images, video, speech, and music via the MiniMax AI platform. Covers text generation (MiniMax-M3 model), image generation (image-01), video generation (Hailuo-2.3), speech synthesis (speech-2.8-hd, 300+ voices), music generation (music-2.6 with lyrics, cover, and instrumental), and web search. Use when the user needs to create AI-generated multimedia content, produce narrated audio from text, compose music, or search the web through MiniMax AI services.
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Discover and recommend **combinations** of agent skills to complete complex, multi-faceted tasks. Provides two recommendation strategies — **Maximum Quality** (best skill per subtask) and **Minimum Dependencies** (fewest installs). Use this skill whenever the user wants to find skills, asks "how do I do X", "find a skill for X", or describes a task that likely requires multiple capabilities working together. Also use when the user mentions composing workflows, building pipelines, or needs help across several domains at once — even if they only say "find me a skill". This skill supersedes simple single-skill search by decomposing the task into subtasks and assembling an optimal skill portfolio.
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Use when the user wants help with academic papers or citations but it's unclear which specific workflow fits — reviewing a paper, checking a BibTeX file for fake references, or benchmarking multiple LLMs on reference-recommendation accuracy. Also use when the user mentions paper review, peer review, BibTeX verification, citation checking, reference hallucination, or academic literature accuracy and hasn't specified which of those three tasks they mean. This skill is the entry router for the academic-eval suite: it asks one diagnostic question then routes to the right sub-skill.
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Review academic papers for correctness, quality, and novelty using OpenJudge's multi-stage pipeline. Supports PDF files and LaTeX source packages (.tar.gz/.zip). Covers 10 disciplines: cs, medicine, physics, chemistry, biology, economics, psychology, environmental_science, mathematics, social_sciences. Use when the user asks to review, evaluate, critique, or assess a research paper, check references, or verify a BibTeX file.
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Verify a BibTeX file for hallucinated or fabricated references by cross-checking every entry against CrossRef, arXiv, and DBLP. Reports each reference as verified, suspect, or not found, with field-level mismatch details (title, authors, year, DOI). Use when the user wants to check a .bib file for fake citations, validate references in a paper, or audit bibliography entries for accuracy.
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Use when the user wants to compare or benchmark multiple LLMs/agents arena-style but it's unclear which specific workflow fits — a general-purpose win-rate comparison on a custom task, or a benchmark specifically about reference/citation hallucination rate. Also use when the user mentions model arena, agent arena, pairwise model comparison, win-rate ranking, or comparing models on a task and hasn't specified whether that task is generic or about citation accuracy. This skill is the entry router for the arena-eval suite: it asks one diagnostic question when needed, then recommends the workflow or workflows needed to cover the request.
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Automatically evaluate and compare multiple AI models or agents without pre-existing test data. Generates test queries from a task description, collects responses from all target endpoints, auto-generates evaluation rubrics, runs pairwise comparisons via a judge model, and produces win-rate rankings with reports and charts. Supports checkpoint resume, incremental endpoint addition, and judge model hot-swap. Use when the user asks to compare, benchmark, or rank multiple models or agents on a custom task, or run an arena-style evaluation.
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Use when the user has a RAG (Retrieval-Augmented Generation) system and wants to evaluate its quality — separating retrieval issues from generation issues. Also use when the user mentions RAG evaluation, faithfulness checking, hallucination detection in RAG, retrieval quality, chunking optimization, or "is my RAG pipeline working." Outputs a diagnostic matrix that pinpoints whether problems are in retrieval or generation.
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Use when the user needs to design evaluation datasets, create test cases, stratify samples, generate adversarial examples, extract eval dimensions from traces/specs, or build a labeled evaluation set. Also use when the user mentions test data design, eval coverage, difficulty stratification, synthetic data generation for eval, or "how to create good evaluation data." Outputs datasets in OpenJudge-compatible format.
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Use when the user has run multiple evaluation skills and wants a comprehensive analysis — maturity assessment, cross-skill signals, trends, prioritized actions, and an executive summary. Also use when the user mentions eval health check, evaluation audit, ship readiness, evaluation maturity, or "how good is my evaluation system itself." This is a read-only analysis skill.
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