cpf-workflow-runner

name

cpf-workflow-runner

description

Run, resume, and manage checkpointflow workflows interactively. Use this skill whenever the user wants to run a workflow, execute a YAML pipeline, resume a paused run, check workflow status, cancel a run, or interact with cpf at runtime. Also triggers on phrases like "run the workflow", "execute this", "resume the run", "what's the status", or when the user provides input for a waiting workflow step.

cpf-workflow-runner

Run checkpointflow workflows interactively, handling pause/resume cycles.

Core concept: Audience-driven dispatch

Checkpointflow workflows pause at await_event steps. Each pause has an audience field in the envelope's wait block that tells you who should act:

• audience: "agent" — The prompt is your work assignment. Do the work (read code, write files, run tools), produce JSON matching input_schema, and resume immediately. Never present agent pauses to the user.
• audience: "user" — Present the prompt and collect input from the user via structured prompts, then resume with their answers.

This is the only dispatch you need. The envelope tells you everything — no need to read the workflow YAML to figure out what to do.

Agent pause example

{
  "wait": {
    "audience": "agent",
    "event_name": "codebase_analysis",
    "prompt": "Analyze the codebase for feature \"run-delete\" in area \"gui\".\nScan src/checkpointflow/gui/ and identify source files, tests, dependencies...",
    "input_schema": {
      "type": "object",
      "required": ["area", "source_dir", "notes"],
      "properties": {
        "area": { "type": "string" },
        "source_dir": { "type": "string" },
        "notes": { "type": "string" }
      }
    }
  }
}

When you see this:

1. Read the prompt — it describes exactly what to do
2. Do the work (read files, explore code, run tools, etc.)
3. Build JSON matching input_schema with your real findings
4. Resume immediately: cpf resume --run-id <id> --event codebase_analysis --input '<json>'

User pause example

{
  "wait": {
    "audience": "user",
    "event_name": "scope_choice",
    "prompt": "Feature: run-delete\nHow should we scope this?\n- full: Complete implementation...\n- minimal: Core logic only...",
    "input_schema": {
      "type": "object",
      "required": ["scope"],
      "properties": {
        "scope": { "type": "string", "enum": ["full", "minimal", "spike", "cancel"] }
      }
    }
  }
}

When you see this:

1. Present the prompt context to the user
2. Collect their input via structured prompts
3. Resume: cpf resume --run-id <id> --event scope_choice --input '{"scope": "full"}'

Critical: Collect input through structured prompts, not chat

Every time the workflow needs input from the user — whether collecting workflow inputs before cpf run, gathering event data at an await_event pause, or confirming a cancellation — present structured choices using whatever interactive prompt tool is available in your environment (e.g., a question/answer UI, a selection picker, a form). The user should experience the workflow as a guided flow where they pick options and provide values through structured prompts, not by typing free-form messages into chat.

Do not say "what would you like to do?" and wait for a text reply. Always present choices and collect input through a structured prompt tool.

Running a workflow

Step 1: Discover and understand the flow

Use cpf flows to list available workflows, then cpf flows --detail <id> to see everything about a specific flow — description, required/optional inputs with types and descriptions, step overview, and a ready-to-use run command:

cpf flows                        # List all available workflows
cpf flows --detail <id>          # Full detail: inputs, steps, run command

The detail output tells you exactly what inputs to collect and gives you the cpf run command template. No need to read the workflow YAML.

Step 2: Collect inputs and execute

Use the inputs section from cpf flows --detail to collect values from the user via structured prompts. Read the description field on each input — it explains what the value should be and often includes examples or hints (e.g., "Directory containing the tools/ subdirectory").

For inputs you can infer from context, do so without asking:

• repo_dir → current working directory
• artifact_dir → create a temp directory
• author → git config user.name
• Paths referencing the current project → resolve from the working directory

Only prompt the user for inputs that are genuinely ambiguous. For each prompted input, build options using this priority:

1. enum values → one option per enum value (the user picks one)
2. type: boolean → two options: "Yes" / "No"
3. examples array → one option per example value (user picks one or types custom)
4. No examples → craft 2 realistic example values from the description

Never create placeholder options like "Type value" or "Enter value". Every option should be a real, plausible value the user might choose.

Then run:

cpf run -f <path> --input '<json>'

Parse the JSON envelope from stdout. The ok, status, and exit_code fields tell you what happened.

Step 3: Handle the result

Based on exit_code:

Exit code	What happened	What to do
0	Completed	Show the result to the user. Done.
40	Waiting for input	Enter the interactive loop (dispatches on audience).
10	Validation error	Show error.message. Check inputs match the schema.
30	Step failed	Immediately run cpf inspect --run-id <id>, read the stderr_path from the failed step result, and diagnose the root cause before reporting to the user.
80	Unsupported step	Tell the user this step kind isn't implemented yet.
Other	Error	Show the error and suggest next steps.

