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skill-reinforcement技能强化

Agent Skill

skill-reinforcement 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

333

周安装

14

GitHub Stars

219

下载量

116
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:skill-reinforcement(技能强化)
来源仓库:https://github.com/different-ai/agent-bank
仓库路径:skills/skill-reinforcement
安装命令:
npx skills add https://github.com/different-ai/agent-bank --skill skill-reinforcement
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/different-ai/agent-bank --skill skill-reinforcement

简介

用于查找、检索和筛选强化学习相关技术资源。

  • 适合在算法研究或模型训练优化场景中提供支持。
  • 通过 GitHub 仓库安装,适用于 Codex、Claude 等宿主环境。
  • 检索结果需结合具体实验环境验证有效性。skill-reinforcement 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 建议优先选择有活跃维护记录的项目作为参考。

SKILL.md

What I Do

After ANY skill is used, this meta-skill triggers to:

  1. Analyze what worked and what didn't
  2. Identify new patterns or shortcuts discovered
  3. Update the skill file with learnings
  4. Prevent knowledge loss between sessions

Relationship with self-improve:

  • This skill (skill-reinforcement) = WHEN to update (post-use triggers)
  • The self-improve skill = HOW to update (templates, structures, decision trees)

When to Trigger

Invoke this skill automatically when:

  • Any skill from .opencode/skill/*/SKILL.md completes
  • A workflow succeeds or fails in a notable way
  • New shortcuts or anti-patterns are discovered
  • Token usage could be reduced with better patterns
  • API behavior differs from documentation
  • Commands fail and I find the fix
  • User confirms something works
  • I do the same task twice (should become a skill/tool)

Reinforcement Process

Step 1: Capture the Context

After using a skill, note:

- Skill used: [skill-name]
- Task: [what was being done]
- Outcome: [success/partial/failure]
- Token cost: [high/medium/low]
- Time taken: [fast/normal/slow]

Step 2: Identify Learnings

Ask these questions:

  1. What took longer than expected? → Document the fix
  2. What failed unexpectedly? → Add to "Common Issues"
  3. What shortcut was discovered? → Add to "Token Saving Tips"
  4. What assumption was wrong? → Correct in documentation
  5. What worked better than documented? → Update the workflow

Step 3: Categorize the Learning

CategoryWhere to AddExample
New shortcut"Token Saving Tips"OTP visible in email preview
Failure mode"Common Issues"Popup blocker breaks flow
Better patternMain workflowCheck login state first
Anti-pattern"Anti-Patterns to Avoid"Don't snapshot spam
Environment quirk"Prerequisites" or "Notes"Session persists

Step 4: Update the Skill File

# Read current skill
cat .opencode/skill/[skill-name]/SKILL.md

# Edit to add learning in appropriate section
# Use the Edit tool to append or modify

Step 5: Validate the Update

Ensure updates are:

  • Actionable - Not vague observations
  • Specific - Include exact commands/patterns
  • Formatted - Match existing style
  • Non-redundant - Don't duplicate existing content

Learning Templates

For New Shortcuts

### [Shortcut Name]

**Discovery**: [How it was found]
**Before**: [Old approach]
**After**: [New approach]
**Savings**: [Token/time reduction]

For Failure Modes

| Issue  | Symptom        | Fix              |
| ------ | -------------- | ---------------- |
| [Name] | [What you see] | [How to resolve] |

For Anti-Patterns

### Don't: [Bad Pattern Name]

// BAD - [explanation] [bad code]

Do: [Good Pattern Name]

// GOOD - [explanation]
[good code]
### For Workflow Improvements

Updated: [Section Name]

[New content that replaces or augments existing]

Note: Updated [date] after discovering [context]

## Real Examples

### Example 1: Session Persistence Discovery

**Context**: Testing staging branch, went through full login flow **Learning**: Chrome MCP persists sessions - was already logged in **Action**: Added "Session Persistence is Your Friend" section with check-first pattern

