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eval-harness-updater评估线束更新程序

Agent Skill

eval-harness-updater 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

1,001

周安装

43

GitHub Stars

25

下载量

351
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

2

许可证

unknown

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:eval-harness-updater(评估线束更新程序)
来源仓库:https://github.com/oimiragieo/agent-studio
仓库路径:skills/eval-harness-updater
安装命令:
npx skills add https://github.com/oimiragieo/agent-studio --skill eval-harness-updater
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。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/oimiragieo/agent-studio --skill eval-harness-updater

简介

eval-harness-updater 刷新评估线束以应对不稳定环境下的解析与超时问题。

  • 适用于 prompt 漂移、流中断或部分响应一致性保障。
  • 必须结合 Exa 与 arXiv 研究最新最佳实践后再行修补。
  • 同时处理 RED 回归测试与配套工件缺失问题。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Eval Harness Updater

Refresh eval harnesses to keep live + fallback modes actionable under unstable environments.

Focus Areas

  • Prompt and parser drift
  • Timeout/partial-stream handling
  • SLO and regression gates
  • Dual-run fallback consistency

Workflow

  1. Resolve harness path.
  2. Research test/eval best practices (Exa + arXiv — see Research Gate below).
  3. Add RED regressions for parsing and timeout edge cases.
  4. Patch minimal harness logic.
  5. Validate eval outputs and CI gates.
  6. Resolve companion artifact gaps (see Cross-Reference table below).

Research Gate (Exa + arXiv — BOTH MANDATORY)

Before proposing harness changes, gather current best practices:

  1. Use Exa for implementation and ecosystem patterns:

- mcp__Exa__web_search_exa({query: 'LLM eval harness 2025 best practices'}) - mcp__Exa__get_code_context_exa({query: 'eval harness parser reliability timeout handling'})

  1. Search arXiv for academic research on evaluation methodology (mandatory):

- Via Exa: mcp__Exa__web_search_exa({query: 'site:arxiv.org LLM evaluation harness 2024 2025'}) - Direct API: WebFetch({url: 'https://arxiv.org/search/?query=LLM+evaluation+harness&searchtype=all&start=0'})

  1. Record decisions, constraints, and non-goals in memory learnings.

arXiv is mandatory (not fallback) when topic involves: LLM evaluation, agent evaluation, SLO gates, regression testing methodology, or parser reliability.

Cross-Reference: Creator Ecosystem

This skill is part of the Creator Ecosystem. When research uncovers gaps, trigger the appropriate companion creator:

Gap DiscoveredRequired ArtifactCreator to InvokeWhen
Domain knowledge needs a reusable skillskillSkill({skill: 'skill-creator'})Gap is a full skill domain
Existing skill has incomplete coverageskill updateSkill({skill: 'skill-updater'})Close skill exists but incomplete
Capability needs a dedicated agentagentSkill({skill: 'agent-creator'})Agent to own the capability
Existing agent needs capability updateagent updateSkill({skill: 'agent-updater'})Close agent exists but incomplete
Domain needs code/project scaffoldingtemplateSkill({skill: 'template-creator'})Reusable code patterns needed
Behavior needs pre/post execution guardshookSkill({skill: 'hook-creator'})Enforcement behavior required
Process needs multi-phase orchestrationworkflowSkill({skill: 'workflow-creator'})Multi-step coordination needed
Artifact needs structured I/O validationschemaSkill({skill: 'schema-creator'})JSON schema for artifact I/O
User interaction needs a slash commandcommandSkill({skill: 'command-creator'})User-facing shortcut needed
Repeated logic needs a reusable CLI tooltoolSkill({skill: 'tool-creator'})CLI utility needed
Narrow/single-artifact capability onlyinlineDocument within this artifact onlyToo specific to generalize

Iron Laws

  1. ALWAYS run the Exa + arXiv research gate before updating any eval harness — updating without current external knowledge produces stale evaluation criteria.
  2. NEVER remove existing evaluation criteria without replacing them with equivalent or better ones — reducing test coverage in an eval harness is a regression.
  3. ALWAYS cross-reference the creator ecosystem for gaps before declaring the harness complete — missing companion artifacts (skills, agents, schemas) leave the harness unable to test new capabilities.
  4. NEVER update eval harness in isolation from the skill/agent it evaluates — harness and artifact must stay synchronized or the harness tests the wrong behavior.
  5. ALWAYS preserve backward compatibility in eval scoring — changing scoring semantics without migrating historical baselines makes trend analysis impossible.

Anti-Patterns

Anti-PatternWhy It FailsCorrect Approach
Updating eval harness without research gateCriteria based on outdated knowledge; misses recent evaluation methodology advancesAlways run Exa + arXiv research before updating any eval criteria
Removing test cases to simplify the harnessSilently reduces coverage; regressions pass undetectedOnly remove test cases when the behavior they tested has been deliberately removed
Harness and artifact in separate PRsHarness tests wrong behavior the moment artifact changes; immediate test driftAlways update harness and artifact in the same commit
Changing scoring scale mid-projectHistorical baselines become incomparable; trend analysis breaksDefine scoring scale once; create a migration if it must change
Declaring harness complete without companion checkMissing skills or schemas leave evaluation gapsAlways run companion artifact check before marking harness update complete

Memory Protocol (MANDATORY)

Before starting: Read .claude/context/memory/learnings.md

After completing:

  • New evaluation pattern → .claude/context/memory/learnings.md
  • Evaluation gap found → .claude/context/memory/issues.md
  • Scoring decision made → .claude/context/memory/decisions.md
ASSUME INTERRUPTION: If it's not in memory, it didn't happen.

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

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

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

Codex

35.87%
按下载量换算126

Claude

27.97%
按下载量换算98

Cursor

19.52%
按下载量换算69

Gemini CLI

8.56%
按下载量换算30

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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