Token导航 LogoToken导航

Agent Skill · 研究检索

agent-integration(Agent 整合)

agent-integration 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。

agent-integration 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 59,579。

总安装 384周安装 16GitHub Stars 3下载 128核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:agent-integration(Agent 整合)
来源仓库:https://github.com/veris-ai/veris-skills
仓库路径:skills/agent-integration
安装命令:
npx skills add https://github.com/veris-ai/veris-skills --skill agent-integration
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/veris-ai/veris-skills --skill agent-integration

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

Agent 整合技能用于将代理仓库与 Veris 从零开始集成,使其成为可推送的测试平台。适用于从普通客户代理源转换为 Veris-ready 代理的测试和验证场景。通过 GitHub 仓库安装,使用 npx skills add 命令加载,需参考原始 README 了解详细配置。

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。注意该技能存在版本兼容性问题,需检查 .veris/ 文件以避免冲突。

来源仓库内路径:skills/agent-integration

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/veris-ai/veris-skills --skill agent-integratio…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

agent-integration 怎么安装?

在终端执行 npx skills add https://github.com/veris-ai/veris-skills --skill agent-integration。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

agent-integration 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

agent-integration 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

agent-integration 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

agent-integration 用的人多吗?

公开数据是累计安装 384 次,近一周安装 16 次,下载 128 次,GitHub 3 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

agent-integration 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
agent-integration

Agent 整合 · 本页

3843接触敏感数据未明确未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Integrate this agent repo with Veris from scratch.

This skill takes a repo from "plain customer agent source" to "Veris-ready and pushable." If the user provided a path to an agent repo, use that as the repo root. Otherwise use the current working directory.

Treat any existing .veris/ files or old scaffold output as starting material only. Use the current bundled references in this skill as the source of truth for what you generate.

Core framing: the agent is the constant, Veris is the test harness

Veris exists to test an agent under realistic conditions. The agent is the thing being tested; Veris is the harness around it. That asymmetry drives every decision in this skill:

  • The agent runs the same way in Veris as it does in production. If the agent speaks HTTP to a Slack Web API in prod, it speaks HTTP to the Veris Slack mock in sim. If it shells out to a CLI in prod, it shells out in sim. No special simulation code path.
  • All integration work lives in .veris/. .veris/veris.yaml, .veris/Dockerfile.sandbox, .veris/config.yaml, .veris/.dockerignore are the deployment descriptor — the equivalent of a Helm chart or docker-compose.yaml for this agent. They describe *how to stand the agent up for this environment*. They do not contain behavior that belongs inside the agent.
  • Do not write wrappers, shims, or glue code that "adapts" the agent to Veris. A Python file that wraps a CLI agent to expose a callable, a script that translates Veris's actor format into the agent's native format, a patched version of the agent that accepts Veris-specific parameters — all of these are the wrong shape. They mean the thing you end up testing is not the agent.
  • Do not modify the agent's source code to make it work in Veris. If the agent assumes something Veris can't satisfy as-is, that's either a Veris platform gap to be logged, or a real issue with the agent that would also break production. Either way, the fix does not belong in the agent's source.
  • If you find yourself needing a wrapper, stop and treat it as a finding. Ask: what is the agent's real production integration path? If the agent has an HTTP server in prod, use that. If it's CLI-only in prod and Veris's actor can't drive a CLI, escalate — that's a Veris capability gap, not a license to invent glue.
  • The one legitimate .veris/ file that is not pure config is a container-orchestration start.sh for bundling multiple processes (e.g., a database alongside the agent). Even that starts and runs the agent as-shipped; it does not transform its behavior.

When in doubt: the agent's author should be able to read .veris/ and recognize it as "the deploy config for Veris," not as "someone forked and patched my agent."

