Design AI agents that break down goals, plan actions, use tools, and correct their own mistakes. The guidance focuses on reliable execution, clear operating boundaries, and managing conversation context as tasks grow longer.
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Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.
安装前可以查看交给 AI 代理的指令内容。
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.
This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% by step 10. Build for reliability first, autonomy second.
2025 lesson: The winners are constrained, domain-specific agents with clear boundaries, not "autonomous everything." Treat AI outputs as proposals, not truth.
Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
class ContextManager: def init(self, max_tokens=100000): self.max_tokens = max_tokens self.messages = []
def add(self, message):
self.messages.append(message)
self.maybe_compact()
def maybe_compact(self):
if self.token_count() > self.max_tokens * 0.8:
self.compact()
def compact(self):
# Always keep: system prompt
system = self.messages[0]
# Always keep: last N messages
recent = self.messages[-10:]
# Summarize: everything else
middle = self.messages[1:-10]
if middle:
summary = summarize_messages(middle)
self.messages = [system, summary] + recent
User request:
Use @autonomous-agents for this task: Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance.
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Assess a new or existing AI project by examining its code, goals, and constraints. Recommend skills confirmed to exist in the available registry, then outline tasks and implementation phases with dependencies, completion criteria, and checks.
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Review code and infrastructure for security risks, map potential threats, and plan stronger defenses. The skill covers OWASP checks, attack and defense exercises, and incident response, with validation required for each environment.
中文简介正在准备中。当前显示英语简介。
Turn a repeatable task into an Andru.ia skill with clear inputs, outputs, examples, and limits. Draft SKILL.md, check the folder structure and required validations, and prepare a pull request describing the changes for review.
中文简介正在准备中。当前显示英语简介。
Prepare an industry briefing once a project's sector is defined. It covers sector context, potentially applicable regulations, familiar interface patterns, technology choices, and risks, while separating verified evidence from hypotheses and questions requiring further review.
中文简介正在准备中。当前显示英语简介。
Create presentation drafts from text, outlines, or documents through the 2slides API. Match a reference image's style, choose themes, add AI voice narration, and export slide images or audio for review and use.
中文简介正在准备中。当前显示英语简介。
Plan a 360-degree or peer feedback process and create empty response templates from confirmed requirements. Produces consistent CSV, SQL, JSON Schema, or Notion property mappings while keeping visibility choices and scoring configuration separate.
中文简介正在准备中。当前显示英语简介。