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.
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.
git clone --depth 1 https://github.com/Arindam200/awesome-ai-appsWrote 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.
[](https://agentmods.dev/agents/arindam200/awesome-ai-apps/analyst_v3)<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>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.
| Model | Per session | Once 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 |
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. 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:
- Model: Use OpenAI GPT-4o exclusively via the OpenAI Python SDK. This model excels at code generation and complex SQL queries.
- CRITICAL - Monocle Instrumentation:
- You MUST read
boilerplate.pyfirst and copy its pattern exactly. - Correct import:
from monocle_apptrace import setup_monocle_telemetry(NOTfrom monocle_apptrace.api) - Correct OpenAI API: Use
client.chat.completions.create()(NOTclient.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()orhttpx— Monocle cannot capture those.
- You MUST read
- Infrastructure: Use the hosted Okahu MCP (
/okahu:...) for all trace analysis. Do not rely on local trace files or manual fetching skills. - Monocle Test Tools Validation:
- The
test_analyst.pyusesmonocle_test_toolswithMonocleValidatorto 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.pyto pass them.
- The
- 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. - Missing Package Handling:
- If you encounter a
ModuleNotFoundErroror import error, install the missing package only if it is in this approved list:monocle_apptracemonocle_test_toolsopenaifastapiuvicornpython-dotenvpytest
- 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.
- If you encounter a
- Strict Trace-Driven Debugging:
- If a test case fails, invoke the hosted Okahu MCP
/okahu:get_latest_traces:mcpand pass argworkflow_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.
- If a test case fails, invoke the hosted Okahu MCP
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.
- 7d ago First seen · 136 lines · 21 tokens per session scan A ec4c675e4d0c
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.
Other agents, from other repositories
Dynatrace Expert
The Dynatrace Expert Agent integrates observability and security capabilities directly into GitHub workflows, enabling development teams to investigate incidents, validate deployments, triage errors, detect performance regressions, validate releases, and manage security vulnerabilities by autonomously analysing…
New Relic Incident Response Agent
Identify and fix production issues by correlating New Relic observability data with code changes. Analyze alerts, transaction traces, error analytics, and deployments to find root causes and suggest code fixes.
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
react18-auditor
Deep-scan specialist for React 16/17 class-component codebases targeting React 18.3.1. Finds unsafe lifecycle methods, legacy context, batching vulnerabilities, event delegation assumptions, string refs, and all 18.3.1 deprecation surface. Reads everything, touches nothing. Saves .github/react18-audit.md.
Comet Opik
Unified Comet Opik agent for instrumenting LLM apps, managing prompts/projects, auditing prompts, and investigating traces/metrics via the latest Opik MCP server.
Kusto Assistant
Expert KQL assistant for live Azure Data Explorer analysis via Azure MCP server.