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Le prompt qui vérifie d'abord la source est sélectionné par défaut. Vous pouvez passer à une commande directe ou télécharger une copie locale.
Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.
Basé sur la classification professionnelle SOC
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
npx skills add https://github.com/tsinghua-fib-lab/AgentSociety --skill my-custom-skillLa commande reste sur une seule ligne. Faites défiler horizontalement pour la vérifier avant de la copier.
Vous préférez une copie locale ? Téléchargez les fichiers actuellement disponibles dans SkillsMP.
Affichage de SKILL.md
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.
| 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.