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Cdd Dev

Community
Oscaner
cdd-dev

Independent cdd-dev orchestrator -- Node-anchored flow with digraph as single control-flow source of truth. Determines base via the engine artifact, dispatches the CDD implement / review / fix chain following the engine's next face, and runs the branch final review — the engine's next directive carries the pipeline continuation. Callable standalone; referenced by no other skill.

Overview

PublisherOscaner
Repositoryskills
Skill namecdd-dev
Stars
63
Forks
0
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Oscaner on GitHub. Read the source before you install it.

Installation

Install the Cdd Dev AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Oscaner/skills/tree/main/packages/kairos/skills/cdd-dev
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Oscaner/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/packages/kairos/skills/cdd-dev .claude/skills/cdd-dev
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cdd Dev in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cdd Dev on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cdd Dev is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Kairos CDD-Dev

Executes planned tasks through the engine's implement / review / fix chain — its own orchestrator decisions (mode chain, final review) following the engine's next: line, with no installed-engine precondition (npx pulls the engine on demand); on closure the engine's next: face carries the pipeline continuation.

Invocation discipline — every engine command runs via npx -y @oscaner-skills/cdd-engine@latest <subcommand> as a background dispatch: launch it in the background and await the completion notification — never poll — then read the full output (stdout/stderr). Output filtering is forbidden — no piping to tail/head, no 2>&1 |, no EXIT=$? capture. Round rhythm — one review pass per dispatch; a fix round re-enters the review while the route is not done; the implement child commits its own work; ensure the working tree is clean before entering review (dirty → BLOCKED); the orchestrator writes no tree during dispatch. Failure face — cross-node failure handling lives in the Failure Modes table below (the single source); node Fail entries keep only local behavior.

Flow Digraph

Node Definitions

determine-base

  • Do: Resolve the base branch — inference sources in order: plan base field → branch upstream (git rev-parse --abbrev-ref @{u}) → conversation context; if none yields a definitive base, ask the user — do not guess. The base may already be present in the artifact (npx -y @oscaner-skills/cdd-engine@latest base get --plan <path>); skip inference when the artifact is present.
  • Read: plan document + git upstream + conversation context + npx -y @oscaner-skills/cdd-engine@latest base get --plan <path>
  • Exit: base resolved → set-base
  • Fail: user refuses to confirm → BLOCKED: base-undecided

set-base

  • Do: Persist the base via the engine CLI — npx -y @oscaner-skills/cdd-engine@latest base set --plan <path> --base <branch> --source <enum>, where source is plan-field | branch-upstream | conversation-context | user-confirmed. The engine is the sole write/read path for the artifact — never hand-write it; set is idempotent, refusals and validation errors are engine-handled.
  • Read: npx -y @oscaner-skills/cdd-engine@latest base get --plan <path> (the artifact on stdout)
  • Exit: artifact written (or already present) → implement-wave
  • Fail: engine refusal / validation error → report to user; user decides (no hand-written artifact)

implement-wave

  • Do: Dispatch npx -y @oscaner-skills/cdd-engine@latest implement --tasks <n|n,n,…> --plan <path> — one dispatch wave per iteration; dispatch each wave as the engine returns it, never a hand-picked subset.
  • Read: the engine output — its conclusion and the dispatch-ready next: line (the literal next step); a BLOCKED round carries no next: line and rides its reason on stderr
  • Exit: dispatch complete → NEXT-LOOP
  • Fail: nested CLI exits with no output → BLOCKED: engine-error (report via kairos:cdd-report(pi:/skill:cdd-report))

