junai Pipeline MCP

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Agentic pipeline orchestration MCP server for AI-driven development workflows.

Installation

Ajoutez ceci à la configuration MCP de votre client (Claude Desktop : claude_desktop_config.json ; Cursor : .cursor/mcp.json ; VS Code et les autres utilisent la même forme « mcpServers »).

PYPI package junai-mcp
Claude Code
claude mcp add junai-pipeline-mcp -e 'PIPELINE_STATE_PATH=<PIPELINE_STATE_PATH>' -e 'JUNAI_WORKSPACE_ROOT=<JUNAI_WORKSPACE_ROOT>' -- uvx junai-mcp==0.1.1
Configuration JSON (Claude Desktop, Cursor, VS Code)
{
    "mcpServers": {
        "junai-pipeline-mcp": {
            "command": "uvx",
            "args": [
                "junai-mcp==0.1.1"
            ],
            "env": {
                "PIPELINE_STATE_PATH": "<PIPELINE_STATE_PATH>",
                "JUNAI_WORKSPACE_ROOT": "<JUNAI_WORKSPACE_ROOT>"
            }
        }
    }
}

Variables d'environnement

  • PIPELINE_STATE_PATH Path to pipeline-state.json. Defaults to .github/pipeline-state.json relative to the workspace root (detected by searching upward from the current directory for a .github folder).
  • JUNAI_WORKSPACE_ROOT Explicit workspace root path override. Use if automatic detection (searching upward for .github) does not find the correct root.

Documentation

Tirée du README du projet sur GitHub, actualisée chaque semaine. Les liens ouvrent GitHub.

Sommaire
  1. caddis — a Claude Code harness
  2. Before you start
  3. Your first five minutes
  4. Install (Claude Code)
  5. What you get
  6. Installing outside Claude Code (Codex, Antigravity, …)
  7. Repo layout
  8. A note on names
  9. License

caddis — a Claude Code harness

Browse every command, skill, agent and hook -> saajunaid.github.io/caddis-plugin — the reference site, rebuilt on every publish.

caddis is a Claude Code harness — a plugin (plus a shared pool of skills, subagents, slash-commands, and hooks) that makes a single Claude Code session dramatically more capable and much harder to derail. It's agent-agnostic: the same pool is exported to other AI CLIs (Codex, Antigravity, Copilot), and CLAUDE.md↔AGENTS.md are mirrors so every agent reads the same conventions.

Before you start

You needWhy
Claude Code, installed and signed incaddis is a plugin for it
Python 3.11+ on PATHthe hooks and the cross-review tool are Python
Gitseveral commands read repository state
(optional) an API key for DeepSeek or GLMonly for cross-vendor review and the OSS lanes

Honest about the platform: caddis is developed and used daily on Windows with PowerShell. The hooks are pure Python and cross-platform, and the skills, commands and agents are plain markdown that work anywhere — but some launchers ship as both .ps1 and .sh, the deployment skill is Windows-specific, and the publishing machinery (sync.ps1) is PowerShell-only. Nothing here is hostile to macOS or Linux; it is simply less travelled, so expect rough edges and please report them.

Your first five minutes

claude plugin marketplace add saajunaid/caddis-plugin
claude plugin install caddis@caddis

Then, in a Claude Code session, confirm it actually loaded:

/caddis:version

If that prints a version, you are installed. From there, the shortest path to seeing what caddis is for:

Try thisWhat it does
/caddis:feature-plan add CSV exportwrites a phased, TDD-structured plan to .caddis/plans/ — the durable spine the rest of the harness reads
/caddis:implement <plan>executes that plan phase by phase, committing each, and stops only at a real boundary
/caddis:cross-reviewhas a different vendor's model review your diff, because a same-vendor reviewer shares your blind spots
/caddis:handoffwrites the resume doc, so the next session starts with zero re-discovery

Everything else is optional. If you only ever use feature-plan → implement → cross-review, you have most of the value.

This repository is the published marketplace mirror: it hosts the caddis and caddis-extras Claude Code plugins, the portable per-harness bundles under bundles/, and the shared pool. It is generated and pushed by the caddis authoring repo's exporter — issues are welcome here, but code changes land upstream and are synced in.


