prune-workflows

Prune Workflows Skill

VCS Provider

This skill's safeguards use VCS operations internally (open PR detection). The orchestrate handler manages VCS provider dispatch automatically. No gh/glab/az commands needed — the MCP server handles provider dispatch.

Overview

Bulk-cancel stale non-terminal workflows that have accumulated in the pipeline. Wraps the prune_stale_workflows orchestrate action with an interactive dry-run-then-confirm UX so the user always sees the candidate set before any state mutates.

Pruning is a maintenance operation -- not a workflow phase. It produces workflow.pruned events alongside the standard workflow.cancelled events emitted by the underlying cancel path, so downstream views can distinguish user-intent cancellations from batch cleanup.

Triggers

Activate this skill when:

• User runs prune command
• User says "prune workflows", "clean stale workflows", "pipeline cleanup"
• mcp__exarchos__exarchos_view({ action: "pipeline" }) shows many inactive workflows the user wants to clear in bulk

Prerequisites

• An exarchos state directory with one or more non-terminal workflows
• For safeguard checks against live PRs: gh available in PATH (the orchestrate handler shells out)
• For safeguard checks against branch activity: git available in PATH

Process

Step 1: Dry-Run Preview

Always start with dryRun: true. This call performs candidate selection and safeguard evaluation but does not mutate any workflow state.

mcp__exarchos__exarchos_orchestrate({
  action: "prune_stale_workflows",
  args: {
    dryRun: true
  }
})

The response shape is:

{
  candidates: [
    { featureId: string, phase: string, workflowType: string, stalenessMinutes: number }
  ],
  skipped: [
    { featureId: string, reason: "open-pr" | "active-branch" }
  ]
}

Optional args:

• thresholdMinutes -- staleness cutoff. Default is 10080 (7 days).
• includeOneShot -- whether to include oneshot workflows in the candidate set. Default true.
• force -- not relevant in dry-run; only honored in apply mode (Step 4).

Step 2: Display Candidate Table

Render a table to the user so they can review what would be pruned. Use this format:

## Prune Candidates (3)

| Feature ID | Type | Phase | Stale (min) |
|---|---|---|---|
| feat-old-experiment | feature | implementing | 14430 |
| oneshot-typo-fix | oneshot | plan | 9120 |
| debug-flaky-test | debug | investigate | 8650 |

## Skipped by Safeguards (2)

| Feature ID | Reason |
|---|---|
| feat-active-pr | open-pr |
| feat-recent-work | active-branch |

If candidates.length === 0 AND skipped.length === 0, output:

No stale workflows to prune. Pipeline is clean.

Then exit -- no further action.

Step 3: Prompt for Confirmation

Ask the user one of three choices:

proceed -- prune the listed candidates (skipped workflows stay) abort -- exit without changes force -- bypass safeguards and prune skipped workflows too

Wait for explicit user input. Do not auto-proceed.

Step 4a: Apply (proceed)

On proceed, invoke the same action with dryRun: false. Pass through thresholdMinutes and includeOneShot if the user set them in Step 1.

mcp__exarchos__exarchos_orchestrate({
  action: "prune_stale_workflows",
  args: {
    dryRun: false
  }
})

The response now includes a pruned array:

{
  candidates: [...],
  skipped: [...],
  pruned: [
    { featureId: string, previousPhase: string }
  ]
}

Each entry in pruned corresponds to a workflow that transitioned to cancelled and emitted a workflow.pruned event.

Step 4b: Apply (force)

On force, set force: true. Safeguards are bypassed but the bypass is recorded in the workflow.pruned event payload as skippedSafeguards: [...] so the audit trail is intact.

mcp__exarchos__exarchos_orchestrate({
  action: "prune_stale_workflows",
  args: {
    dryRun: false,
    force: true
  }
})

In force mode, workflows that were in the skipped list during dry-run are now eligible for pruning.

Step 4c: Apply (abort)

On abort, exit without invoking the action a second time. The dry-run has not mutated any state.

Step 5: Report Results

After the apply call returns, summarize the outcome:

## Prune Complete

**Pruned:** 3 workflows transitioned to cancelled
**Skipped:** 2 workflows preserved by safeguards
**Force bypass:** no

### Pruned Workflows
- feat-old-experiment (was: implementing)
- oneshot-typo-fix (was: plan)
- debug-flaky-test (was: investigate)

If force was used, also list any safeguards that were bypassed:

### Safeguards Bypassed
- feat-active-pr: open-pr
- feat-recent-work: active-branch

Safeguards Explained

Two safeguards run automatically in the orchestrate handler before each cancel:

Safeguard	Behavior	Reason key
Open PR	Skip if gh pr list --head <branch> --state open returns any results	open-pr
Recent commits	Skip if git log --since "24 hours ago" origin/<branch> shows commits	active-branch

A workflow without a branchName in state (e.g., abandoned at ideate/plan before delegation) cannot have a PR or branch activity, so safeguards short-circuit and the workflow is eligible for pruning.

force: true bypasses both checks but does not bypass the audit -- the bypassed safeguard names are recorded in the workflow.pruned event payload.

Anti-Patterns

Don't	Do Instead
Skip the dry-run step	Always start with dryRun: true so the user sees candidates first
Auto-confirm without showing the table	Render the candidate table and wait for explicit user input
Use force: true by default	Reserve force for cases where the user has explicitly opted in
Manually call cancel for each stale workflow	Use this skill -- it batches the cancel loop and emits the workflow.pruned audit event
Run on every session	Pruning is a maintenance operation; run when pipeline accumulation is observable

Examples

Example 1: Clean dry-run, user proceeds

> prune

## Prune Candidates (2)

| Feature ID | Type | Phase | Stale (min) |
|---|---|---|---|
| feat-old-spike | feature | plan | 12880 |
| oneshot-readme-tweak | oneshot | implementing | 9700 |

## Skipped by Safeguards (0)

(none)

Proceed? (proceed/abort/force)

> proceed

## Prune Complete

**Pruned:** 2 workflows transitioned to cancelled
**Skipped:** 0
**Force bypass:** no

### Pruned Workflows
- feat-old-spike (was: plan)
- oneshot-readme-tweak (was: implementing)

Example 2: Safeguard skip, user forces

> prune

## Prune Candidates (1)

| Feature ID | Type | Phase | Stale (min) |
|---|---|---|---|
| feat-stale-no-pr | feature | implementing | 11200 |

## Skipped by Safeguards (1)

| Feature ID | Reason |
|---|---|
| feat-stale-with-pr | open-pr |

Proceed? (proceed/abort/force)

> force

## Prune Complete

**Pruned:** 2 workflows transitioned to cancelled
**Skipped:** 0
**Force bypass:** yes

### Pruned Workflows
- feat-stale-no-pr (was: implementing)
- feat-stale-with-pr (was: implementing)

### Safeguards Bypassed
- feat-stale-with-pr: open-pr

Example 3: Empty pipeline

> prune

No stale workflows to prune. Pipeline is clean.

Schema Discovery

For the canonical argument schema and any future fields, use:

mcp__exarchos__exarchos_orchestrate({
  action: "describe",
  actions: ["prune_stale_workflows"]
})

Exarchos Integration

The prune_stale_workflows action emits two events per pruned workflow:

• workflow.cancelled (auto-emitted by the underlying handleCancel path)
• workflow.pruned (emitted by this handler with triggeredBy: 'manual' and optional skippedSafeguards)

Both are projected into the workflow state's _events array by the standard projection. Downstream views can filter on workflow.pruned to identify batch cleanups versus user-initiated cancels.