DevTool Answer Monitor

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 Monitor is the companion skill for the devtool-answer-monitor repo and covers query pool design, four-metric monitoring, model-specific content placement, content checks, negative-answer repair, activation analysis, and T+7 or T+14 regression validation.

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you want an agent to help you monitor how LLMs describe your product, build a reusable query pool, diagnose negative or outdated answers, and plan what to fix next.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• Find negative or outdated LLM claims about my project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

• Zero-install demo: sample-run viewer
• Public benchmarks: MinerU story and Sciverse API story
• Sample outputs: leaderboard snapshot and repair trend snapshot

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.