Sharkbait

Experimental: This project is under active development. APIs may change, features may break, and Dory might forget what she was doing. Use at your own risk!

"Just keep coding, just keep coding..." — Dory, probably

Sharkbait is a CLI-based AI coding assistant built with Bun and TypeScript. It uses the OpenAI Responses API (not Chat Completions) for enhanced tool calling and streaming. Like Nemo escaping the dentist's fish tank, it helps you break free from tedious coding tasks.

The development environment is powered by Anthropic skills and Claude Code plugins that provide specialized workflows across design, engineering, research, and operations.

The ASCII Shark

                              +.+++.
                            ## ....-### -
                           +  ###++++-#++-
                           - ##+-++--+-+--.
                             #++++-+-------..
                           - -#+-----++-.....
                           ##-.----+-.  ###########
                           #..-++--. +#####+.   .. .
                       ####..--+--..##. ...-+####++-.
                     #  . .-.--+-. ## .+++++++++++-++..
                    # ##+### -+--.## .--+++++++++++++-+
                   + .#+-###.--+.-# -+--------+++++-...
                     -#.-###.--- #+.+---... ...-+++-.###
                  ###.--+##-.++.## ---...####...-+++- .###
    #           .. ##- --## -#.-#.-+...##   ###.-++++   ##+
   #- ###+--+#---#- # +++-..#-.+# +-.-##.    ##-.-+++#####.
  ## ##---+##+--++#+.+-+-+--+-.+#.+-.####--.###+.-+++.###+.
  # .#+++----++-+-++.#..+-.---.#+---..########..-++++-....-+##-.
  #. #++##++--+----+--#.#-----.#+-----........-+++++++++++-----+#.
  #- #++-+++++++--+++.# -#..--.+#.--------....--+++++--.--.#++++#+
  +# ##---+++++++---+.+#.-#+---.#+..-------++--.....--++-.++--++#.
   # .##+++--++-++-+-....++- ....+#...----++--++####++..-##++++##
    #  ###+++-++-++..  .    +##..+.+#-......------....+#+++++##-
     #   #####+++.           ## ++--+.  ..      .-###########-
       #.    .+.                ..- +.               .###-
           #-.                +   #

Features

• Fast — Built on Bun. Swims through code faster than Marlin crossing the EAC
• Responses API — Uses OpenAI's Responses API for better streaming and tool calling
• Tool-equipped — File ops, shell commands, Git, GitHub. Everything but the Ring of Fire
• Persistent Memory — Beads give your AI long-term memory that survives sessions (unlike Dory)
• Git-backed Context — Your AI's memory lives in your repo. Clone it, branch it, merge it
• Beautiful UI — Ink-based terminal interface. P. Sherman would approve
• Safe — Confirms dangerous operations before executing. No surprise rm -rf moments

The Memory Problem

Most AI coding assistants have the memory of... well, Dory. They forget context between sessions, lose track of what you were working on, and make you repeat yourself constantly.

Sharkbait is different. Built-in Beads provide persistent, git-backed memory that survives across sessions:

• Task Memory: Create a bead for a feature, and Sharkbait remembers the context, decisions, and progress — even after you close the terminal
• Git-Native: Beads are stored alongside your code in git, so your AI's memory travels with your repo
• No External Services: Your context stays local. No cloud sync, no API calls for memory — just git

Skills & Plugins

Sharkbait's development environment ships with the full Anthropic Skills catalog and a curated set of Claude Code plugins. These are used during development with Claude Code — they are not runtime features of the Sharkbait application itself.

