Agent skill

whip-simulate

Run multi-agent simulations to measure consistency of non-deterministic behavior. Use when the user wants to A/B test, validate behavioral equivalence, or stress-test outputs at scale.

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/whip-simulate

SKILL.md

Run multi-agent simulations from a user-provided scenario. Concretize the scenario into test cases, spawn agents, and analyze output patterns for consistency.

Input

Extract from $ARGUMENTS:

  • Scenario: what to simulate, compare, or verify
  • --runs N: number of simulation runs (default: 5)
  • --agent: use Agent tool directly instead of whip task create (lightweight, faster, good for quick sims)

Dispatch

  • Whip mode (default): dispatches through /whip-start Team Flow. Each simulation run becomes one task spec handed to /whip-start. IRC selection, workspace, and polling follow /whip-start conventions.
  • Agent mode (--agent): bypasses /whip-start entirely — uses Agent tool directly for lightweight single-shot runs.

If running inside an active whip workspace, use whip workspace view <workspace-name> to get the worktree path for reading code artifacts referenced in the scenario. In whip mode, simulation tasks go in the global workspace (ephemeral — do not pollute the active workspace).

Workflow

1. Concretize

Read any files, git refs, or codebase artifacts referenced in the scenario, then transform it into concrete test cases:

Field Description
Name Short identifier (e.g., deprecated-move-1)
Setup Context the agent receives (file contents, code, instructions)
Action What the agent executes
Output contract Structured format the agent must produce

The output contract is critical — all agents must produce the same structure so results are mechanically comparable:

### Result
- pattern: [short label for the approach taken]
- output:

[code block, JSON, or other structured output]

- decisions: [key judgment calls made]

For A/B comparisons, choose a strategy:

Strategy When to use Agent count
Sequential Outputs are structured (code, configs) — one agent runs A then B N
Isolated Outputs involve judgment or prose — separate agents per version 2N

Present the test plan including:

  • Test cases with output contracts
  • Execution mode (whip or agent)
  • A/B strategy if applicable
  • Total agent count

Wait for user approval before executing.

2. Execute

Whip mode (default)

Hand off dispatch to /whip-start. Prepare one task spec per simulation run and let /whip-start handle IRC, creation, assignment, and monitoring.

Each simulation run becomes one task:

  • Title: sim-{test-case}-{run}
  • Workspace: global
  • Difficulty: easy
  • Description: self-contained prompt (Role + Context + Task + Output Contract)

After all tasks complete, collect outputs and proceed to analysis.

Agent mode (--agent)

Spawn one Agent tool call per run, named sim-{test-case}-{run}.

Each prompt must be self-contained — embed all context inline, not file paths:

  1. Role: "You are a simulation agent. Execute the task and produce structured output."
  2. Context: All file contents and reference material inline
  3. Task: The test case action
  4. Output contract: The exact format to produce

Batching:

  • ≤ 10 runs: spawn all at once with run_in_background: true
  • > 10 runs: groups of 10, next batch after previous completes

3. Analyze

Classify outputs into patterns:

  1. Collect all agent outputs
  2. Group by structural similarity — ignore cosmetic differences (whitespace, comment style, translation wording)
  3. Label each group (A, B, C...)
  4. Identify root cause of each divergent pattern
  5. Flag agents with malformed output as "unclassifiable"

4. Report

## Simulation Report

### Consistency: X/N (Y%)

### Output Patterns
| Pattern | Count | Runs | Description |
|---------|-------|------|-------------|
| A       | 8     | #1-6,#8,#10 | [dominant behavior] |
| B       | 2     | #7,#9 | [variant behavior] |

### Divergence Analysis
For each non-dominant pattern:
- Runs: [list]
- Root cause: [why]
- Severity: cosmetic | functional | breaking
- Diff from dominant: [key differences]

### Summary
- Total: N runs across M test cases
- Dominant pattern: A (X%)
- Key findings: ...
- Recommendation: [if applicable]

Save the full report with raw agent outputs to /tmp/simulate-{slug}-{timestamp}.md and tell the user the path.

Rules

  • Never execute before user approves the test plan
  • Embed all context inline in prompts — no shared state assumptions
  • For A/B comparisons, both versions receive identical inputs
  • Use real file contents from the codebase — never fabricate code
  • In whip mode, use global workspace and delegate dispatch to /whip-start
  • In whip mode, clean up simulation tasks after collecting results: whip task clean
  • In agent mode, each run is single-shot — no follow-up messages or shared state

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