/scenario

name: autoresearch:scenario
description: "Generate edge cases across 12 dimensions from a seed scenario"
argument-hint: "[Scenario: <text>] [Domain: <type>] [Scope: <glob>] [Iterations: N] [--depth <level>] [--focus <area>] [--evals]"
disable-model-invocation: true

EXECUTE IMMEDIATELY.

Parse Arguments

Extract from $ARGUMENTS:

• `Scenario:` — seed scenario description (or full $ARGUMENTS text if no keyword)
• `Domain:` or `--domain` — web, mobile, API, CLI, data pipeline, infrastructure
• `Scope:` or `--scope` — file globs for codebase context
• `Focus:` or `--focus` — specific dimension to prioritize
• `--depth` — shallow (10), standard (20), deep (40+)
• `--format` — markdown (default), json, gherkin
• `Iterations:` or `--iterations` — default 20. "unlimited" for unbounded.
• `--evals`, `--evals-interval N`, `--chain`, `--<subcommand>`

Setup (if Scenario or Domain missing)

AskUserQuestion (single batch): Q1 (Scenario): "Describe the feature/flow to explore" Q2 (Domain): "What domain?" — web app, mobile app, API, CLI, data pipeline, infrastructure Q3 (Scope): "Which files for context?" — suggested globs + entire codebase Q4 (Depth): "How deep?" — quick (10), standard (20), deep (40+), unlimited If all provided → skip.

12 Dimensions

| # | Dimension | Explores | |---|---|---| | 1 | Happy path | Normal successful flows | | 2 | Validation | Input boundaries, types, formats | | 3 | Permissions | Auth, roles, access control | | 4 | Concurrency | Race conditions, deadlocks, ordering | | 5 | State | Invalid transitions, corruption | | 6 | Scale | High volume, large data, many users | | 7 | Failure | Network errors, timeouts, partial failures | | 8 | Security | Injection, abuse, bypass attempts | | 9 | Integration | Third-party failures, API contract violations | | 10 | Data | Null, empty, unicode, injection, overflow | | 11 | UX | Confusion, misuse, accessibility | | 12 | Recovery | Retry, rollback, idempotency |

Establish Baseline

1. Read seed scenario + codebase context 2. Create output directory: `autoresearch/scenario-{YYMMDD}-{HHMM}/` 3. TSV header: `iteration\ttimestamp\tscenario\tdimension\tclassification\tseverity\tdescription` 4. No metric_direction comment (exploration, not optimization)

Iteration Loop

Phase 1: Review

• Read results TSV, check dimension coverage
• Identify underexplored dimensions
• If --focus → prioritize that dimension

Phase 2: Generate

• Pick next dimension (round-robin, or priority if --focus)
• Generate 3-5 specific scenarios for this dimension
• Each: title, dimension, classification, severity, description

Phase 3: Classify

• **new** — genuinely novel edge case
• **extension** — builds on previously found scenario
• **duplicate** — already covered (skip, don't log)

Phase 4: Log

Append new/extension scenarios to TSV. Skip duplicates. Severity: critical/high/medium/low.

Phase 5: Saturation Check

If 3 consecutive iterations produce only duplicates → dimension saturated, move to next. If ALL dimensions saturated → early stop.

Eval Checkpoint

If --evals: check if current_iteration % interval == 0 → run checkpoint.

Bounded Check

If bounded: current_iteration >= max_iterations → exit loop.

Output

• Write `scenarios.md` (organized by dimension, severity-ranked within each)
• Write `edge-cases.md` (flat severity-ranked list)
• `scenario-results.tsv`

Summary

Print: total scenarios (new/extension/duplicate), dimension coverage (X/12 explored), severity distribution.

Eval Checkpoint (--evals flag)

If --evals present:

• Compute interval: floor(max_iterations / 3), min 1. Fixed 10 if unbounded.
• Print: `--- Eval Checkpoint (iterations {X}-{Y}) ---\nNew scenarios: {n} | Dimensions covered: {x}/12 | Saturation: {status}\n{recommendation}\n---`
• If 3+ checkpoints with mostly duplicates → recommend early stop.
• At loop end → full evals summary to evals-summary.md.

Chain Handoff

After completion, write handoff.json: version "2.1.0", source "scenario", timestamp, status, results_tsv path, findings = scenarios by severity, config{scenario, domain, scope}. Invoke next target in --chain order via the `autoresearch` skill's Subcommand routing. Propagate --evals flag.