KS
Killer-Skills

workflow-bridge — how to use workflow-bridge how to use workflow-bridge, workflow-bridge alternative, workflow-bridge setup guide, what is workflow-bridge, workflow-bridge vs other CI tools, install workflow-bridge, workflow-bridge for AI coding agents, git integration with workflow-bridge, deterministic task management with workflow-bridge

v1.0.0
GitHub

About this Skill

Ideal for AI Coding Agents requiring streamlined pipeline management and deterministic task continuation. workflow-bridge is a lightweight CLI task management tool designed for AI coding agents, prioritizing determinism, simplicity, and zero-friction git integration.

Features

Enters plan mode with deterministic continuation instructions
Discovers next pipeline phase and creates plan mode handoff
Survives context compaction for seamless workflow
Invoked by processing skills like technical-discussion and technical-specification
Adheres to zero output rule for efficient processing

# Core Topics

leeovery leeovery
[2]
[0]
Updated: 2/28/2026

Quality Score

Top 5%
57
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add leeovery/tick/workflow-bridge

Agent Capability Analysis

The workflow-bridge MCP Server by leeovery is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion. Optimized for how to use workflow-bridge, workflow-bridge alternative, workflow-bridge setup guide.

Ideal Agent Persona

Ideal for AI Coding Agents requiring streamlined pipeline management and deterministic task continuation.

Core Value

Empowers agents to manage pipeline phases with simplicity, utilizing deterministic continuation instructions and context compaction, while supporting CLI-based task management and plan mode handoffs via technical skills like technical-discussion and technical-specification.

Capabilities Granted for workflow-bridge MCP Server

Streamlining pipeline phases for AI coding tasks
Automating task handoffs between pipeline phases
Ensuring deterministic continuation of tasks

! Prerequisites & Limits

  • Requires technical skills like technical-discussion and technical-specification for invocation
  • Must be used within a CLI environment
  • Survives context compaction but may require additional configuration
Project
SKILL.md
3.1 KB
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8

# Tags

[No tags]
SKILL.md
Readonly

Enter plan mode with deterministic continuation instructions.

This skill is invoked by processing skills (technical-discussion, technical-specification, etc.) when a pipeline phase concludes. It discovers the next phase and creates a plan mode handoff that survives context compaction.

ZERO OUTPUT RULE: Do not narrate your processing. Produce no output until a step or reference file explicitly specifies display content. No "proceeding with...", no discovery summaries, no routing decisions, no transition text. Your first output must be content explicitly called for by the instructions.

Instructions

This skill receives context from the calling processing skill:

  • Topic: The topic name
  • Work type: greenfield, feature, or bugfix
  • Completed phase: The phase that just concluded

Step 1: Run Discovery

!.claude/skills/workflow-bridge/scripts/discovery.sh

If the above shows a script invocation rather than YAML output, the dynamic content preprocessor did not run. Execute the script before continuing:

bash
1.claude/skills/workflow-bridge/scripts/discovery.sh

The output contains three sections: features:, bugfixes:, and greenfield:. Use the known work type and topic from the calling context to extract the relevant data:

If work type is "feature"

Find the topic entry under features: > topics: and extract its next_phase.

If work type is "bugfix"

Find the topic entry under bugfixes: > topics: and extract its next_phase.

If work type is "greenfield"

Parse the greenfield: section for phase-centric state:

  • state: Counts of artifacts across all phases
  • Phase-specific file lists with their statuses

→ Proceed to Step 2.


Step 2: Route to Continuation Reference

Based on work type, load the appropriate continuation reference:

If work type is "feature"

Load feature-continuation.md and follow its instructions as written.

If work type is "bugfix"

Load bugfix-continuation.md and follow its instructions as written.

If work type is "greenfield"

Load greenfield-continuation.md and follow its instructions as written.


Notes

Feature/bugfix continuation references:

  1. Use discovery output to compute a single next_phase
  2. Call EnterPlanMode tool, write plan file with instructions to invoke start-{next_phase} with topic + work_type
  3. Call ExitPlanMode tool for user approval

The user will then clear context, and the fresh session will invoke the appropriate start-* skill with the topic and work_type provided, causing it to skip discovery and proceed directly to validation/processing.

Greenfield continuation is interactive — greenfield is phase-centric with multiple actionable items, so there is no single next phase. The reference displays state, presents a menu of choices, waits for user selection, then enters plan mode with that specific choice. The plan mode content is deterministic (same as feature/bugfix) once the user has chosen.

Related Skills

Looking for an alternative to workflow-bridge or building a Categories.community AI Agent? Explore these related open-source MCP Servers.

View All

widget-generator

Logo of f
f

widget-generator is an open-source AI agent skill for creating widget plugins that are injected into prompt feeds on prompts.chat. It supports two rendering modes: standard prompt widgets using default PromptCard styling and custom render widgets built as full React components.

149.6k
0
Design

chat-sdk

Logo of lobehub
lobehub

chat-sdk is a unified TypeScript SDK for building chat bots across multiple platforms, providing a single interface for deploying bot logic.

73.0k
0
Communication

zustand

Logo of lobehub
lobehub

The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction.

72.8k
0
Communication

data-fetching

Logo of lobehub
lobehub

The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction.

72.8k
0
Communication