Automated summary
This skill provides a structured methodology for orchestrating AI agents with upfront planning, subagent delegation, self-improvement through lessons, rigorous verification, and a pursuit of elegance.
Strengths
- Reduces ambiguity by requiring a detailed planning phase before execution.
- Keeps the main context clean by offloading exploratory tasks to subagents.
- Encourages continuous learning by documenting corrections in a lessons file.
- Enforces systematic verification (logs, tests, diffs) before marking tasks complete.
Limitations
- Can slow down simple tasks with overhead of planning and verification.
- Requires disciplined maintenance of task and lesson files.
- The elegance demand may lead to over-engineering if not balanced with simplicity.
Use this skill for complex projects or non-trivial tasks that benefit from a structured, repeatable approach with AI agents.
Avoid this skill for quick, obvious fixes or when the task is too simple to warrant detailed planning and verification.
Security analysis
SafeThe skill defines abstract workflow and process guidelines (plan mode, subagents, verification, bug fixing). It does not instruct the agent to execute any destructive, exfiltrating, or obfuscated actions. No tools are declared, and the content is purely advisory for improving code quality and development practices.
No concerns found
How this analysis is madeExamples
I need to implement user authentication with JWT. Enter plan mode, write a detailed plan to tasks/todo.md with checkable items, then verify the plan with me before starting.Create an architecture for a microservices app. Use subagents to research the pros and cons of different service mesh tools, then compile the results into a comparison table.I just corrected you on using the wrong API endpoint. Add a lesson to tasks/lessons.md so you don't make the same mistake again.Workflow Orchestration
1. Plan Mode Default
- Enter plan mode for ANY non-trivial task (3+ steps or architectural decisions)
- If something goes sideways, STOP and re-plan immediately — don't keep pushing
- Use plan mode for verification steps, not just building
- Write detailed specs upfront to reduce ambiguity
2. Subagent Strategy
- Use subagents liberally to keep main context window clean
- Offload research, exploration, and parallel analysis to subagents
- For complex problems, throw more compute at it via subagents
- One task per subagent for focused execution
3. Self-Improvement Loop
- After ANY correction from the user: update
tasks/lessons.mdwith the pattern - Write rules for yourself that prevent the same mistake
- Ruthlessly iterate on these lessons until mistake rate drops
- Review lessons at session start for relevant project
4. Verification Before Done
- Never mark a task complete without proving it works
- Diff behavior between main and your changes when relevant
- Ask yourself: “Would a staff engineer approve this?”
- Run tests, check logs, demonstrate correctness
5. Demand Elegance (Balanced)
- For non-trivial changes: pause and ask “is there a more elegant way?”
- If a fix feels hacky: “Knowing everything I know now, implement the elegant solution”
- Skip this for simple, obvious fixes — don't over-engineer
- Challenge your own work before presenting it
6. Autonomous Bug Fixing
- When given a bug report: just fix it. Don't ask for hand-holding
- Point at logs, errors, failing tests — then resolve them
- Zero context switching required from the user
- Go fix failing CI tests without being told how
Task Management
- Plan First: Write plan to
tasks/todo.mdwith checkable items - Verify Plan: Check in before starting implementation
- Track Progress: Mark items complete as you go
- Explain Changes: High-level summary at each step
- Document Results: Add review section to
tasks/todo.md - Capture Lessons: Update
tasks/lessons.mdafter corrections
Core Principles
- Simplicity First: Make every change as simple as possible. Impact minimal code.
- No Laziness: Find root causes. No temporary fixes. Senior developer standards.
- Minimal Impact: Changes should only touch what's necessary. Avoid introducing bugs.
From the file to a first result
The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.
View the source and supporting filesThis download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.
1. Download the file
Download SKILL.mdFrom the root of a test project, save the file as .skill-downloads/ai-workflow-orchestration-mrwak8h6/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.
Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.
mkdir -p '.skill-downloads/ai-workflow-orchestration-mrwak8h6' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/ai-workflow-orchestration-mrwak8h6/download' --output '.skill-downloads/ai-workflow-orchestration-mrwak8h6/SKILL.md.part' && mv '.skill-downloads/ai-workflow-orchestration-mrwak8h6/SKILL.md.part' '.skill-downloads/ai-workflow-orchestration-mrwak8h6/SKILL.md'2. Check prerequisites
Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.
No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.
The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.
3. Load the skill
This file does not provide a usable name/description header for this native procedure. It remains available to download and inspect; use the manual method below.
Manual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.
4. Try a first task
Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:
Read .skill-downloads/ai-workflow-orchestration-mrwak8h6/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.
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