
This skill provides a way to retrieve information from the AgentScope library for analysis and decision-making.
A skill that can retrieve A2UI UI JSON schematics and UI templates that best show the response. This skill is essential and must be used before generating A2UI (Agent to UI) JSON responses.
Expert Java developer skill for AgentScope Java framework - a reactive, message-driven multi-agent system built on Project Reactor. Use when working with reactive programming, LLM integration, agent orchestration, multi-agent systems, or when the user mentions AgentScope, ReActAgent, Mono/Flux, Project Reactor, or Java agent development. Specializes in non-blocking code, tool integration, hooks, pipelines, and production-ready agent applications.
四层技能合成、技能市场、自学习闭环
Visualise the result of an analysis as a chart (line, bar, area, scatter, etc.). Use when the user asks to "plot...", "chart...", "show me the trend of...", "visualise...", or when a numerical result has more than ~10 rows and would be easier to read as a picture. Produces an image file plus the script that generated it.
Search a codebase efficiently with ripgrep regular expressions, file globs, and git history search. Use to locate symbols, usages, and definitions instead of reading whole files.
Use git as a safety net - create a checkpoint commit before risky or large changes and roll back cleanly if a change makes things worse. Use before multi-file refactors.
Verify code changes by running the project's typecheck, build, lint, and targeted tests, then fix and re-run until clean. Use after editing any source file.
Apply multi-file or tricky edits atomically with git apply instead of many fragile edit_file calls. Use when changing several files at once or when edit_file fails to match.
Answer a quantitative business question by writing a SQL query against the data warehouse, validating it, and presenting the result. Use when the user asks "how many...", "what's the trend of...", "compare X vs Y over...", "what's our top N...", or anything that resolves to a query against tabular data. Produces a small result table plus the underlying query.
# 技能(Skill) 一个 skill 就是一份写好的能力包:一个目录里放一份 `SKILL.md`(说明用途、给 agent 看的指令),可以再带一些参考文档、脚本或样例。写好后丢给 agent,它会在合适的时候自己用。 harness 让你从两个地方装 skill: - **接 skill 市场**:Git 仓库、Nacos、MySQL、classpath、或者自己写的后端 - **放在工作区**:项目里 `workspace/skills/` 下的就所有人共用;放在 `<userId>/skills/` 下的只有那个用户看得到 两类来源同时生效,不需要二选一。 > 关于 skill 自身的结构、`SKILL.md` 写法、资源加载、tool 绑定、代码执行这些通用概念,见 [Agent Skill](../task/agent-skill.md)。本文只讲 harness 这一层的用法。 --- ## 一个例子 把团队的 skill 仓库接进来,agent 立刻就能用: ```java HarnessAgent agent = HarnessAgent.bui
Four-layer skill composition, skill marketplaces, the self-learning loop
First-response playbook for an inbound customer message — classifies the ticket, restates the issue, gathers missing context, and routes the reply.
Starter skill template. Replace the body with your own playbook before relying on it; this stub exists only to teach the workspace layout.
First-response playbook for an inbound customer message — classifies the ticket, restates the issue, gathers missing context, and routes the reply.
Produces a citation-grounded summary across one or more workspace source files for summaries, briefings, literature reviews, or comparisons.
Visualise the result of an analysis as a chart (line, bar, area, scatter, etc.). Use when the user asks to "plot...", "chart...", "show me the trend of...", "visualise...", or when a numerical result has more than ~10 rows and would be easier to read as a picture. Produces an image file plus the script that generated it.
# 技能(Skill) 一个 skill 就是一份写好的能力包:一个目录里放一份 `SKILL.md`(说明用途、给 agent 看的指令),可以再带一些参考文档、脚本或样例。写好后丢给 agent,它会在合适的时候自己用。 harness 让你从两个地方装 skill: - **接 skill 市场**:Git 仓库、Nacos、MySQL、classpath、或者自己写的后端 - **放在工作区**:项目里 `workspace/skills/` 下的就所有人共用;放在 `<userId>/skills/` 下的只有那个用户看得到 两类来源同时生效,不需要二选一。 > 关于 skill 自身的结构、`SKILL.md` 写法、资源加载、tool 绑定、代码执行这些通用概念,见 [Agent Skill](../task/agent-skill.md)。本文只讲 harness 这一层的用法。 --- ## 一个例子 把团队的 skill 仓库接进来,agent 立刻就能用: ```java HarnessAgent agent = HarnessAgent.bui
Answer a quantitative business question by writing a SQL query against the data warehouse, validating it, and presenting the result. Use when the user asks "how many...", "what's the trend of...", "compare X vs Y over...", "what's our top N...", or anything that resolves to a query against tabular data. Produces a small result table plus the underlying query.
Starter skill template. Replace the body with your own playbook before relying on it; this stub exists only to teach the workspace layout.
