autoresearch
Autonomous iteration loop: modify, verify, keep/discard against any metric
Generate edge cases across 12 dimensions from a seed scenario
> /plugin marketplace add uditgoenka/autoresearch > /plugin install autoresearch@autoresearch
How it fires
How this command gets triggered: by you, by Claude, or both.
/scenarioContext preview
What this command does when you run it.
Generate edge cases across 12 dimensions from a seed 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]"
EXECUTE IMMEDIATELY.
Extract from $ARGUMENTS:
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.
| # | 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 |
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)
Append new/extension scenarios to TSV. Skip duplicates. Severity: critical/high/medium/low.
If 3 consecutive iterations produce only duplicates → dimension saturated, move to next. If ALL dimensions saturated → early stop.
If --evals: check if current_iteration % interval == 0 → run checkpoint.
If bounded: current_iteration >= max_iterations → exit loop.
Print: total scenarios (new/extension/duplicate), dimension coverage (X/12 explored), severity distribution.
If --evals present:
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. Propagate --evals flag.
Turn Claude Code, OpenCode, or OpenAI Codex into a relentless improvement engine. Based on Karpathy's autoresearch — constraint + mechanical metric + autonomous iteration = compounding gains.
Repo: uditgoenka/autoresearch
Autonomous iteration loop: modify, verify, keep/discard against any metric
Analyze iteration results: trends, plateaus, regressions, recommendations
Scout codebase and auto-generate docs — or a navigable wiki knowledge base — with validation-fix loop