Manage a project’s execution plan and lifecycle across multiple agents. Track task dependencies or repeated work cycles, identify tasks ready to start, handle pauses and completion, and prepare project status reports and final results.
Turn ready project tasks into clear instructions for AI workers, with defined ownership, deliverables, acceptance criteria, and reporting rules. Handle assignment notifications and check submitted results before passing them to the project management workflow for acceptance.
Record completed steps, decisions, issues, and next actions in dated progress logs. Maintain recent task history and sync updates to shared storage so an agent can recover context and resume work after a session reset.
Handle standalone administration tasks on a Tuwunel Matrix server, including registering users, creating rooms, managing membership, and uploading files. It also covers sending files to an administrator and identifies mention and room-joining requirements that affect Workers.
Find and install agent skills for specific tasks, from testing and development to design and documentation. Translate needs into search terms, present relevant options with installation commands, and check or update installed skills.
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.
Manage consumers, credentials, AI providers, and model routing in Higress AI Gateway through its Console API. The skill explains session authentication, access permissions, and how to update existing settings while preserving required version information.
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.
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.
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.
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.
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.
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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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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Detect whether an API endpoint is backed by genuine Claude (not a wrapper, proxy, or impersonator) using 9 weighted rule-based checks that mirror the claude-verify project. Also extracts injected system prompts from providers that override Claude's identity. Fully self-contained — copy the code below and run, no extra packages beyond httpx. Use when the user wants to verify a Claude API key or endpoint, check if a third-party Claude service is authentic, audit API providers for Claude authenticity, test multiple models in parallel, or discover what system prompt a provider has injected.
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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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Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation 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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Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.
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Use when the user wants to build an evaluation system for an LLM/agent application but doesn't know where to start — they have traces, prompts, RAG pipelines, or nothing at all. Also use when the user mentions evaluation, eval, benchmarking, testing LLM quality, measuring agent performance, assessing RAG accuracy, or wants to compare prompts/models. This skill is the entry router: it asks diagnostic questions then recommends which sub-skill (local workflow) to use next.
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Use when the user has evaluation principles or a dataset but needs help choosing the right graders, designing evaluation metrics, creating LLM-as-judge prompts, combining multiple metrics into a composite score, or building an automated evaluation pipeline. Also use when the user mentions grader selection, metric design, judge prompt engineering, rubric design, evaluation pipeline code, or "how to evaluate [X] automatically." Outputs executable OpenJudge pipeline code.
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