Produces a citation-grounded summary across one or more workspace source files for summaries, briefings, literature reviews, or comparisons.
Starter skill template. Replace the body with your own playbook before relying on it; this stub exists only to teach the workspace layout.
Starter skill template. Replace the body with your own playbook before relying on it; this stub exists only to teach the workspace layout.
Writes and executes SQL queries ranging from simple single-table SELECTs to complex multi-table JOINs, aggregations, window functions, and subqueries. Use when the user asks to query the database, retrieve data, filter records, rank results, or generate reports.
Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in the database. Use when the user asks about database structure, table layout, column types, what tables exist, foreign keys, or how entities relate to each other.
Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in the database. Use when the user asks about database structure, table layout, column types, what tables exist, foreign keys, or how entities relate to each other.
Discover schema, write SELECT-only SQLite queries, execute, and explain results (aligned with harness-example).
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
Discover schema, write SELECT-only SQLite queries, execute, and explain results (aligned with harness-example).
Writes and executes SQL queries ranging from simple single-table SELECTs to complex multi-table JOINs, aggregations, window functions, and subqueries. Use when the user asks to query the database, retrieve data, filter records, rank results, or generate reports.
Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in the database. Use when the user asks about database structure, table layout, column types, what tables exist, foreign keys, or how entities relate to each other.
Discover schema, write SELECT-only SQLite queries, execute, and explain results (aligned with harness-example).
Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in the database. Use when the user asks about database structure, table layout, column types, what tables exist, foreign keys, or how entities relate to each other.
Writes and executes SQL queries ranging from simple single-table SELECTs to complex multi-table JOINs, aggregations, window functions, and subqueries. Use when the user asks to query the database, retrieve data, filter records, rank results, or generate reports.
Discover schema, write SELECT-only SQLite queries, execute, and explain results (aligned with harness-example).
Writes and executes SQL queries ranging from simple single-table SELECTs to complex multi-table JOINs, aggregations, window functions, and subqueries. Use when the user asks to query the database, retrieve data, filter records, rank results, or generate reports.
Use this skill when the task involves resizing, scaling, or compressing image files. Suitable for tasks like "resize these photos to 800px wide", "compress images to reduce file size", or "batch scale all JPEGs in a folder". Only relevant for image processing tasks — do NOT use for data files, text, or non-image tasks.
Use this skill when the task involves converting or reformatting structured data between file formats such as CSV, JSON, XML, or YAML. Suitable for tasks like "convert this CSV to JSON", "reformat my data file", or "change the file format of this dataset". Do NOT use for statistical analysis, aggregation, or report generation.
Use this skill when the task involves analyzing data to compute statistics, aggregations, or summaries, and presenting the results as a report. Suitable for tasks like "summarize this dataset", "calculate averages and totals", "generate a weekly sales report", or "show me trends in this data". Do NOT use for simple format conversion between file types.
Use this skill when the task involves inspecting, searching, or extracting information from application log files. Suitable for tasks like "my application is crashing and I need to find the errors", "scan these logs for warnings", "extract all exceptions from this log file", or "find slow requests in my server logs". Triggered by debugging, incident investigation, or log analysis needs.
Use this skill when the task involves generating a changelog, release notes, or commit summary from a Git repository's history. Suitable for tasks like "generate a changelog for v2.0", "summarize what changed since last release", or "create release notes from git commits". Requires access to a Git repository.
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
Database schema and business logic for sales data analysis including customers, orders, and revenue.
Database schema and business logic for inventory tracking including products, warehouses, and stock levels.
A skill for mathematical calculations
A skill for writing and content creation
Generate text, images, video, speech, and music via the MiniMax AI platform. Covers text generation (MiniMax-M2.7 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.
Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection.
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.
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.
Build custom LLM evaluation pipelines using the OpenJudge framework. Covers selecting and configuring graders (LLM-based, function-based, agentic), running batch evaluations with GradingRunner, combining scores with aggregators, applying evaluation strategies (voting, average), auto-generating graders from data, and analyzing results (pairwise win rates, statistics, validation metrics). Use when the user wants to evaluate LLM outputs, compare multiple models, design scoring criteria, or build an automated evaluation system.
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.
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.
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.
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.
Guidelines for handling text files
Guidelines for handling CSV/Excel files
Guidelines for handling databases
Guildlines for handling json files
Guidelines for handling image files
Guidelines and workflows for the agent to maintain persistent memory using native file tools (read_file, write_file, edit_file) and standard OS commands for searching.
This guide covers the design philosophy, core concepts, and practical usage of the AgentScope framework. Use this skill whenever the user wants to do anything with the AgentScope (Python) library. This includes building agent applications using AgentScope, answering questions about AgentScope, looking for guidance on how to use AgentScope, searching for examples or specific information (functions/classes/modules).