plugins/handsonai/skills/run/SKILL.md
This skill should be used when the user has built and tested workflow artifacts and wants a Run Guide for deploying and operating their AI workflow. It generates a plain-language guide with setup steps, deployment patterns, and sharing instructions — tailored to the user's platform and build path. This is Step 6 (Run) of the AI Workflow Framework.
npx skillsauth add jamesgray-ai/handsonai-plugins runInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Generate a Run Guide for deploying, executing, and testing an AI workflow. The Run Guide bridges the gap between "artifacts exist" and "workflow is running."
Design principle: The skill is the framework, the model is the platform expert. No platform names, SDK references, API patterns, GUI walkthroughs, or tool-specific examples appear anywhere in the skill. All platform-specific knowledge is researched by the model at runtime via web search.
Role: You are an Agentic AI Architect. Your role is to guide the user through getting their workflow running — with clear, platform-specific instructions tailored to their technical comfort level.
Read the workflow's manifest (outputs/[workflow-name]/workflow.yaml) and load the Design Spec it registers (normally outputs/[workflow-name]/design-spec.md). If the user specifies a file path, use that; if no manifest exists but legacy flat files do, use those paths.
Detect the build path — don't ask first. The artifacts on disk plus the spec frontmatter answer this in almost every case:
platform_mode: guided → Path 3 (Guided-mode): the Build phase produced GUI instruction documents (for guided-mode platforms like Copilot Studio, Workspace Studio, ChatGPT Agent Mode).platform_mode: code and the manifest's artifacts.platform_artifacts entries (or the spec's Deployment Plan target locations) resolve to files on disk → Path 1 (Model-built): the model generated the artifacts during Build.platform_mode: code and no generated artifacts found → Path 2 (Manual build): the user chose to build artifacts themselves using the spec as the guide.State the detected path and let the user correct it ("It looks like the artifacts were model-built — I'll write the Run Guide for that. Say so if you actually built them yourself."). Only ask the open question — "Did the model generate your workflow artifacts (Path 1), are you building them yourself from the spec (Path 2), or did the model produce GUI instruction documents (Path 3)?" — if the evidence is genuinely ambiguous.
Generate the Run Guide based on the build path.
Variant A: Model-built artifacts (Path 1)
Walk the user through getting the workflow running. Use the platform and code comfort (resolved during artifact generation) to tailor every instruction to their specific setup. Use web search to verify current platform steps. Write in plain language — assume no technical background unless code comfort was confirmed.
The Run Guide covers four sections:
A. What was built — List every artifact produced, what it does, and where it was saved. Use a simple table:
| Artifact | What it does | Location | |----------|-------------|----------|
B. Setup steps — Numbered, platform-specific instructions for getting each artifact into the right place. Research the platform's current UI/workflow via web search. For each step:
C. First run — A guided test run:
D. What to do next — Brief guidance on:
Variant B: Manual build (Path 2)
Provide a Construction Guide instead of setup instructions. The user will build the artifacts themselves.
A. What to build — List every artifact from the spec, what it does, and the recommended file format for the user's platform. Use a table:
| Artifact | Purpose | Format | Priority | |----------|---------|--------|----------|
B. Build sequence — Ordered implementation steps following the spec's recommended implementation order. For each artifact:
C. First run — Same as Variant A: guided test run with sample input.
D. What to do next — Same as Variant A: repeatable trigger, sharing, iteration guidance.
Variant C: Guided-mode platforms (Path 3)
The Build phase produced GUI instruction documents rather than deployable code artifacts. The Run Guide walks the user through following these instructions.
A. What was built — List the instruction documents produced, what each covers, and which platform screens they reference. Use a table:
| Document | What it covers | Platform area | |----------|---------------|---------------|
B. Setup steps — Walk through following the GUI instructions in order:
C. First run — Same as Variant A: guided test with sample input, expected behavior at each step, what good output looks like, common first-run issues.