Core rules

  • Explain what you are about to do before each major step.
  • Surface decisions with real tradeoffs and let the user choose.
  • Cite concrete evidence from the repo when you classify dependencies or decide how the agent should be integrated.
  • Do not silently preserve stale Veris config. Migrate it to the current preferred shape.
  • Do not generate .env.simulation. The current runtime flow is agent.environment plus veris env vars set.
  • Prefer the current actor.channels schema and canonical service names. Do not generate legacy persona.modality, email_address, or old service aliases unless the user explicitly asks for compatibility.
  • Do not write Python wrappers, shell shims, or any "adapter" code that translates between Veris and the agent. Use the agent's real production interface. If that isn't possible as-is, surface it as a platform gap, not as a wrapper opportunity.
  • Ask before external or irreversible actions:

- installing veris-cli - running veris login - running veris env create - setting environment variables with veris env vars set - pushing with veris env push

Fast-track mode

If the user says "go all the way", "do everything", or otherwise pre-approves the full flow:

  • Skip intermediate checkpoints (end-of-Phase 2, end-of-Phase 3, end-of-Phase 4)
  • Still explain decisions inline as you make them, so the user can follow along
  • Still stop and ask before truly irreversible or external actions: veris env create, veris env push, veris env vars set with real secrets
  • If a decision has genuinely ambiguous tradeoffs (e.g., bundle-vs-external for a heavy service), pause and ask even in fast-track mode
  • At the end, present a consolidated summary of all decisions made

Read these files when needed

Workflow Overview

PhaseGoal
0Bootstrap Veris tooling and environment
1Discover the repo and current runtime
2Analyze dependencies and service strategy
3Choose integration mode and container architecture
4Generate .veris/veris.yaml
5Generate .veris/Dockerfile.sandbox and supporting files
6Configure runtime env vars, validate, and push
7Smoke-validate with a single scenario + simulation

Phase 0: Bootstrap Veris Tooling And Environment

[Phase 0/7]

Tell the user: "I'm going to make sure this repo has the Veris tooling and environment wiring needed for the rest of the integration work."

0.1 Verify repo root

Confirm the directory is an agent repo, not just a parent folder. Look for source code, dependency manifests, and app entrypoints.

0.2 Verify veris-cli

Check whether veris is installed and working.

If not installed:

  • Prefer uv tool install veris-cli
  • Fallback: pip install veris-cli

Explain which install path you are using and why.

0.3 Verify Veris authentication

Check whether the user is already logged in and which profile/backend they are using.

If not authenticated:

  • Recommend veris login for browser auth
  • Use API-key login only if the user explicitly prefers it

Do not proceed to veris env push until auth is working.

0.4 Verify or create .veris/

Inspect:

  • .veris/config.yaml
  • .veris/veris.yaml
  • .veris/Dockerfile.sandbox
  • .veris/.dockerignore

If .veris/ does not exist, or it exists but has no environment binding:

  1. Derive a candidate environment name from the repo directory.
  2. Show the user the proposed name.
  3. On approval, run veris env create --name "<name>".

Explain what veris env create gives them:

  • .veris/veris.yaml — Veris simulation config
  • .veris/Dockerfile.sandbox — image build definition
  • .veris/.dockerignore — build-context exclusions
  • .veris/config.yaml — environment binding for this repo

0.5 Treat scaffolding as placeholders, not truth

The generated .veris/ files are just a starting point. They may use old defaults or generic placeholders. You are responsible for replacing them with the correct integration for this repo.

Proceed directly to Phase 1.


Phase 1: Discover The Repo And Current Runtime

[Phase 1/7]

Tell the user: "I'm going to inventory how this repo currently runs, what it depends on, and how users interact with it."

1.1 Existing Veris state

If .veris/ already exists, read all existing Veris files first. Call out anything that looks stale or legacy:

  • persona.modality
  • email_address
  • old service names like crm, calendar, oracle
  • missing .veris/config.yaml env binding
  • assumptions that conflict with the current docs

1.2 Infrastructure files

Read and summarize any of:

  • docker-compose.yml, docker-compose.yaml, compose.yml
  • Dockerfile, Dockerfile.*
  • Procfile
  • supervisord.conf, supervisord.ini
  • vercel.json, serverless.yml, netlify.toml
  • Kubernetes manifests

Identify:

  • which process is the user-facing agent
  • what other services exist
  • how the system currently starts

1.3 Environment and secrets

Read:

  • .env.example, .env.sample, .env.template
  • config/settings modules
  • secret or vault references

Collect every env var the agent reads, and note which are:

  • stable non-secrets
  • secrets
  • service endpoints
  • optional or debug-only

1.4 Dependencies

Read the package manifests for the repo’s language/runtime and identify:

  • package manager
  • framework
  • Python/Node runtime assumptions
  • SDKs for external services

1.5 Source-code entrypoints

Find the actual code path that handles incoming user work:

  • app/server entrypoint
  • chat/message handler
  • config/settings module
  • request routing
  • any background worker or webhook listener that matters during a user conversation

1.5a Platform-hosted agents (config-only repos)

If the repo has no traditional application entrypoint — no main.py, app.py, server.js, index.ts — check whether it is a platform-hosted agent: a repo of config files that runs on an installed framework (CrewAI, LangServe, AutoGen, Dify, n8n, Flowise, or similar).

