Hire an AI Qdrant performance reviewer

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• Performance baseline and candidate fixes
• Capacity plan
• Monitoring checks

How it does the job well

• Read the existing code, its conventions and its tests before changing anything. Match the project's patterns.
• Make the smallest change that fully solves the request. Keep unrelated refactors out.
• Reproduce a bug before fixing it, and add a test that fails without the fix.
• Treat input from users, files and networks as untrusted. Validate it at the boundary.

Mistakes it avoids

• Claiming code works without running it or its tests.
• Inventing APIs, package versions or settings instead of checking the installed ones.
• Changing behaviour outside the request, such as reformatting whole files or renaming public interfaces.

What a good result looks like

An example, not your task: Your p95 or p99 search time has risen, or you hit a throughput limit.

• Performance baseline and candidate fixes. Likely causes, most likely first, with the setting to change, a before and after test and the risk.
• Capacity plan. The bottleneck, a labelled memory estimate and when more nodes are needed.
• Monitoring checks. Metrics to collect, health checks and alerts that show slow or unhealthy search.

How the work is checked

• The changed files build, and the relevant tests and linters pass. The commands and results are shown.
• Each change is explained in one line with its reason, and risky areas are named.
• Any check that could not run is reported as NOT_RUN with what it needs.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• Qdrant search speed tuner. Share your search workload. Get index, query and hardware changes to test against a clear baseline.
• Qdrant cluster capacity planner. Share your data size and traffic. Get a scaling plan and the evidence needed before adding nodes.
• Qdrant health monitoring planner. Share your Qdrant setup. Get useful health metrics, alerts and checks for slow or unhealthy search.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id diagnose-slow-qdrant-search only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/diagnose-slow-qdrant-search/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• Your Qdrant version, point count, vector size and collection settings
• Query traces with their latency (without them you get likely causes only)
• The machine's RAM, CPU and disk, plus your data size and traffic

Tools and access

• Claude or ChatGPT with web access: They can open the linked Qdrant docs for exact setting names.
• Grafana or Prometheus: They give you the starting numbers that make each test fair.
• Claude or ChatGPT with web access: They can open the linked Qdrant docs for sizing rules.
• Grafana or Prometheus: They show the memory, CPU and disk numbers that name the bottleneck.
• Prometheus and Grafana: They collect and chart Qdrant metrics, and Qdrant publishes example setups for both.
• Claude or ChatGPT with web access: They can open the Qdrant docs the skill links to.

Functions and deliverables

• Qdrant performance reviewer: Review slow Qdrant search, compare candidate fixes and plan capacity and health checks against a clear baseline.
  • Performance baseline and candidate fixes
  • Capacity plan
  • Monitoring checks
• Qdrant search speed tuner: Get a ranked list of Qdrant settings to change for faster search, faster uploads or lower memory, each with a test to run first.
  • A short list of likely causes, most likely first
  • Settings to change, each with why and what it might cost
  • A test for each change, compared with your starting numbers
  • A list of changes to avoid for your case
  Source: GitHub Copilot community.
• Qdrant cluster capacity planner: Get a Qdrant scaling plan: how much RAM or how many nodes you need, in what order to add them, and what to measure first.
  • The bottleneck, named, with the numbers that show it
  • A scaling order, such as a bigger machine first and more machines later
  • A rough memory estimate from your data size, marked as an estimate
  • Node and copy counts to start with, if more machines are needed
  • The first test to run and what result to look for
  Source: GitHub Copilot community.
• Qdrant health monitoring planner: Get a Qdrant monitoring plan: what to watch, which alerts to set, and how to find why search is slow or memory keeps growing.
  • A list of the metrics to collect and where to find them
  • Health checks for your setup
  • A short list of alerts to set, with the reason for each
  • A log plan, if you need central or audit logs
  • For a live problem, the likely cause, the number that proves it and what not to touch
  Source: GitHub Copilot community.
• AI project context organiser: Share your project and next task. Get a short rules file, a focused file list and a clear handoff for later sessions.
  • A ready rules file for your project
  • A list of which files to load for each task
  • Rules for when to reset or trim the session
  • A checklist for spotting drifting answers
  Source: Addy Osmani.

Tasks you can use it for

• Your p95 or p99 search time has risen, or you hit a throughput limit.
• Qdrant is running short of memory and you need to know whether to tune or add nodes.

Limits to check before starting

• Search is fast but returns the wrong results: use the Qdrant search relevance investigator.
• You are choosing where to host Qdrant: use the Qdrant hosting option adviser.
• You are moving to a new embedding model or a new Qdrant version: use the Qdrant embedding migration planner or the Qdrant version upgrade planner.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Add the complete skill pack to your AI.
• Share your goal and the files listed above.
• Ask it to follow the steps and mark any missing facts.
• Review the result before approving external actions.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/diagnose-slow-qdrant-search for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.