dlab-cli

# decision-lab (dlab) dlab runs autonomous coding agents in frozen Docker environments with domain-specific skills and parallel subagents. You package the environment, prompts, and skills into a **decision-pack**, point it at data, and get back reports and recommendations that hold up to scrutiny. ## When to use this skill - Creating a new decision-pack (interactive or programmatic) - Designing agent system prompts for data science workflows - Understanding how parallel agents, consolidators, and retry protocols work - Analyzing a completed session's logs, outputs, and artifacts - Running or configuring dlab CLI commands ## Workflow overview 1. **Create a decision-pack** — scaffold with `dlab create-dpack` wizard or `generate_dpack()` programmatically 2. **Design agents** — write orchestrator, subagent, and parallel agent configs 3. **Run a session** — `dlab --dpack <path> --data <data> --prompt "..."` 4. **Monitor** — `dlab connect <work-dir>` (live TUI) or `dlab timeline <work-dir>` (Gantt chart) 5. **Analyze results** — browse session directory, logs, parallel instance outputs ## Key concepts **decision-pack**: A directory containing `config.yaml`, `docker/`, and `opencode/` (agents, skills, tools, permissions). Everything an agent needs to run. **Orchestrator** (`mode: primary`): Coordinates the workflow, spawns parallel agents, evaluates results, writes reports. One per decision-pack. **Subagents** (`mode: subagent`): Execute focused tasks. Each runs ONE strategy per run. If it fails, it writes diagnosis and stops — the orchestrator coordinates retries. **Consolidator**: Auto-generated read-only agent that compares parallel instance results. Never picks a winner. **Parallel exploration**: Fan out multiple agents with structurally diverse approaches (different priors, models, data prep). Check if they agree before recommending. ## Critical methodology rules These are non-negotiable for any data science agent system: 1. **Never fabricate** — no mocking data, no silently swallowing errors, no `try/except: value = 0` 2. **Understanding over fitting** — a model that doesn't converge is evidence, not failure 3. **Know when to stop** — hard round limits, conflict detection, degenerate problem reports 4. **Templates, not implementations** — no concrete numbers in prompts, use `<PLACEHOLDER>` syntax 5. **Uncertainty, not point estimates** — always report intervals, distinguish model vs structural uncertainty 6. **Recommendations must be computed** — no napkin math, multiple scenarios, realistic actions 7. **Document everything** — including failures, two reports (business + technical) Load `references/agent-design.md` for the full methodology guide. ## References Load these as needed — don't read all upfront: - `references/agent-design.md` — Full methodology: anti-fabrication, retry protocol, epistemic humility, conflict detection, prompt design, parallel exploration, degenerate problems, agent prompt structure, runtime directory layout, YAML config - `references/create-dpack.md` — Programmatic decision-pack creation: `generate_dpack()` API, config keys, package managers, permissions, Modal integration - `references/create-dpack-interactive.md` — Interactive wizard guide: how to interview a user and call `generate_dpack()` with the right config - `references/run-analyzer.md` — Session analysis: directory layout, log format (NDJSON events), how to navigate parallel runs, what to look for