Signs:

  • Primary files are YAML/JSON config, prompt templates, and tool definitions
  • pyproject.toml or package.json lists a framework as the main dependency
  • No substantial application logic beyond small tool/hook files
  • README instructions say "install [framework], then run [framework command]"

If this is the case:

  • The framework is the runtime — it will be installed in the Dockerfile, not built from source
  • The entry point is the framework's CLI or server command
  • See Pattern 8 in reference/infrastructure-patterns.md for the full restructuring approach
  • Watch for source-tree compile errors if you attempt pip install. on these repos

1.6 Determine the integration interface

This is critical. Determine how the simulated actor should talk to the agent.

Look for four classes of interfaces:

HTTP

  • Chat endpoint
  • Request/response body shape
  • Session or conversation field
  • JSON or SSE response style

WebSocket

  • WS route
  • Message framing
  • Session handling

Email

  • Inbox address
  • Polling or webhook flow

Function

  • A clean Python callable the agent *already exposes* as part of its public API
  • Existing handle_message-style functions the agent's own documentation treats as an entry point

Do not invent a function interface by wrapping a CLI or a server. If the agent is CLI-only in production, the integration is CLI-driven — surface that and find the right Veris channel for it, or log it as a platform gap. A function channel is only correct when the repo already ships a callable as its primary or documented interface.

If both network and function modes are viable, use the repo's real product interface. That is what runs in production; that is what we test.

Tell the user exactly what you found and confirm the likely best integration path before continuing.

Proceed to Phase 2.


Phase 2: Analyze Dependencies And Service Strategy

[Phase 2/7]

Tell the user: "I'm now classifying each dependency into mock, bundle, external, or skip."

Read:

For every dependency, classify it as one of:

  1. Mock with Veris
  2. Bundle inside the container
  3. Use an external endpoint
  4. Skip entirely
  5. Needs discussion
  6. Allow real egress — the agent must reach the real internet (e.g., web search, URL scraping, live API with no mock). Results will be nondeterministic across simulation runs.

Classification rules

  • Always read the source code before deciding. Do not infer importance from service names alone.
  • Show evidence when you decide something is skippable.
  • Surface bundle cost when it matters, especially for heavy services like Elasticsearch or LocalStack.
  • Prefer mock services when the dependency maps cleanly to Veris.
  • Prefer env-var overrides over code changes whenever possible.

Special cases

Postgres

  • Decide whether to use Veris postgres or an external DB
  • If using Veris postgres, find the schema artifact or migration source and determine the best copy path

LLM providers

  • No Veris service entry is needed
  • The LLM proxy intercepts supported domains automatically

Email

  • If the actor uses an email channel, note that the Veris email service is injected automatically

Auth helpers

  • Google/Microsoft/Atlassian/Intuit auth helpers are platform-level helpers, not services you should normally add manually

Web search and scraping

  • If the agent calls search APIs (Google, Bing, SerpAPI, Tavily, Brave Search, DuckDuckGo) or fetches live URLs, these cannot be mocked
  • Classify as "Allow real egress"
  • Warn the user: live internet calls make simulation results nondeterministic — the same scenario may produce different outputs on different runs
  • If the search is truly optional (e.g., a fallback when the knowledge base has no answer), consider disabling it via env var for deterministic simulations

Real internet egress (general)

  • Some agents need to hit arbitrary external endpoints that Veris cannot mock (webhooks to third-party services, real-time data feeds, public REST APIs without a Veris service)
  • These also classify as "Allow real egress"
  • The Veris container allows outbound internet by default for non-intercepted domains
  • Surface the nondeterminism tradeoff to the user

Checkpoint

Walk through your dependency analysis with the user before moving on. The user should understand:

  • what will be mocked
  • what will be bundled
  • what stays external
  • what gets skipped
  • what still needs a decision

Wait for approval before proceeding.


Phase 3: Choose Integration Mode And Container Architecture

[Phase 3/7]

Tell the user: "I'm locking down how this agent will run inside the Veris container and how the actor will talk to it."

