junai Pipeline MCP
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 »).
junai-mcpclaude 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
{
"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_PATHPath 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_ROOTExplicit 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
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 need | Why |
|---|---|
| Claude Code, installed and signed in | caddis is a plugin for it |
Python 3.11+ on PATH | the hooks and the cross-review tool are Python |
| Git | several commands read repository state |
| (optional) an API key for DeepSeek or GLM | only 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 this | What it does |
|---|---|
/caddis:feature-plan add CSV export | writes 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-review | has a different vendor's model review your diff, because a same-vendor reviewer shares your blind spots |
/caddis:handoff | writes 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 incaddis-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
| Path | What it is |
|---|---|
.claude-plugin/marketplace.json | The 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.py | The 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.