### Example 2: OTP Extraction Optimization

**Context**: Opened email, waited for load, extracted OTP **Learning**: OTP visible in Gmail list preview without opening email **Action**: Updated OTP section with faster "search + list preview" method

### Example 3: Deployment URL Pattern

**Context**: Manually constructed URL, got it wrong **Learning**: Can extract URL directly from Vercel bot's PR comment **Action**: Added `gh pr view` command to get URL automatically

## Skill File Structure Convention

Every skill should have these sections (add if missing):

What I Do

[Core purpose]

Prerequisites

[Requirements]

Workflow

[Main steps]

Common Issues

[Failure modes and fixes]

Token Saving Tips

[Efficiency patterns]

Anti-Patterns to Avoid

[What NOT to do]

Real Examples

[Actual usage examples]

Learnings Log

[Append-only log of discoveries - optional]


## Integration with Other Skills

When reinforcing a skill, check if learnings apply to related skills:

| Skill | Related Skills |
| --- | --- |
| test-staging-branch | Any Chrome MCP skill |
| skill-reinforcement | All skills (meta) |
| self-improve | skill-reinforcement (companion) |
| chrome-devtools-mcp | test-staging-branch, gmail |
| safe-infrastructure | new-vault-implementation |

Cross-pollinate learnings when applicable.

### Deciding What to Create

If reinforcement reveals a need for new capability, use the `self-improve` skill's decision tree:

Need to extend capabilities? │ ├─ Just need docs/commands? → SKILL ├─ Need specialized AI persona? → AGENT ├─ Need event hooks? → PLUGIN ├─ Need callable function? → TOOL └─ Need external integration? → MCP SERVER


## Automation Hooks

### Post-Skill Trigger

After completing any skill, automatically ask:

1. "Did anything unexpected happen?"
2. "Was there a faster way to do this?"
3. "What would I do differently next time?"

If answers exist, invoke skill-reinforcement.

### Periodic Review

Every ~10 skill uses, review:

- Most frequently used skills (prioritize improvements)
- Skills with most "Common Issues" (need better documentation)
- Skills with outdated information (need refresh)

## Meta: Reinforcing This Skill

This skill should also improve itself. Track:

- How often it triggers
- Quality of captured learnings
- Whether skills actually improve over time
- Time cost of reinforcement vs value gained

## Quick Reinforcement Checklist

[ ] Skill completed [ ] Outcome noted (success/fail/partial) [ ] Any surprises? → Document [ ] Any shortcuts found? → Add to tips [ ] Any failures? → Add to issues [ ] Could be faster? → Add anti-pattern [ ] Update skill file [ ] Validate formatting [ ] Done


## Immediate Update Rule

**UPDATE IMMEDIATELY** - Don't wait until end of conversation.

When any of these happen, stop and update the relevant skill:

1. API format is wrong (like `-d` vs `-F` for curl)
2. Commands fail and I find the fix
3. User confirms something works
4. I discover a better/faster way
5. Something is missing from docs

Example:

❌ "I'll note this for later" ✅ *immediately edits skill file*


## Learnings Log

- 2026-01-12: Bulk remote branch cleanup can hit stale refs and timeouts; run `git fetch --prune origin` first, delete with a loop that tolerates missing refs, then prune again to verify only `origin/main` remains.
- 2026-01-13: When extending agent policy docs, renumber numbered headings after inserts and place testing tools + real-funds protocol near the Testability section for clarity.
- 2026-01-13: When refactoring MCP tool handlers into a registry, remove the legacy switch and align handler arg types with tool schemas to avoid type errors.
- 2026-01-13: When adding a new Next.js route handler export, ensure it sits outside existing handler functions to avoid "Modifiers cannot appear here" errors.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenCode

27.54%
按下载量换算32

Claude Code

22.96%
按下载量换算27

Antigravity

19.33%
按下载量换算22

Cursor

12.88%
按下载量换算15

Codex

8.86%
按下载量换算10

Gemini CLI

3.81%
按下载量换算4

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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