Read reference/infrastructure-patterns.md.

3.1 Choose the channel strategy

Pick one of:

  • HTTP — preferred when the product is already an HTTP chat API
  • WebSocket — preferred when real-time stateful messaging is core
  • Email — preferred when the product is genuinely email-driven
  • Function — preferred when the repo has a clean callable path or should be treated as a one-shot request/response agent

3.2 Function-channel rules

If you choose a function channel:

  • The callable path must be something the agent repo already exposes as a public interface (documented, referenced in its README, or otherwise part of its contract)
  • Do not create a wrapper file to conjure a callable out of a CLI or server — if the repo doesn't already expose one, function is the wrong channel
  • Omit agent.entry_point and agent.port in veris.yaml
  • If the callable is one-shot and stateless, set actor.config.MAX_TURNS: 1

3.3 Network-channel rules

If you choose HTTP / WS / email:

  • determine the exact request and response mappings
  • determine the startup command
  • choose a non-reserved port
  • decide whether start.sh is needed for bundled infra or multiple processes

3.4 Container layout

Determine:

  • what gets copied into /agent
  • which files should stay out of the image
  • whether a start.sh is required to bundle multiple processes (this is container orchestration, not agent modification)

Do not plan "which code changes are necessary." The target is zero code changes to the agent. If an env-var override isn't enough and the agent genuinely can't run as-shipped, that's a finding — escalate it rather than patching the source.

Checkpoint

Explain:

  • how the actor will communicate with the agent (using the agent's real production interface)
  • how the agent will start inside the container (its real production start command)
  • what files will be copied

If you believe *any* agent-side code change is needed, flag it here and stop. The default answer is zero code changes. If you can't see a way forward without one, it's probably a Veris platform gap, not an integration step.

Wait for approval before proceeding.


Phase 4: Generate .veris/veris.yaml

[Phase 4/7]

Tell the user: "I'm generating the final Veris configuration in the current preferred schema."

Read:

Rules

  • Use actor.channels, not persona.modality
  • Use canonical service names from reference/service-mapping.md
  • Use agent_inbox, not email_address
  • Only set actor.config.MAX_TURNS when there is a concrete reason, usually a one-shot function integration
  • Do not add the *_INTERVAL knobs unless the user explicitly asks for advanced tuning
  • Keep secrets out of veris.yaml
  • Put stable non-secret defaults in agent.environment
  • Only use ${VAR} in agent.environment when you need expansion/composition; if the agent can read a runtime env var directly, prefer setting it with veris env vars set

Channel-specific rules

HTTP / WS / Email

  • include agent.code_path
  • include agent.entry_point
  • include agent.port

Function

  • include agent.code_path
  • omit agent.entry_point
  • omit agent.port
  • set actor.channels[0].type: function
  • set callable:...

Checkpoint

Show the complete veris.yaml, explain the sections, and get approval before writing or finalizing it.


Phase 5: Generate .veris/Dockerfile.sandbox And Supporting Files

[Phase 5/7]

Tell the user: "I'm generating the image build and any small support files needed for this integration."

Read:

5.1 Dockerfile rules

  • Start with:
ARG GVISOR_BASE
FROM ${GVISOR_BASE}
  • Build context is the repo root
  • Copy dependency manifests before source code
  • Copy only what the agent actually needs
  • End with WORKDIR /app
  • Do not bake veris.yaml into the image

5.2 Runtime notes

  • The current base image already includes Python, uv, and Node.js
  • Only install extra runtimes or system packages when the repo truly needs them
  • If using a function channel, you still package the agent code and dependencies normally; you just do not start a network server

5.3 Supporting files

Create only what is needed:

  • start.sh for bundling multiple processes (e.g., a database alongside the agent) — this is container orchestration, same as a docker-compose.yaml would be
  • .veris/.dockerignore updates if the repo has large directories the default ignore file misses

Do not create Python "wrapper modules" that expose the agent as a callable, translate Veris actor calls into the agent's native format, or otherwise insert themselves between the actor and the agent. Use the agent's real interface.