Install (Claude Code)

# 1. Register this repo as a plugin marketplace (once)
claude plugin marketplace add saajunaid/caddis-plugin

# 2. Install the core plugin (always-on, lean context)
claude plugin install caddis@caddis

# 3. (optional) Add the long-tail skill library — cloud/data/media/etc. Disabled by default,
#    so it costs zero always-on context until you enable it.
claude plugin install caddis-extras@caddis

Then, in any repo:

/caddis:setup-project-ai     # deploy the harness into the project

A normal working loop looks like:

/feature-plan      # (or /prd first) → writes .caddis/plans/<slug>.md, the durable spine
/implement         # executes the plan phase-by-phase, TDD, commit per phase
/ship              # express lane: commit → push → CI (hotfixes); feature work: /ship-pr then /ship-merge
/handoff           # ALWAYS end a session with this — writes the resume doc

The one habit that matters most: end every session with /handoff. It writes .caddis/relay.md, which is re-injected automatically at the next session start — you resume with zero re-discovery instead of a cold, forgetful session.

See USERGUIDE.md for the full command/skill/hook reference and the mental models.


What you get

  • Slash-commands — /feature-plan, /prd, /implement, /tdd, /ship · /ship-pr · /ship-merge, /handoff, /kb, /digress · /resume, /cross-review, /usage-review, /mermaid-db · /excalidraw-db, /setup-project-ai, /migrate-dir.
  • Subagents — lean, own-context helpers: anchor (evidence-first verification), code-reviewer, preflight (plan-vs-codebase validation), tester, debug, codebase-audit, security-analyst, data-engineer, sql-expert, knowledge-transfer.
  • A four-layer memory model — session relay (relay.md), automatic Dream Memory (memory.jsonl), a per-repo knowledge base (.caddis/kb/ + DOC-MAP.md), and cross-repo memory.
  • Hooks & safety — a PreToolUse guard for secret/destructive writes, auto-lint on edit, and the SessionStart/Stop hooks behind relay + memory.
  • Tiered skills — the core dev set ships in caddis; the long tail (cloud, data, media, productivity) lives in caddis-extras, off by default.

Installing outside Claude Code (Codex, Antigravity, …)

The knowledge layer (skills + AGENTS.md conventions) works in any harness. Per-harness bundles are published under bundles/ and installed with caddis-init:

# from a checkout of this repo (the installer ships in plugin/scripts/)
python plugin/scripts/claudster_init.py --target codex        --dest /path/to/project
python plugin/scripts/claudster_init.py --target antigravity  --dest /path/to/project

Safe by design: a sha256 manifest tracks what the installer wrote; re-runs update only unmodified files, and anything you edited locally is reported as a conflict, never overwritten.


Repo layout

PathWhat it is
.claude-plugin/marketplace.jsonThe marketplace manifest (caddis + caddis-extras)
plugin/The core caddis Claude Code plugin (agents, commands, hooks, core skills)
plugin-extras/The caddis-extras skill library (disabled by default)
bundles/Portable per-harness exports (codex, antigravity) for caddis-init
.github/The shared pool: skills, agents, prompts, instructions, tools
sync.ps1, export_runtime_resources.py, validate_pool.pyThe build/sync machinery

A note on names

caddis was previously published as claudster (and, before that, this repo hosted the junai Copilot pipeline — see the pre-1.2 entries in CHANGELOG.md). Repos set up under the old name have a .claudster/ artifact dir; everything reads both .caddis/ and .claudster/ and writes where the repo already lives, so nothing breaks. Run /caddis:migrate-dir when you want a repo converted. The ~/.claudster/ user scope (keys, install records) is permanent.


License

MIT. The published plugin bundles (caddis / caddis-extras) carry the same license in their manifests.

Questions fréquentes

Qu'est-ce que junai Pipeline MCP ?

Agentic pipeline orchestration MCP server for AI-driven development workflows.

Comment installer junai Pipeline MCP ?

Il s'exécute sur votre machine. Dans Claude Code : claude mcp add junai-pipeline-mcp -e 'PIPELINE_STATE_PATH=<PIPELINE_STATE_PATH>' -e 'JUNAI_WORKSPACE_ROOT=<JUNAI_WORKSPACE_ROOT>' -- uvx junai-mcp==0.1.1. Pour les autres clients, collez la configuration JSON de cette page dans leurs réglages MCP.

Faut-il une clé API pour junai Pipeline MCP ?

Son entrée au registre n'indique aucune clé ni secret obligatoire. Certaines fonctions peuvent tout de même demander un compte auprès du service auquel il se connecte.

junai Pipeline MCP est-il open source ?

Oui. Son code est sur GitHub sous licence MIT.

Qui maintient junai Pipeline MCP ?

Il est publié par saajunaid. AI.tn le référence sans le maintenir ni le recommander.

Issu du registre MCP officiel. Être listé n'est pas une recommandation : vérifiez le code et les permissions d'un serveur avant de lui donner accès à vos données.