A workflow assistant for building an AI agent from initial customer discovery through deployment. It uses project files such as meeting notes, customer feedback, code, and usage data to produce PDF reports for the workflow stages.

Use it to process discovery transcripts, validation feedback, Python code, or deployment metrics for an AI-agent project and generate project reports.

It organizes scattered project inputs and turns them into documented outputs. This reduces the need to manually assemble discovery, review, MVP, and summary reports.

rapp — Command for Claude Code by microsoft/aibast-agents-library

About the project

AIBAST Agents Library is a collection of industry-focused AI agent templates accompanied by a local server that connects agents to GitHub Copilot for language-model inference. It helps developers create and run tool-using agents and isolated project environments, with an optional cloud-backed path for persistent memory. The catalogue entries provide the repository's agents, skills, commands, hooks, and instructions.

microsoft/aibast-agents-library · 8 stars · on GitHub

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/microsoft/aibast-agents-library

Made for: Claude Code.

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.

README.md

[![agentmods](https://agentmods.dev/badge/commands/microsoft/aibast-agents-library/rapp/github.svg)](https://agentmods.dev/commands/microsoft/aibast-agents-library/rapp)

Your own site

<a href="https://agentmods.dev/commands/microsoft/aibast-agents-library/rapp"><img src="https://agentmods.dev/badge/commands/microsoft/aibast-agents-library/rapp/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/commands/microsoft/aibast-agents-library/rapp"><img src="https://agentmods.dev/badge/commands/microsoft/aibast-agents-library/rapp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,153 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. 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.

Model	Per session	Once invoked
Fable 5.1	$0.00000	$0.01153
Opus 5.5	$0.00000	$0.00461
Sonnet 5.5	$0.00000	$0.00231
Haiku 4.5	$0.00000	$0.00115

Measured 2mo ago against content hash eb7e810137dd, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

rapp 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 2mo 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.

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.

community_rapp/project/.claude/commands/rapp.md · 112 lines

How it starts

The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.

RAPP Pipeline Assistant

You are now in RAPP Pipeline mode. Help the user build an AI agent from discovery to deployment using the unified RAPP agent.

RECOMMENDED: Automated Workflow

The easiest way to use RAPP is the auto_process action:

Create project folder in Azure File Storage:

rapp_projects/{project_id}/
    inputs/
    outputs/

Drop input files into the inputs/ folder:

File Type	Expected Names
Discovery transcript	`transcript.txt`, `discovery_call.txt`, `meeting_notes.txt`
Customer feedback	`customer_feedback.txt`, `validation.txt`, `approval.txt`
Code for review	`agent_code.py`, `*.py`
Deployment metrics	`metrics.json`, `telemetry.json`, `usage.json`

Call the RAPP agent with auto_process:

{
  "action": "auto_process",
  "project_id": "acme-inventory-2024",
  "customer_name": "Acme Corp",
  "project_name": "Inventory Optimizer"
}

Professional PDF reports are generated in outputs/:
- discovery_report_YYYYMMDD.pdf
- qg1_report_YYYYMMDD.pdf
- mvp_report_YYYYMMDD.pdf
- executive_summary_report_YYYYMMDD.pdf
- etc.

Quick Reference - Pipeline Steps & Actions

Step	Name	RAPP Agent Action	Gate
1	Discovery Call	`prepare_discovery_call`, `process_transcript`	-
2	Transcript Validation	`execute_quality_gate` (gate=QG1)	QG1
3	MVP Poke Document	`generate_mvp_poke`, `generate_full_mvp_document`	-
4	Customer Validation	`execute_quality_gate` (gate=QG2)	QG2 (SCOPE LOCK)
5	Generate Agent Code	`generate_agent_code`	-
6	Code Quality Review	`execute_quality_gate` (gate=QG3), `review_code`	QG3
7	Deploy Prototype	`generate_deployment_config`	-
8	Demo Review	`execute_quality_gate` (gate=QG4)	QG4
9	Create Video Demo	-	-
10	Final Demo Review	`execute_quality_gate` (gate=QG5)	QG5
11	Iteration Loop	-	-
12	Production Deploy	-	-
13	Post-Deploy Audit	`execute_quality_gate` (gate=QG6)	QG6
14	Scale & Maintain	-	-

Read the full file on GitHub · 112 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.

2mo ago First seen · 112 lines · 0 tokens per session scan A eb7e810137dd

Subscribe to this mod's changes

rapp is a command published in the GitHub repository microsoft/aibast-agents-library (8 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,153 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Building agents Planning

rapp next to 6 commands from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
rappmicrosoft/aibast-agents-library	Command	8	yesterday	MIT	A	0	Claude Code
checklistgithub/spec-kit	Command	148k	yesterday	MIT	A	13	Claude Code
clarifygithub/spec-kit	Command	148k	yesterday	MIT	A	27	Claude Code
specifygithub/spec-kit	Command	148k	yesterday	MIT	A	13	Claude Code
analyzegithub/spec-kit	Command	148k	yesterday	MIT	A	26	Claude Code
convergegithub/spec-kit	Command	148k	yesterday	MIT	A	35	Claude Code
implementgithub/spec-kit	Command	148k	yesterday	MIT	A	14	Claude Code