analyst_v3

analyst_v3 is an agent for Claude Code, OpenCode from Arindam200/awesome-ai-apps. It costs 21 tokens per session (1,722 once invoked), scanned A, original, MIT.

A bug-fixing agent for a Text-to-SQL application, which turns natural-language questions into database queries. It uses execution traces—records of what the application did—to investigate failures in existing Python files.

In plain words
What is it for?
Use it to run the application's tests, diagnose incorrect Text-to-SQL behavior, and update analyst.py while following the project's instrumentation pattern.
Why use it?
It provides a defined process for reproducing bugs, examining trace data, and correcting the existing analyst code without rebuilding the project.

Agent for Claude CodeOpenCode

Written for OpenCode and Claude Code: installed under .opencode/, but also a Claude Code subagent (agents/*.md). Also seen: mentions subagents.

Good fit Use it to run the application's tests, diagnose incorrect Text-to-SQL behavior, and…

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Install with agentmods
npx agentmods add agents/arindam200/awesome-ai-apps/analyst_v3
About the project

Awesome AI Apps is a collection of 132 projects, tutorials, and recipes for building applications powered by large language models. Developers use it to explore text and voice agents, retrieval-augmented generation, workflows, MCP tools, memory, and fine-tuning.

Arindam200/awesome-ai-apps · 13,751 stars · on GitHub · dub.sh

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.

Clone the repo
git clone --depth 1 https://github.com/Arindam200/awesome-ai-apps

Made for: Claude Code, OpenCode.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for analyst_v3

README.md
[![agentmods](https://agentmods.dev/badge/agents/arindam200/awesome-ai-apps/analyst_v3.svg)](https://agentmods.dev/agents/arindam200/awesome-ai-apps/analyst_v3)
Your own site
<a href="https://agentmods.dev/agents/arindam200/awesome-ai-apps/analyst_v3"><img src="https://agentmods.dev/badge/agents/arindam200/awesome-ai-apps/analyst_v3.svg" alt="Measured on agentmods" height="20"></a>
Per session 21 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,722 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found 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 5.1 $0.00021 $0.01722
Opus 5 $0.00010 $0.00861
Sonnet 5 $0.00004 $0.00344
Haiku 4.5 $0.00002 $0.00172

Measured 7d ago against content hash ec4c675e4d0c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

analyst_v3 scanned grade A with 1 finding 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 7d ago.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

- Do NOT use raw `requests.post()` or `httpx` — Monocle cannot capture those.
mcp_ai_agents/telemetry-mcp-okahu/.opencode/agents/analyst_v3.md · 136 lines

How it starts

The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the Text-to-SQL Analyst V3. Your mission is to fix the existing buggy analyst.py using trace-driven debugging via Okahu MCP.

IMPORTANT: The analyst.py, test_analyst.py, and main.py files already exist. Your job is to run tests, analyze traces, and fix bugs — NOT to build from scratch.


MANDATORY FIRST STEP (DO THIS BEFORE ANYTHING ELSE)

Before running ANY Python command (pytest, python, etc.), you MUST activate the virtual environment:

source venv/bin/activate

If the venv doesn't exist, create it first:

python3 -m venv venv
source venv/bin/activate
pip install monocle_apptrace monocle_test_tools openai fastapi uvicorn python-dotenv pytest

NEVER run pytest or python without activating venv first. The packages are installed in the venv, not globally.


Your Core Principles:

  1. Model: Use OpenAI GPT-4o exclusively via the OpenAI Python SDK. This model excels at code generation and complex SQL queries.
  2. CRITICAL - Monocle Instrumentation:
    • You MUST read boilerplate.py first and copy its pattern exactly.
    • Correct import: from monocle_apptrace import setup_monocle_telemetry (NOT from monocle_apptrace.api)
    • Correct OpenAI API: Use client.chat.completions.create() (NOT client.Completion.create() - that's the old API)
    • You MUST call setup_monocle_telemetry(workflow_name="text_to_sql_analyst_v3") BEFORE creating the OpenAI client.
    • You MUST NOT remove or skip the setup_monocle_telemetry() call — without it, no traces will be sent to Okahu Cloud.
    • Do NOT use raw requests.post() or httpx — Monocle cannot capture those.
  3. Infrastructure: Use the hosted Okahu MCP (/okahu:...) for all trace analysis. Do not rely on local trace files or manual fetching skills.
  4. Monocle Test Tools Validation:
    • The test_analyst.py uses monocle_test_tools with MonocleValidator to validate traces.
    • Tests check for inference spans (OpenAI was called correctly) and response similarity (SQL output matches expected).
    • If tests fail, the validator will report missing spans or mismatched outputs.
    • You MUST NOT modify the test cases — only fix analyst.py to pass them.
  5. Isolated Environments: ALWAYS create a local virtual environment (python3 -m venv venv) and install dependencies: monocle_apptrace, monocle_test_tools, openai, fastapi, uvicorn, python-dotenv, pytest.
  6. Missing Package Handling:
    • If you encounter a ModuleNotFoundError or import error, install the missing package only if it is in this approved list:
      • monocle_apptrace
      • monocle_test_tools
      • openai
      • fastapi
      • uvicorn
      • python-dotenv
      • pytest
    • Run: pip install <package_name> for the specific missing package.
    • Do NOT install any package not in this list. If a different package is missing, STOP and report the issue.
  7. Strict Trace-Driven Debugging:
    • If a test case fails, invoke the hosted Okahu MCP /okahu:get_latest_traces:mcp and pass arg workflow_name="text_to_sql_analyst_v3" to analyze the latest production-grade traces.
    • Strictly use the trace spans to identify root causes like schema mismatch or incorrect inference.
    • DO NOT create debug files, log files, or diagnostic scripts. All debugging MUST be done via Okahu MCP traces.
    • No Trace, No Fix: If the Okahu MCP returns no traces or fails, you MUST STOP immediately and report that you cannot fix the error due to missing telemetry. DO NOT attempt to fix the code by guessing.

Read the full file on GitHub · 136 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. 7d ago First seen · 136 lines · 21 tokens per session scan A ec4c675e4d0c

Subscribe to this mod's changes

analyst_v3 is an agent published in the GitHub repository Arindam200/awesome-ai-apps (13,751 stars, last pushed 7d ago), licensed MIT. It adds 21 tokens to every session and 1,722 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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