NEXT-LOOP

  • Do: Run the wave review-fix rhythm through the engine — one review pass per dispatch. Dispatch the wave review (npx -y @oscaner-skills/cdd-engine@latest review --type wave --tasks <n|n,n,…> --plan <path>); dispatch the output's next: line as written — the literal is the route: a fix dispatch consumes the findings handoff, a next-wave dispatch runs through implement-wave; when the waves are exhausted the engine's next face carries the wave→branch transition, and the branch review travels the engine's own branch line (review --type branch --plan <path>, then its fix --type branch findings rounds) with no branch-specific orchestrator routing — the literal dispatches the next hop. A BLOCKED/TIMEOUT round carries no next: line — its reason rides stderr for reporting.
  • Read: the full stdout — the review conclusion and the next: line (the dispatch-ready literal — fix / the next wave / the branch transition — plus the branch line's own carried facts: the base branch the engine injected, the soft-cap suggestion, a persistent-blocker fail-open)
  • Exit: each fix / next-wave pass re-enters this hub while the route is not terminal — the engine's next: literal has not yet handed the orchestrator off to a skill ref or the pipeline terminus (the until next is terminal self-loop)
  • Fail: review exits with no output → BLOCKED: engine-error

Invariants

#Invariant
I1Host Harness Autodetection — the engine resolves the host harness itself; the orchestrator never passes a harness name down to the CLI
I2Review Convergence (next: is the dispatch) — a review closes by its conclusion (APPROVED / CHANGES_REQUESTED / REVIEW_FIX), and the output's next: line carries the engine's default next step as a complete executable command; read the next: line and dispatch it as written — the literal is the dispatch, no kind→command mapping layer. A mid-backfill or a user adjudication that lands governs over the suggestion (current world state wins). Fixes always dispatch via the fix round, consuming the findings handoff; the orchestrator must not edit in place as a substitute; a fix commit makes the next review a new review
I3Wave-Chain Completeness — every dispatch wave (a singleton task or a merged wave) goes through the full implement → review → (fix if findings) chain; review is unskippable, and a fix dispatch requires a prior review handoff for that wave
I4No Controller Bypass — the orchestrator must not hand-write control-flow bypasses; all task execution / review / fix dispatch go through engine CLI calls, and the engine's next: line decides the order — never orchestrator guesswork
I5Mid-Flight Backfill — a user-raised backfill of overall/spec/plan docs surfaced while a dispatch is in flight lands through four ordered steps on the current call's return: (1) land immediately — hot context, no deferral; (2) commit on its own — a standalone change, never mixed with implementation commits; (3) pause the loop until clean — an uncommitted backfill trips the next review's entry gate (dirty → BLOCKED); (4) audit in-band on resume — the next review audits the backfill in-band; a backfill rewriting the current task's own plan/spec text routes through the orchestrator's plan write authority

Failure Modes

Cross-node failure handling (complements node Fail fields):

categoryhandling
TIMEOUTa round that timed out; retry within the handoff's counters cap, then terminal per the output contract
BLOCKEDa round the engine refused to advance (no next: line); report the carried reason to the user and let them decide — never silently override the plan
HARD_ERRORa completed round with no next: line that is not BLOCKED/TIMEOUT — report the carried reason; re-run the same command to continue
branch blockers persistimplicit fail-open — stop + report; the branch is preserved and the user decides (the branch line carries its own closure through the engine's next face)

Fail-open vs BLOCKED convention:

  • BLOCKED: explicit terminal state; requires user intervention to recover.
  • implicit fail-open: node-level failure (not in digraph); flow stops + reports to user.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cdd Dev AI skill do?

Independent cdd-dev orchestrator -- Node-anchored flow with digraph as single control-flow source of truth. Determines base via the engine artifact, dispatches the CDD implement / review / fix chain following the engine's next face, and runs the branch final review — the engine's next directive carries the pipeline continuation. Callable standalone; referenced by no other skill.

Why use Cdd Dev on TypingMind?

Because you install it once and use it with any model. Cdd Dev is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cdd Dev in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Oscaner/skills/tree/main/packages/kairos/skills/cdd-dev. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cdd Dev?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cdd Dev?

As many as you like. As long as a model supports skills, you can use Cdd Dev with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cdd Dev AI skill free?

Yes. It is published on GitHub by Oscaner under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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