Interactive loop (await_event handling)

This is the core of the skill. When a workflow pauses (exit code 40), the envelope contains a wait block. Dispatch on wait.audience:

Agent audience (wait.audience == "agent")

The prompt is your work assignment. Do not present it to the user.

1. Read wait.prompt — it describes the work to do, with full context from previous steps already interpolated in.
2. Do the work — read code, write files, run tools, explore the codebase — whatever the prompt asks for.
3. Build event JSON matching wait.input_schema with your real results.
4. Resume immediately:

   cpf resume --run-id <run_id> --event <event_name> --input '<event_json>'
   

5. Parse the new envelope and loop back.

User audience (wait.audience == "user")

Present the checkpoint to the user and collect their input.

1. Read the wait block — extract prompt, input_schema, event_name, and run_id.

2. Collect input — map each field in input_schema to a structured prompt using the same option-building rules from Step 2 above.

   Multiple fields: If input_schema has multiple required fields, ask them together in a single prompt call when possible. Optional fields can be asked in a follow-up or skipped.

   Use the prompt from the wait block as context when presenting the questions — it explains what the user is deciding on.

3. Build the event JSON from the user's answers. Map each answer back to the field name in input_schema. Make sure the result matches the schema types (strings stay strings, numbers get parsed).

4. Resume the workflow:

   cpf resume --run-id <run_id> --event <event_name> --input '<event_json>'
   

5. Parse the new envelope and loop back:

   • If exit code 0 → done, show result
   • If exit code 40 → another await_event, dispatch on audience again
   • If error → show it

Keep looping until the workflow completes, fails, or the user wants to stop.

Status and inspection

When the user asks about a run:

cpf status --run-id <run_id>    # Quick status check
cpf inspect --run-id <run_id>   # Full execution history

If the user doesn't remember the run ID, check recent output from previous commands in the conversation — the run_id is in every envelope.

Cancelling a run

When the user wants to cancel a waiting run:

cpf cancel --run-id <run_id> --reason "<reason>"

Use a structured prompt to confirm before cancelling, and ask for a reason if not provided.

Web dashboard

Launch the GUI to browse workflows and runs visually:

cpf gui                    # opens http://localhost:8420
cpf gui --port 9000        # custom port

Never modify workflow files during an active run

cpf checksums the workflow YAML when a run starts and verifies it on every resume. If you edit the workflow file while a run is in progress, the next cpf resume will fail with ERR_VALIDATION_WORKFLOW ("Workflow file has changed since the run started"). This forces you to abandon the run and start over.

If you need to fix the workflow YAML mid-session, finish or cancel the active run first.

Parallel agents with sequential callbacks

Some workflows have multiple sequential await_event steps that are all audience: "agent" — for example, 4 specialist reviews that each get their own callback. The optimal pattern:

1. Launch all agents in parallel (background agents) when you first see the sequence.
2. Write event JSON files as each agent completes — don't hold results in memory.
3. Resume callbacks sequentially as the workflow reaches each one, using the pre-written files.

Agents may finish out of order. That's fine — queue their results in files and feed them to the workflow in the order it expects.

Windows path handling

On Windows, backslashes in file paths break inline JSON (--input '{"file": "C:\\..."}' causes JSON parse errors). Two options:

• Use @file references (preferred): write event JSON to a file, then --input @path/to/event.json
• Use forward slashes in inline JSON: "C:/Users/thoma/..." — cpf and Python accept these on Windows

When using @file, make sure you know the actual Windows path — different tools may resolve /tmp/ to different locations (e.g., C:\tmp\ vs C:\Users\...\AppData\Local\Temp\).

Tips

• Agent pauses are auto-resume — never present them to the user. Do the work silently and resume.
• The prompt has full context via interpolation — you do not need to read the workflow YAML to understand the work. The prompt already references previous step outputs.
• If you can't complete agent work, still provide valid JSON with honest status fields (e.g., lint_ok: false, implemented: false). The workflow's branching logic handles failure paths.
• Always parse the JSON envelope — don't rely on exit codes alone. The envelope has richer information.
• Exit code 40 is not an error. Don't treat it as a failure or apologize for it.
• When showing results to the user, format them nicely — don't dump raw JSON unless they ask for it.
• If a workflow has multiple sequential await_event steps (like a quiz), keep the loop going smoothly without re-explaining the process each time.
• The --input flag accepts either inline JSON ('{"key": "value"}') or a file reference (@path/to/file.json). Prefer @file for any input containing file paths or long strings.