Installed Anthropic Skills

Category	Skills	What They Do
Design	canvas-design, frontend-design, brand-guidelines, theme-factory	Visual art, production-grade UI, brand colors, themed styling
Art & Media	algorithmic-art, slack-gif-creator	Generative art with p5.js, animated GIF creation
Documents	docx, pdf, pptx, xlsx, doc-coauthoring	Create/edit Office docs, PDFs, spreadsheets, co-author documents
Engineering	web-artifacts-builder, mcp-builder, webapp-testing	Multi-component web apps, MCP servers, browser testing
Meta	skill-creator, internal-comms	Create new skills, write internal communications

Active Plugins

Plugin	Purpose
compound-engineering	Multi-agent workflows: plan, brainstorm, review, work
feature-dev	Guided feature development with codebase understanding
code-review	PR review with specialized analysis agents
pr-review-toolkit	Silent failure hunting, type design, test coverage
code-simplifier	Post-implementation code clarity pass
coderabbit	AI code review on changes
hookify	Create hooks to prevent unwanted behaviors
plugin-dev	Build and validate Claude Code plugins
agent-sdk-dev	Verify Agent SDK applications
claude-code-setup	Automation recommendations
claude-md-management	CLAUDE.md auditing and improvement
playground	Interactive HTML playground creation
commit-commands	Commit, push, PR workflows
github	GitHub integration

Plugin	Language
typescript-lsp	TypeScript/JavaScript
pyright-lsp	Python
gopls-lsp	Go
clangd-lsp	C/C++
csharp-lsp	C#
jdtls-lsp	Java
kotlin-lsp	Kotlin
lua-lsp	Lua
php-lsp	PHP
rust-analyzer-lsp	Rust
swift-lsp	Swift

Plugin	Domain
data	SQL, dashboards, visualizations, statistical analysis
marketing	Campaigns, brand voice, SEO, content, competitive analysis
finance	Journal entries, reconciliation, SOX, variance analysis
legal	Contract review, NDA triage, compliance checks
product-management	Specs, roadmaps, sprint planning, user research
sales	Pipeline, forecasting, outreach, competitive intel
customer-support	Triage, research, escalation, KB articles
enterprise-search	Cross-source search, knowledge synthesis
productivity	Task management, memory systems
bio-research	PubMed, ChEMBL, clinical trials, bioRxiv, scRNA-seq

Plugin	Purpose
ralph-loop	Autonomous agent loop
huggingface-skills	HF Hub: models, datasets, training, evaluation
context7	Up-to-date library documentation
playwright	Browser automation and testing
frontend-design	Production-grade frontend components

Compound Engineering Workflows

The compound-engineering plugin provides multi-agent orchestration:

Workflow	Command	Description
Plan	/plan	Transform feature descriptions into structured plans
Brainstorm	/brainstorm	Explore requirements through collaborative ideation
Work	/work	Execute plans efficiently with quality gates
Review	/review	Exhaustive multi-agent code review
Compound	/compound	Document solved problems for future reference

Installation

# From source
git clone https://github.com/shyamsridhar123/sharkbait.git
cd sharkbait
bun install
bun run build:binary

Prerequisites

• Bun >= 1.0.0
• Git >= 2.30
• gh (GitHub CLI) >= 2.40 (optional, for GitHub features)
• Azure OpenAI API access

Configuration

1. Set up your Azure OpenAI credentials:

export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com"
export AZURE_OPENAI_API_KEY="your-api-key"
export AZURE_OPENAI_DEPLOYMENT="gpt-codex-5.2"

2. Or create a .env file:

cp .env.example .env
# Edit .env with your credentials

Usage

Interactive Chat

sharkbait chat

One-off Question

sharkbait ask "How do I refactor this function?"

Autonomous Task Execution

sharkbait run "Add input validation to the login endpoint"

Initialize in Project

cd your-project
sharkbait init

Slash Commands

During an interactive chat session, use slash commands for quick actions:

Navigation

Command	Description
/cd <path>	Change working directory (creates if needed)
/pwd	Show current working directory

Session

Command	Description
/clear	Clear message history
/exit	Exit Sharkbait (aliases: /quit, /q)

Configuration

Command	Description
/beads [on|off]	Toggle or check Beads task tracking
/model [name]	Show or switch the LLM model
/tasks	Show Beads task status
/context [add|remove|list]	Manage context files

Actions

Command	Description
/setup	Launch interactive setup wizard
/init	Initialize Sharkbait in current directory
/ask <question>	Ask a one-off question
/run <task>	Execute a task autonomously
/review <file>	Run parallel code review (bugs, security, style, performance)

Information

Command	Description