By satishc2437
Multi-agent product management team that gathers intent, produces feature specs, gates them through a reviewer, opens a spec PR, and on approval seeds the Azure DevOps or GitHub Kanban board with work items for handoff to dev-team.
Run a board operation (create, update, or query) directly against the project's ADO or GitHub Kanban board, independent of the PM flow. Platform auto-detected from the git remote.
Drive a product idea from intent through interview, parallel feature specs, reviewer gate, spec PR, and (after your approval) work-item creation on the ADO or GitHub Kanban board. Three modes — fresh, feedback, approved.
Use this agent for any work-item operation on Azure DevOps or GitHub — creating WIs from approved specs (called by the pm-team skill), or freeform create/update/query requests (called directly via /board-manager). The agent auto-detects the platform from the git remote.
Use this agent when the pm-team skill delegates a single feature spec to write or revise. The agent produces docs/specs/<initiative-slug>/<feature-slug>.md with user stories and testable acceptance criteria.
Use this agent when the pm-team skill needs a go/no-go gate on a set of feature specs before opening (or pushing to) a spec PR. The agent reads each spec, validates completeness and testability, and returns a structured verdict.
Uses power tools
Uses Bash, Write, or Edit tools
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A curated collection of self-contained Model Context Protocol (MCP) tools/servers. Each tool lives in its own folder, is its own Python project, and can be used independently.
requires-python).mcp-tools/
├── agent-memory/
├── pdf-reader/
└── xlsx-reader/
Open this repository in the dev container to get a consistent environment. See .devcontainer/README.md for details.
From the repository root:
uv run pdf-reader
uv run xlsx-reader
uv run agent-memory
Run all tests:
uv sync --dev --all-packages
uv run pytest
Run a specific tool’s tests (example):
uv run pytest mcp-tools/pdf-reader -v
Run docstring lint (pydocstyle D* rules via ruff, Google convention) over the
repo’s in-scope sources:
uv run ruff check --select D mcp-tools/*/src
Notes:
mcp-tools/*/src/**.main.py exists, include it in the command.uvx (from GitHub)Each tool’s README provides a copy/paste snippet intended for MCP client
configuration that runs the tool via uvx directly from GitHub.
General pattern (placeholders):
uvx --from "git+https://github.com/<owner>/<repo>.git@<ref>#subdirectory=mcp-tools/<tool-folder>" \
python -m <tool_module>
Notes:
<ref> with a tag/commit for reproducibility.mcp-tools/<tool-name>/ with its own pyproject.toml.mcp-tools/<tool-name>/README.md, including the uvx snippet.Project development is governed by the constitution in docs/Constitution.md.
uv sync in a WorkspaceThis repo is a uv workspace (multiple independent Python projects under mcp-tools/).
If you want to run the root test suite (which imports all tools), use:
uv sync --dev --all-packages
uv run pytest -q
If you run uv sync at the repo root without --all-packages, uv may remove dependencies that belong to individual tools (because it is syncing the environment to just the root project). That can lead to import errors like missing openpyxl, mcp, pypdf, or tool packages such as pdf_reader.
For per-tool work, cd mcp-tools/<tool> and run:
uv sync --dev
uv run pytest
npx claudepluginhub satishc-dev/maruti --plugin pm-teamDesigns and generates new custom subagents, skills, slash commands, chat modes, and prompt files for Claude Code and GitHub Copilot.
Multi-agent software development team that drives a work item from Azure DevOps or GitHub through design, parallel implementation, code review, and PR creation.
Designs decision-ready MCP tool architectures and emits durable spec/guardrails/success-criteria docs.
Access thousands of AI prompts and skills directly in your AI coding assistant. Search prompts, discover skills, save your own, and improve prompts with AI.
Complete developer toolkit for Claude Code
Intelligent draw.io diagramming plugin with AI-powered diagram generation, multi-platform embedding (GitHub, Confluence, Azure DevOps, Notion, Teams, Harness), conditional formatting, live data binding, and MCP server integration for programmatic diagram creation and management.
Feature development with code-architect/explorer/reviewer agents, CLAUDE.md audit and session learnings, and Agent Skills creation with eval benchmarking from Anthropic.
Orchestrate multi-agent teams for parallel code review, hypothesis-driven debugging, and coordinated feature development using Claude Code's Agent Teams
Production-grade engineering skills for AI coding agents — covering the full software development lifecycle from spec to ship.