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DevTool Answer Monitor

作者 veeicwgy · GitHub ↗ · v0.3.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install devtool-answer-monitor
功能描述
Use when the user wants to monitor how ChatGPT, Claude, Gemini, and other LLMs describe a developer tool, API, SDK, or open-source project. DevTool Answer Mo...
使用说明 (SKILL.md)

Monitor What LLMs Say Before Users Choose Your Dev Tool

Use this skill as the main visibility workflow router for developer tools and open-source products.

Brand: DevTool Answer Monitor

Companion repo: devtool-answer-monitor

Use this when you want an agent to help you monitor how LLMs describe your product, build a reusable query pool, diagnose negative or outdated answers, and plan what to fix next.

Safety First

  • Treat this root skill as a read-only workflow router.
  • Default to quickstart replay or manual paste mode when you only need examples or scoring help.
  • Do not ask users to paste API keys into chat. If API collection mode is needed, tell them to configure local environment variables themselves and then hand off execution to visibility-monitor.
  • Review local scripts such as install.sh, quickstart.sh, and the selected runner before executing shell commands.

Start Here

Copy one of these prompts to begin:

  • Analyze how ChatGPT and Claude describe my API docs
  • Build a developer-tool answer monitoring query pool for my SDK
  • Find negative or outdated LLM claims about my project

30-Second Result

Typical input

  • product truth such as a README, docs, changelog, integrations, or positioning page
  • answer evidence such as copied model answers, screenshots, or cited URLs
  • scope such as target models, languages, regions, or a repeated query set

What this skill returns

  • a reusable query pool
  • raw evidence and a score draft plan
  • a monitoring summary and report outline
  • a repair backlog with T+7 or T+14 validation points

Companion demo and sample outputs

Trigger

Use this skill when the task is any of the following:

  1. generate a visibility query matrix and Query Pool from product truth;
  2. monitor how multiple LLMs mention, recommend, or misunderstand a product;
  3. plan model-specific content placement based on datasource patterns;
  4. check whether a draft page, FAQ, changelog, or case study is ready to influence model answers;
  5. repair wrong, negative, outdated, or competitor-only answers;
  6. verify whether a repair action improved metrics at T+7 or T+14;
  7. help a user choose between quickstart replay, manual paste mode, and API collection mode.

Beginner Routing

When the user is new to the repository, route them in this order.

Situation Next step
Needs environment check first open docs/getting-started.md and review the environment check section
Wants environment-free first run open docs/index.html or docs/for-beginners.md
Wants a short explanation first open docs/for-beginners.md
Wants deeper onboarding open docs/getting-started.md
Wants the English repository overview open README.md
Wants the Chinese repository overview open README.zh-CN.md

Visibility Strategy

Always keep the workflow in this order:

Stage Goal
Query design turn product truth into scenario matrix, three-layer keywords, and Query Pool seeds
Monitoring score mention, positive mention, capability accuracy, and ecosystem accuracy
Placement map each target model to likely datasource channels and publication surfaces
Repair classify bad answers into information error, negative evaluation, outdated information, or competitor insertion
Activation analyze whether answers help a user install, integrate, or invoke the product
Regression compare follow-up runs and check whether metrics improved after action

Mode Selection

Choose the execution mode before running monitoring.

Mode Use when Typical inputs
Quickstart replay user wants the fastest first run without API setup sample model config + sample manual responses
Manual paste mode user already has copied answers from chat tools Query Pool + manual response JSON
API collection mode user wants repeatable real monitoring Query Pool + model config + locally configured provider env vars

Input Contract

Prepare as many of the following as possible before execution.

Input Examples
Product truth README, docs, changelog, integrations, positioning
Answer evidence raw answers, screenshots, copied responses, cited links
Monitoring scope models, languages, regions, dates, repeated query set
Publishing targets docs, blog, GitHub, Q&A, partner channels

Workflow Router

Choose the next sub-skill according to the user's immediate need.

Situation Next Skill
Need query design and scenario clustering visibility-query-matrix
Need weekly monitoring, evidence logging, report output, or shell execution after explicit user approval visibility-monitor
Need pre-publish content QA visibility-content-check
Need to repair bad answers and define regression checks visibility-repair

Required Reading Order

For a full program, read these repository documents in sequence:

  1. playbooks/visibility-workflow-architecture.md
  2. playbooks/keyword-strategy.md
  3. playbooks/monitoring-system.md
  4. playbooks/model-datasources.md
  5. playbooks/content-platform-map.md
  6. playbooks/negative-fix-sop.md

Output Contract

Always preserve the following outputs.

Output Description
Query foundation scenario matrix, keyword layers, Query Pool
Monitoring outputs raw evidence, score draft, summary, report, leaderboard or overview
Action plan content placement priorities and repair backlog
Regression record T+7 and T+14 comparisons after key fixes

Positioning

DevTool Answer Monitor is the skill layer for the devtool-answer-monitor repo.

