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Software Data Self Improving Developer Helper

面向AI代理用户、技能作者及维护团队,核心提供AI代理工作流的bug修复、安全加固、可靠性提升及相邻技能构建的实用支持。典型场景包括需求自改进代理工作流、修复相关bug、生成适配的工作流或清单等。输入含指定触发关键词的需求,输出定制化方案、可复用清单及验证说明,优先本地硬件友好的实现方式。

kyro-ma
kyro-ma
热度 46.55
最近更新 2026-09-10 13:30:49
分类:开发依赖:代码执行 / 读写文件 / 内部调用大模型应用场景:调试排错 / 代码生成 / 自动化脚本 / 工作流 ...

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add software-data-self-improving-developer-helper
Metadata
name
software-data-self-improving-developer-helper
description
Help users with Validated demand: Agent users show strong demand for self-improving agent-style workflows on Clawhub. They need practical help fixing bugs, hardening setup and safety, improving reliability, or creating adjacent skills inspired by the same job-to-be-done. This requirement is supported by 10 separate online signals across 3 source families, so it represents broader demand rather than a single isolated request.. Use when a user asks for software-and-data, self-improving-agent, self, improving, captures, or needs a practical workflow, artifact, checklist, analysis, or implementation support for this requirement.

Software Data Self Improving Developer Helper

Requirement

Use this skill to help AI-agent users, skill authors, maintainers, and teams who want proven popular skill patterns adapted into more reliable or adjacent workflows with:

Validated demand: Agent users show strong demand for self-improving agent-style workflows on Clawhub. They need practical help fixing bugs, hardening setup and safety, improving reliability, or creating adjacent skills inspired by the same job-to-be-done. This requirement is supported by 10 separate online signals across 3 source families, so it represents broader demand rather than a single isolated request.

Demand score: 100/100 (70/70 demand, 30/30 local feasibility). Evidence: 10 signals across 3 source families.

Read references/requirement-plan.md when source evidence, planning details, or review criteria are needed.

Workflow

  1. Restate the user's outcome, constraints, available inputs, and success criteria.
  2. Inspect technical constraints, propose implementation steps, and include test or verification commands when code or data is involved.
  3. Ask only for missing information that materially changes the output; otherwise make reasonable assumptions and continue.
  4. Keep the implementation local-hardware friendly: prefer scripts, templates, checklists, and small-model or CPU-safe workflows over cloud-only or large-training approaches.
  5. Produce the requested artifact, workflow, checklist, analysis, code change, or decision support.
  6. Validate the output against the success criteria and list any remaining risks or follow-up work.

Expected Outputs

  • A tailored answer or artifact for the user's immediate situation.
  • A reusable checklist or workflow when the task is repeatable.
  • A verification note showing how the result was checked.

Validation

  • The output directly addresses the discovered requirement.
  • The user can act on the result without reading the original source post.
  • Assumptions, limits, and required inputs are visible.
  • The final response includes a short usage or next-step note when helpful.

Triggers

Keywords: software-and-data, self-improving-agent, self, improving, captures, learnings, errors, corrections, enable, bug fix

Example trigger sentences:

  • Help me Agent users show strong demand for self-improving agent-style workflows on Clawhub. They need practical help fixing bugs.
  • I need a practical workflow for Agent users show strong demand for self-improving agent-style workflows on Clawhub. They need practical help fixing bugs.
  • Use $software-data-self-improving-developer-helper to handle Agent users show strong demand for self-improving agent-style workflows on Clawhub. They need practical help fixing bugs.