D. What to do next — How to modify the configuration later, share with team members (if the platform supports it), when to revisit and update, change management notes for organizational workflows.
Section E — Running it in a fresh or scheduled session (include in all variants).
A workflow that worked while you were building it can fail the first time it runs in a new or unattended session, because that environment doesn't inherit this one's setup. State these as requirements the run environment must satisfy — and let the model fill in the concrete commands/clicks for the user's specific platform at runtime (per the Design Principle, do not hardcode platform commands in this skill):
Section F — Run log (include in all variants).
Create outputs/[workflow-name]/runs.md with a header row, and make "log the run" part of the workflow's routine — one line per run is enough:
| Date | Input / trigger | Result | Edits needed | Notes |
|---|---|---|---|---|
Tell the user why it's worth ten seconds: when they review this workflow later (Step 7 — Improve), the log is the difference between "I think it's been fine?" and actual evidence of drift, recurring edits, or failures. If the workflow runs on the platform itself (an orchestrator skill or agent the loop executes), Build should already have baked self-logging into the orchestrator artifact (per the spec's Deployment Plan Run Logging requirement) — verify it appends a row to runs.md at the end of each run. If it doesn't (older spec or pre-logging Build), add that logging step to the orchestrator artifact now so it self-logs going forward. Either way, logging then costs the user nothing.
Present the Run Guide directly in the conversation. Also save it to outputs/[workflow-name]/run-guide.md so the user has a reference they can follow later or share with teammates. Then update the workflow manifest (outputs/[workflow-name]/workflow.yaml): set current_step: 6, last_updated, and add run_guide and run_log under artifacts.
outputs/[workflow-name]/run-guide.md — Run GuidePlain-language guide for getting the workflow running. Three variants:
All variants also include Section E — Running it in a fresh or scheduled session (artifact loading, per-session connector authorization, prerequisites and the safety checklist for unattended/scheduled runs) and Section F — Run log (outputs/[workflow-name]/runs.md, one line per run, feeding evidence into Step 7 — Improve), written as platform-agnostic requirements the model resolves to concrete steps at runtime.
outputs/[name]/run-guide.md."next_review: YYYY-MM-DD in the workflow manifest, and tell them the exact re-entry command: "When the date arrives (or sooner if output quality slips), start a new conversation and say: 'Run the improve skill on [workflow name]' — the manifest and run log carry everything it needs." If the platform supports scheduled tasks or reminders, offer to set one up.writing-workflow-sops skill. The SOP captures what the workflow does, when to trigger it, what inputs it needs, and who's responsible — useful for onboarding teammates and maintaining the workflow over time."documentation
Write Standard Operating Procedure documentation for workflows and save as markdown files. Selects full or lightweight SOP template based on autonomy level (deterministic vs. guided/autonomous), then adapts for workflow type (Manual, Augmented, Automated). Use when the user asks to write an SOP, document a workflow, create procedure documentation, or capture how a workflow is executed. Triggers on "write an SOP", "document this workflow", "create operating instructions", "how is this workflow executed".
development
Guide structured testing of AI workflow artifacts, evaluate output quality, identify which building blocks need adjustment, and determine readiness for deployment. Use when the user has built workflow artifacts and needs to test them. This is Step 5 (Test) of the AI Workflow Framework.
development
Evaluate a running AI workflow for quality, relevance, and evolution opportunities. Use when the user wants to review how a deployed workflow is performing, check if it needs tuning, or assess whether it should graduate to a more capable orchestration mechanism. This is Step 7 (Improve) of the AI Workflow Framework.
development
This skill should be used when the user has a Workflow Requirements document and wants to design an AI workflow. It gathers architecture decisions, assesses workflow autonomy level, chooses an orchestration mechanism and involvement mode, classifies steps, maps building blocks, identifies skill candidates, configures agents, and produces a Design Spec for approval. Supports both step-decomposed and goal-driven Workflow Requirements. This is Step 3 (Design) of the AI Workflow Framework.