  • Use the repo when you want runnable demos, scripts, and report artifacts.
  • Use the skill when you want an agent-guided workflow for monitoring, repair, and regression planning.

Handoff Rules

At the end of each run, preserve:

  1. which product was optimized;
  2. which models and languages were in scope;
  3. which queries are reused in weekly tracking;
  4. what the top three visibility weaknesses are;
  5. what actions are already completed and what still needs validation.
安全使用建议
This repository-backed skill appears to do what it says (monitor and score LLM answers). Before installing or running anything: (1) Prefer the zero-install demo (docs/index.html) to inspect outputs without running code. (2) Do NOT paste API keys into chat; if you need API-collection mode, set OPENAI_API_KEY and OPENAI_BASE_URL as local environment variables on a machine you control. (3) Inspect install.sh, quickstart.sh, run_monitor.py and run_chat_completions.py to understand what network calls and packages will be executed/installed. (4) Note the manifest vs SKILL.md mismatch: registry metadata lists OPENAI_API_KEY/OPENAI_BASE_URL as required while SKILL.md marks them optional — that can cause the agent to request secrets unnecessarily. (5) Run installs in an isolated environment (container or VM) if you plan to execute scripts, and restrict network access if you are unsure. If you want, paste any of the specific scripts here and I can review them for network calls or suspicious behavior before you run them.
功能分析
Type: OpenClaw Skill Name: devtool-answer-monitor Version: 0.3.0 The devtool-answer-monitor skill bundle is a legitimate toolkit designed to monitor and analyze how various LLMs describe developer tools and APIs. It includes Python scripts (e.g., run_chat_completions.py, run_monitor.py) for querying OpenAI-compatible endpoints, processing responses, and generating structured reports and visualizations using matplotlib. The SKILL.md instructions provide a clear workflow for an AI agent to assist users with visibility monitoring while explicitly including safety guidelines to prevent the mishandling of sensitive API keys. No evidence of data exfiltration, malicious execution, or unauthorized persistence was found; the code and documentation are consistent with the stated purpose of the tool.
能力标签
requires-sensitive-credentials
能力评估
Purpose & Capability
The skill is clearly for collecting and analyzing LLM answers about developer tools; requiring python3, bash, and an OpenAI-compatible API key/gateway is appropriate for the API-collection mode. However, registry metadata lists OPENAI_API_KEY and OPENAI_BASE_URL as required while the SKILL.md marks them optional (API collection mode only). That mismatch is inconsistent and may cause the agent to ask for secrets even when only read-only demo/manual modes are needed.
Instruction Scope
SKILL.md positions the root skill as a read-only workflow router (allowed-tools: Read) and explicitly recommends quickstart (zero-API) or manual-paste modes and warns not to paste API keys into chat. It also tells users to review local scripts before executing them. The instructions stay within the stated purpose, but they point to executable scripts (install.sh, quickstart.sh, run_monitor.py, run_chat_completions.py) that, if run, will access network/APIs — which is expected but requires user attention.
Install Mechanism
There is no automated install spec in the skill metadata (instruction-only), which lowers automated install risk. The repo nevertheless includes install.sh, quickstart.sh, and many runner scripts that create virtualenvs and install dependencies. These are from the public repo and hosted artifacts (GitHub/jsDelivr) — not an arbitrary remote download — but if you run them they will write to disk and install packages. Review install.sh and requirements.txt before running.
Credentials
Requesting an OpenAI-compatible API key (OPENAI_API_KEY) and an OpenAI gateway URL (OPENAI_BASE_URL) is proportionate for API collection mode. The concern is a metadata/instruction mismatch: registry-level required env vars list both as required, while SKILL.md marks them optional and says API key is only needed for API collection mode. That discrepancy could lead the agent to prompt for secrets when they are not necessary for quickstart/manual modes. PrimaryEnv is set to OPENAI_API_KEY which increases the chance an agent will treat that secret as central.
Persistence & Privilege
always:false and user-invocable:true. The skill does not request permanent/always-on inclusion and does not modify other skills' configurations. Autonomous invocation is allowed (default) but that is platform normal; there is no unusual privilege requested by the manifest.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install devtool-answer-monitor
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /devtool-answer-monitor 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.3.0
Rename the project to DevTool Answer Monitor, add a zero-install demo viewer, and publish public benchmark stories.
元数据
Slug devtool-answer-monitor
版本 0.3.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

DevTool Answer Monitor 是什么?

Use when the user wants to monitor how ChatGPT, Claude, Gemini, and other LLMs describe a developer tool, API, SDK, or open-source project. DevTool Answer Mo... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 982 次。

如何安装 DevTool Answer Monitor?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install devtool-answer-monitor」即可一键安装,无需额外配置。

DevTool Answer Monitor 是免费的吗?

是的,DevTool Answer Monitor 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

DevTool Answer Monitor 支持哪些平台?

DevTool Answer Monitor 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 DevTool Answer Monitor?

由 veeicwgy(@veeicwgy)开发并维护,当前版本 v0.3.0。

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