Works with the AI tools you already use
Agentic Workflow
by Sinu
A risk-aware, evidence-based engineering lifecycle protocol for robust agentic task execution and safety.
Free
About this skill
What it does
The Agentic Workflow is a language-agnostic, risk-aware protocol designed to prevent the four most common AI agent failures: silent assumptions, scope creep, untested deployments, and credential leakage. It transforms vague requests into evidence-based execution by enforcing a multi-stage engineering lifecycle similar to a senior dev's mental model.
Why use this skill
Prompting an AI often results in "hallucinated" progress or unexpected side effects. This skill forces your agent to follow a structured discipline of research, planning, safety-gating, and verification. It is better than raw prompting because it includes built-in guardrails like cascading failure detection, secret scanning, and an absolute "never push" rule for production stability.
Supported tools
- Development Environments: Works inside or outside Git worktrees with filesystem fallback.
- Frameworks: Language-agnostic (Python, Go, Node, Rust, etc.).
- CI/CD: Comprehensive verification matrices and rollback planning.
- Safety: Integrated secret scanning and shell quoting safety protocols.
The Output
Instead of just code, you receive an audit-ready closure report including a Risk Class assessment, a Verification Matrix (Passed/Failed/Unstable), a Scope Check (Planned vs. Actual changes), and a Rollback Strategy. It ensures all work is staged, verified, and ready for human approval.
Details
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
Reviews
Security Scanned
Passed automated security review
Permissions
File Scopes
Creator
Frequently Asked Questions
Browse More Skills
prompt-engineer
Professional prompt engineering patterns for building robust, secure, and production-ready LLM applications.
designing-hybrid-context-layers
Architects the right retrieval strategy for every query — teaching your agent when to use RAG, a knowledge graph, or a temporal index instead of defaulting to vector search for everything.
synthesizing-institutional-knowledge
Builds the organizational memory schema your AI agent needs to answer why — capturing decision provenance, causal chains, and event context that embedding-based retrieval permanently discards.
code-reviewer
Reviews your code for bugs, security vulnerabilities, logic errors, performance issues, and style violations. Organizes findings by severity and suggests fixes with code examples.