5.4 No code changes to the agent

The agent runs in Veris exactly as it runs in production. That means:

  • No source-code patches to accommodate simulation
  • No "simulation mode" flags or Veris-specific branches
  • No forked copies of the agent with local modifications

If you find yourself wanting to change the agent's source, stop. Either:

  • The change can be expressed as an env-var override (then do it that way, via agent.environment or veris env vars set), or
  • The change is a real issue in the agent (then it's the customer's responsibility to fix, and it would also affect production), or
  • Veris can't accommodate the agent as-shipped (then it's a platform gap — escalate)

Unrelated refactors are obviously out.

Proceed directly to Phase 6 once the files are in place.


Phase 6: Configure Runtime Env Vars, Validate, And Push

[Phase 6/7]

Tell the user: "I'm turning this into a pushable Veris environment."

Read:

6.1 Build the env-var plan

Classify env vars into:

  1. Stable non-secret defaults → put in agent.environment
  2. Secrets / per-environment values → set with veris env vars set
  3. Local-only convenience values → optional root .env or shell exports for local smoke tests

Do not create .env.simulation.

6.2 Produce exact commands

Generate the exact veris env vars set commands the user needs.

If the user provides actual values and wants you to do it, run the commands for them.

Shell interpolation pitfall: when running veris env vars set KEY="$VAR" --secret with a shell variable, verify the source variable is actually set first (printenv VAR or test -n "$VAR"). An empty or unset variable expands to "" silently — the CLI will happily save an empty secret with no error, and the agent will fail at runtime with a confusing auth/provider error instead of a clear "missing key" message.

6.3 Validate push preconditions

Before pushing, verify:

  • veris is installed
  • auth/profile works
  • .veris/config.yaml has an environment ID
  • .veris/veris.yaml exists
  • .veris/Dockerfile.sandbox exists

Optional but encouraged:

  • run a local docker build -f.veris/Dockerfile.sandbox. smoke test when that is likely to catch obvious breakage quickly

6.4 Push

If the user approves, run:

veris env push

Or with an explicit tag if the user wants one:

veris env push --tag <tag>

If the push fails:

  • diagnose the failing build step
  • fix the integration
  • retry

6.5 Final summary

Summarize:

  • files created or modified
  • integration mode chosen
  • services mocked, bundled, external, or skipped
  • env vars set vs left for the user
  • whether veris env push succeeded and which tag was created

Then suggest the next commands:

  • veris scenarios create
  • veris simulations create

Phase 7: Smoke Validation

[Phase 7/7]

Tell the user: "I'm going to run a single scenario and simulation to verify the integration works end-to-end."

7.1 Create a smoke scenario

veris scenarios create --num 1

The goal is a single short interaction that exercises the agent's primary interface.

7.2 Run a single simulation

veris simulations create --scenario-set-id <id>

Wait for it to complete.

7.3 Check the results

Review the simulation for:

  1. Agent responded with real content — not an error page, empty body, or exception traceback
  2. Mock services were called — if the agent should call Slack, Salesforce, etc., confirm those calls appear
  3. No startup crashes — the agent process stayed alive for the duration
  4. Channel contract is correct — the actor's messages reached the agent and responses came back in the expected shape

7.4 Diagnose failures

If the smoke test fails:

  • Check agent container logs for startup errors or missing env vars
  • Verify actor.channels request/response mapping matches the actual API shape
  • Confirm mock service credentials and DNS aliases are correct
  • Return to the relevant phase to fix and re-push

7.5 Sign off

If the smoke test passes, summarize:

  • What the actor sent and what the agent responded
  • Which services were exercised
  • Confidence level that the integration is ready for full scenario generation

Then suggest full scenario generation (veris scenarios create --num N) and simulation as the next step.


Practical guidance

Prefer current conventions over stale scaffolding

If veris env create scaffolds old-looking placeholders, overwrite them with the current preferred shape from this skill.

Keep the skill honest about function channels

Use a function channel only when the agent already exposes a callable as part of its public interface. Do not force a networked product into a function callable just because it seems simpler, and never create a wrapper file to invent a callable the agent doesn't already have.

Keep the skill honest about one-shot agents

If the integrated agent is clearly one-shot/stateless, carry that through explicitly by setting actor.config.MAX_TURNS: 1.

Be explicit about what you did not automate

If login, secrets, or env-var values still require user action, say so plainly. The goal is to get as far as possible, not to hide blockers.

平台分布

Codex

33.02%
按下载量换算42

Claude

31.03%
按下载量换算40

Cursor

18.26%
按下载量换算23

Gemini CLI

8.31%
按下载量换算11

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills