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デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/tsinghua-fib-lab/AgentSociety --skill my-custom-skillコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Use when an experiment run has completed and the user wants rigorous interpretation, claim-driven charts, bilingual reports, or cross-hypothesis synthesis from simulation data. Also use when multiple charts or PNG/JPG assets must be assembled into one labeled composite figure. Requires high-quality narrative and evidence traceability, not only harness gate PASS.
Create distinctive, production-grade frontend interfaces with high design quality. Use when building or polishing analysis HTML reports under agentsociety-analysis — read via support/frontend-design/ inside that skill, not as a separate pipeline skill.
Use when a completed research manuscript needs a robust internal, venue-calibrated mock peer review. Orchestrates three isolated reviewer subagents in parallel, then a separate MetaReview subagent that verifies evidence, reports agreement and score dispersion, and emits advisory reroutes. Never revises research artifacts, executes experiments, or updates pipeline state.
SOC 職業分類に基づく
SKILL.md を表示中
| name | my-custom-skill |
| description | Example custom agent skill — a template to get started. |
This is a template for creating a custom agent skill.
There are two valid skill patterns in the current architecture:
No script field in frontmatter. This SKILL.md is injected into the LLM's context
when activate_skill is called. The LLM then uses built-in atomic tools
(read, write, append, list, grep, ask_env) to accomplish the task.
This is the primary extension mechanism — like Claude Code's slash commands.
Add script: scripts/my-script.py to frontmatter. The script is executed as a
cached in-process module via entrypoint(argv, ctx) when available, with dynamic
wrapper/subprocess fallback. It should communicate via the returned stdout string
and file I/O under ctx.workspace_root. Scripts cannot access the LLM or
environment router directly — use this only for deterministic computation.
When this skill is activated:
ask_env to query the environment for relevant information.read / write / append to persist state.execute_skill_script for deterministic computation when a script exists.finish with a summary when finished.When activated, the agent should:
ask_env with instruction: "What happened recently? Summarize recent events."read path journal.jsonl to load previous entries if it exists.append path journal.jsonl to add today's entry.finish with summary of what was journaled.