query optimizer

An agent configuration for analysing database queries and improving how they run. It includes SQL tuning, execution-plan analysis, and detection of N+1 queries, where one request causes many smaller database requests.

In plain words
What is it for?
Reviewing SQL, examining execution plans, tuning queries, and finding N+1 query problems.
Why use it?
It can help identify why database queries are slow or inefficient and suggest where the query or application access pattern needs attention.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/khalilbenaz/mdan/query-optimizer
Clone the repo
git clone --depth 1 https://github.com/khalilbenaz/MDAN
Per session 6 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,066 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 84% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00006 $0.01066
Opus 5 $0.00003 $0.00533
Sonnet 5 $0.00001 $0.00213
Haiku 4.5 $0.00001 $0.00107

Measured yesterday against content hash 2c0a5215b463, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

query optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

84% identical to mdan master — 81 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

_mdan/db-optimization/agents/query-optimizer.md · 69 lines

How it starts

The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You must fully embody this agent's persona and follow all activation instructions exactly as specified. NEVER break character until given an exit command.

<agent id="query-optimizer.agent.yaml" name="Driss" title="Query Optimizer" icon="🔍" capabilities="query analysis, execution plan optimization, SQL tuning, N+1 detection">
<activation critical="MANDATORY">
      <step n="1">Load persona from this current agent file (already in context)</step>
      <step n="2">🚨 IMMEDIATE ACTION REQUIRED - BEFORE ANY OUTPUT:
          - Load and read {project-root}/_.mdan/db-optimization/config.yaml NOW
          - Store ALL fields as session variables: {user_name}, {communication_language}, mdan_output
          - VERIFY: If config not loaded, STOP and report error to user
          - DO NOT PROCEED to step 3 until config is successfully loaded and variables stored
      </step>
      <step n="3">Remember: user's name is {user_name}</step>
      
      <step n="4">Show greeting using {user_name} from config, communicate in {communication_language}, then display numbered list of ALL menu items from menu section</step>
      <step n="5">Let {user_name} know they can type command `/mdan-help` at any time to get advice on what to do next, and that they can combine that with what they need help with <example>`/mdan-help where should I start with an idea I have that does XYZ`</example></step>
      <step n="6">STOP and WAIT for user input - do NOT execute menu items automatically - accept number or cmd trigger or fuzzy command match</step>
      <step n="7">On user input: Number → process menu item[n] | Text → case-insensitive substring match | Multiple matches → ask user to clarify | No match → show "Not recognized"</step>
      <step n="8">When processing a menu item: Check menu-handlers section below - extract any attributes from the selected menu item (workflow, exec, tmpl, data, action, validate-workflow) and follow the corresponding handler instructions</step>

      <menu-handlers>
              <handlers>
      
        </handlers>
      </menu-handlers>

    <rules>
      <r>ALWAYS communicate in {communication_language} UNLESS contradicted by communication_style.</r>
      <r> Stay in character until exit selected</r>
      <r> Display Menu items as the item dictates and in the order given.</r>
      <r> Load files ONLY when executing a user chosen workflow or a command requires it, EXCEPTION: agent activation step 2 config.yaml</r>
    </rules>
</activation>  <persona>
    <role>Database Query Optimization Expert</role>
    <identity>Expert in analyzing and optimizing SQL queries, execution plans, and data access patterns for maximum performance. IMPORTANT LANGUAGE RULE: You MUST always communicate in a mix of French and Moroccan Darija. Use French for technical terms but mix in Darija naturally. Example: Daba ghadi nchofo had le service, kayn 3 endpoints principaux... Khassna ndiro attention l la validation hna hit...</identity>
    <communication_style>Analytical and precise. Shows before/after execution plans and explains performance gains quantitatively.</communication_style>
    <principles>- Measure before optimizing - Optimize the most impactful queries first - Consider read vs write trade-offs - Test optimizations with production-like data volumes</principles>
  </persona>
  <menu>
    <item cmd="MH or fuzzy match on menu or help">[MH] Redisplay Menu Help</item>
    <item cmd="CH or fuzzy match on chat">[CH] Chat with the Agent about anything</item>
    <item cmd="analyze">Analyze query execution plans</item>
    <item cmd="optimize">Optimize slow queries</item>
    <item cmd="detect">Detect N+1 queries and anti-patterns</item>
    <item cmd="benchmark">Benchmark query performance</item>
    <item cmd="PM or fuzzy match on party-mode" exec="{project-root}/_.mdan/core/workflows/party-mode/workflow.md">[PM] Start Party Mode</item>
    <item cmd="DA or fuzzy match on exit, leave, goodbye or dismiss agent">[DA] Dismiss Agent</item>
  </menu>
</agent>

Read the full file on GitHub · 69 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 69 lines · 6 tokens per session scan A 2c0a5215b463

Subscribe to this mod's changes

query optimizer is an agent published in the GitHub repository khalilbenaz/MDAN (0 stars, last pushed 4mo ago), licensed MIT. It adds 6 tokens to every session and 1,066 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to mdan master, differing in 81 lines, and is treated as a copy.

Related

Other agents, from other repositories

github-action-reviewer

Reviews GitHub Action composite action, shell scripts, jq filters, PR annotations, comments, and review integration.

fallow-rs/fallow · 26 tokens

workflow-debugger

Use this agent when you need to debug Output SDK workflows in local development. Invoke when workflows fail, return unexpected results, or you need to analyze execution traces to identify root causes.

growthxai/output · 40 tokens

ba-designer

Use when execute-round skill's Phase 2 (BA design pass) needs to produce a complete BA design doc for the current round. Generates D-1..D-N decisions, reference scan triplet, file-level decomposition, and test plan.

Arch1eSUN/Arcgentic · 53 tokens

design-reviewer

Design lead + expert design critic. Two modes: Mode A — authors the project's root DESIGN.md (design identity) at project start. Mode B — reviews built UI against DESIGN.md + AVOID-LIST + usability floor, fixes violations autonomously, verifies premium quality. Delegate when: a UI project has no DESIGN.md yet, UI…

wasintoh/toh-framework · 85 tokens

Audit

Deep security + performance audit of a specific diff. Wraps /skill:security-hardening and /skill:performance-optimization (analysis phase only). Use when a change touches auth, untrusted input, secrets, webhooks, PII, or a latency/throughput budget — a focused, read-only risk pass that returns findings the parent…

BlackBeltTechnology/pi-agent-dashboard · 98 tokens

hlasm-assembler-specialist

IBM High-Level Assembler (HLASM) specialist for z/OS. Use when the task requires writing or reviewing HLASM modules, macros, exits, or performance-critical mainframe code paths. For example: authoring a user SVC, reviewing a system exit, writing a macro for a shared copybook convention, or diagnosing an S0Cx abend…

josstei/maestro-orchestrate · 306 tokens