Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
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Structured decision-making framework for technical proposals and trade-off analysis. When the user faces a choice between multiple approaches (architecture decisions, tool selection, refactoring strategies, migration paths), this skill produces a 1-3-1 format: one clear problem statement, three distinct options with pros/cons, and one concrete recommendation with definition of done and implementation plan. Use when the user asks for a "1-3-1", says "give me options", or needs help choosing between competing approaches.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Structured decision-making format for when a task has multiple viable approaches and the user needs a clear recommendation. Produces a concise problem framing, three options with trade-offs, and an actionable plan for the recommended path.
Do NOT use for simple questions with one obvious answer, debugging sessions, or tasks where the user has already decided on an approach.
Problem (one sentence)
Options (exactly three)
Recommendation (one option)
Definition of Done
Implementation Plan
User: "Give me a 1-3-1 for adding retry logic to our API client."
Agent:
Problem: API calls to external services fail intermittently under load and need graceful retry handling.
Options:
Option A: Exponential backoff with jitter in a shared utility. Pros: Simple, well-understood pattern; single implementation shared across all callers. Cons: No circuit-breaking; callers still block during retries.
Option B: Retry library (tenacity) as a decorator on each endpoint call. Pros: Configurable per-endpoint; built-in support for circuit breakers and rate limits. Cons: Adds a dependency; decorator pattern can obscure control flow.
Option C: Queue-based async retry with dead letter handling. Pros: Non-blocking; failed calls are tracked and replayable; scales well. Cons: Significant complexity increase; requires a queue backend; overkill for low-volume services.
Recommendation: Option A. The call volume doesn't justify queue infrastructure, and a shared utility with exponential backoff + jitter covers the failure mode cleanly without new dependencies.
Definition of Done:
api_retry() utility handles transient HTTP errors (429, 502, 503, 504).Implementation Plan:
utils/api_retry.py with configurable max retries, base delay, and retryable status codes.random.uniform(0, base_delay) to prevent thundering herd.api_client.py with the retry utility.Imported from NousResearch Hermes Agent optional skills (optional-skills/communication/one-three-one-rule) under the MIT license. Source: https://github.com/NousResearch/hermes-agent/tree/main/optional-skills/communication/one-three-one-rule
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Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
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Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
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Adds educational comments to a code file so it becomes a learning resource, asking for the file (with a list of close matches) if none is given. Use when preparing code for learners, onboarding, or teaching.
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Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
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Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
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Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
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