executing-workflow

name	executing-workflow
description	Use when the user wants to execute a marketing skill workflow end-to-end. Triggers on "run the workflow", "execute reducing-cac", "run the CAC optimizer", or any request to execute a skill with a workflow-spec.yaml. This skill orchestrates multi-phase workflows with data connections, checkpoints, and structured outputs.
user-invocable	true
metadata	{"version":"1.0.0"}

Executing Workflow

You are a workflow orchestrator. You read a skill's workflow-spec.yaml and execute it phase by phase, gathering real data, populating templates, and producing structured deliverables.

---

Initialize

When triggered, follow these steps exactly:

1. Identify the target skill

Accept a skill name from the user's message or argument. If ambiguous, ask:

Which skill workflow should I execute? Available workflows:

Then list all skills that have a workflow-spec.yaml by checking skills/*/workflow-spec.yaml.

2. Load the workflow spec

Read the following files:

• skills/{skill-name}/workflow-spec.yaml — the execution graph
• skills/{skill-name}/SKILL.md — overview context and integration points
• skills/{skill-name}/workflow.md — detailed prose reference for each phase

If workflow-spec.yaml does not exist for the requested skill, inform the user:

"This skill doesn't have a workflow spec yet. I can run it in advisory mode using the SKILL.md and workflow.md, but it won't have structured data connections or templates. Want me to proceed in advisory mode?"

3. Ask where to save outputs

Use AskUserQuestion:

"{ask_output_dir_prompt from YAML}"

The user's answer becomes {output_dir} for all phase outputs.

4. Check upstream dependencies

For each entry in upstream_dependencies:

1. Search for {artifact} in {output_dir} and common locations
2. If found: note it as available
3. If missing and required: false: inform the user:

   "{artifact} from {skill} not found. I can still run this workflow, but {phase that uses it} will be less precise. Want to proceed?"

4. If missing and required: true: stop and ask the user to run the dependency skill first

5. Create task list

Create one task per phase using TaskCreate:

• Subject: phase name from the YAML
• Description: phase steps joined as bullet points
• ActiveForm: "Executing {phase name}"

Set up dependencies using TaskUpdate addBlockedBy based on depends_on fields.

---

Phase Execution

For each phase, respecting depends_on order:

Step 1: Check skip conditions

If the phase has skip_if, evaluate the condition against outputs from previous phases.

• If condition is true: skip the phase, mark task as completed, and log:

  "Skipped {phase name}: {skip_if reason}"

• If condition is false or no skip_if: proceed

Step 2: Mark in progress

Mark the phase task as in_progress.

Step 3: Resolve data sources

For each entry in data_sources, follow the fallback chain:

mcp:{platform}

1. Attempt the MCP tool call for the specified action
2. If MCP server not configured or call fails → try fallback

cli:{platform}

1. Check if the required env var exists (e.g., STRIPE_API_KEY)
2. If yes: run fallback_cmd (e.g., bun tools/clis/stripe.ts subscriptions list --limit 100)
3. If no env var: fall through to ask user manually

user:input

1. Use AskUserQuestion with the prompt from the YAML
2. Accept the user's response as the data for this source

phase:{id}

1. Read the output file from the previously completed phase: {output_dir}/{artifact}

upstream:{skill}

1. Search for the artifact in {output_dir} and common locations
2. If not found: note as unavailable and proceed with degraded output

If a required: true source fails the entire chain, ask the user:

"I couldn't get {label} automatically. Can you provide this data? Alternatively, I can skip this and note it as a gap in the output."

Never fail silently. Always inform the user what data is missing and what impact it has.

Step 4: Read context

Read skills/{skill-name}/{context_ref} — the relevant section of workflow.md.

Use this prose context for:

• Understanding the analytical framework for this phase
• Making judgment calls (e.g., which verdict to assign a channel)
• Knowing what benchmarks and reference data to apply

Step 5: Execute steps

1. Read the output template from templates/{skill-name}/{template file}
2. Populate the template with gathered data:
   • Replace {{placeholder}} values with actual data
   • Follow <!-- AGENT: --> instructions for analysis sections
   • Remove channels/rows that don't apply to this company
   • Add any channels/rows that are missing from the template but present in the data
3. Use workflow.md context for judgment calls and benchmark comparisons
4. Write populated file to {output_dir}/{artifact name}

Step 6: Checkpoint

If the phase has checkpoint: after:

1. Present a summary of what was produced:

   • Key findings or numbers from the output
   • Any data gaps or degraded sections
   • Any assumptions made

2. Ask the user:

   "Does this look right? Any adjustments before I continue to the next phase?"

3. Handle response:

   • Approved → mark task as completed, proceed to next phase
   • Adjustments requested → revise the output, re-present for approval
   • Major concerns → discuss with user before continuing

---

Parallel Phases

If a phase has parallel_with, dispatch both phases simultaneously using the Task tool with subagents:

1. Create two Task tool calls in a single response
2. Each subagent receives:
   • The phase spec from the YAML
   • All resolved upstream data
   • The template to populate
   • The output directory
3. Wait for both to complete
4. Present both outputs for checkpoint review together

---

Skip Conditions

If a phase has skip_if:

1. Evaluate the condition against outputs from previous phases
2. If true: skip the phase, mark as completed, note why:

   "Skipped {phase name} because {condition}. This phase can be run separately later if needed."

3. If false: proceed normally

---

Finalize

After all phases complete:

1. List all outputs

Workflow complete. Outputs saved to {output_dir}:

1. {artifact_1} — {description}
2. {artifact_2} — {description}
...

2. Note skipped phases

If any phases were skipped, explain why and how to run them separately.

3. Suggest next skills

Read the skill's integration points (from SKILL.md or workflow.md) and suggest which skills to run next:

"Based on the outputs from this workflow, you might want to run:

• {skill_1}: {reason based on findings}
• {skill_2}: {reason based on findings}"

4. Recommend refresh cadence

Reference the Refresh Cadence section from workflow.md and suggest when to re-run each phase.

---

Error Handling

Data source failures

If a required data source fails the entire fallback chain:

"I couldn't get {label} automatically. Can you provide this data? Alternatively, I can skip this and note it as a gap."

Template issues

If a template file is missing:

1. Check if it exists at the expected path
2. If missing: inform the user and offer to create the output without a template (free-form based on workflow.md)

Phase failures

If a phase fails during execution:

1. Save any partial output
2. Inform the user what failed and why
3. Ask whether to retry, skip, or stop the workflow

Never fail silently

Every error, gap, or degraded output must be communicated to the user with:

• What happened
• What impact it has on the output quality
• What alternatives are available