// Looping research pipeline — hypotheses deepen via Ouros sessions, artifacts flow back
SDLC pipeline — assess plan prepare execute validate evolve
Get a project agent-ready — readiness setup + mode selection
Pre-implementation failure analysis — first-principles retrospective with tiger/elephant risk classification
Structural + semantic code review — bugbot L1/L2 facts + tldr analysis + Claude Code reasoning
Extend the ouros sandbox with new external functions (bridge functions, security policy, tests)
| name | autonomous-research |
| description | Looping research pipeline — hypotheses deepen via Ouros sessions, artifacts flow back |
| user-invocable | true |
Orchestrate. Never research directly. Workers research inside Ouros; you plan, decompose, delegate, synthesize. Carry: hypothesis status, confidence scores, iteration number, ouros session names. No web searches yourself. Workers share state through Ouros sessions — not through your context window.
Pipeline: ASSESS → PLAN → PREPARE → EXECUTE → VALIDATE → EVOLVE → (loop to PLAN) Terminates when: all hypotheses confidence >= 0.8, context budget >= 75%, or user stops.
ASSESS
Read research question. Classify scope: focused — single question, 1-3 hypotheses, 2-3 iterations exploratory — broad topic, 5+ hypotheses, open-ended comparative — N alternatives to evaluate, structured comparison Estimate iteration budget: focused ~3, exploratory ~6, comparative ~4. Check available tools: ouros harness, exa, nia, bloks. Create ouros session name: {topic-slug} (e.g., "attention-scale", "auth-patterns").
PLAN
Hypothesis contract first — what would "understanding this" look like?
research_contract.json:
{ "question": "How do attention patterns change with model scale?", "scope": "exploratory", "iteration": 1, "ouros_session": "attention-scale", "hypotheses": [ {"id": "H-001", "text": "Attention head specialization increases with scale", "confidence": 0, "status": "pending", "sources": 0, "depends": [], "worker": null} ], "accumulated_findings": [], "iterations_completed": 0 }
Confidence: 0 (unknown) → 0.3 (speculative) → 0.6 (supported) → 0.9 (well-established). Status: pending, researching, supported, refuted, inconclusive, superseded.
On iteration 2+: read EVOLVE output. Add new hypotheses from workers' new_questions. Supersede refuted hypotheses. Carry all findings forward.
PREMORTEM
Bias check: confirmation bias — only searching for supporting evidence? source bias — over-relying on one source type? blind spots — obvious sub-questions not covered? Present to user. BLOCK on blind spots, WARN on bias, PASS otherwise.
PREPARE
Front-load what's already known:
BLOKS_CONTEXT=$(bloks context .) BLOKS_CARDS=$(bloks search {topic keywords}) PRIOR_VARS=$(python tools/ouros_harness.py --session {topic} --list-vars) # iteration 2+
On iteration 1: mostly empty. Workers discover. On iteration 2+: Ouros session has accumulated variables from prior iterations. Workers --load the session and build on prior state.
EXECUTE
Workers research INSIDE Ouros. Same contract pattern as /autonomous — worker does the work, orchestrator just provides hypothesis + context + where to report.
Worker prompt — structured JSON, same shape as /autonomous:
{ "role": "research", "hypothesis": {"id": "H-001", "text": "Attention head specialization increases with scale"}, "context": { "bloks_context": "{verbatim bloks output}", "bloks_cards": [], "prior_findings": "{accumulated_findings from research_contract.json}", "available_tools": "exa_search, nia_universal, nia_search, nia_web, llm_call, agent_call, read_file, write_file" }, "bounds": { "ouros_session": "attention-scale", "ouros_storage": "continuum/research/attention-scale", "ouros_load": false, "ouros_fork": "attention-scale-H001", "max_sources": 5, "source_types": ["arxiv", "official_docs", "reference_impl"], "artifact_path": "continuum/research/attention-scale/artifacts/H-001-iter1.md" }, "output": "continuum/research/attention-scale/reports/H-001-iter1.json" }
bounds.ouros_load: true on iteration 2+ (worker resumes session, sees prior variables). bounds.ouros_fork: set when parallel workers need independent variable space.
Worker flow (the worker decides HOW to research — orchestrator just says WHAT):
The worker is an agent with full autonomy over research strategy. It writes the program, picks the queries, chooses which tools to use, decides how to synthesize. The orchestrator only controls: which hypothesis, which session, where to report.
Sharing between workers (same iteration): Fork: --fork {topic}-{hypothesis-id}. Independent variable space, no cross-contamination.
Sharing between iterations: Load: --load resumes base session. Iteration 2 workers see iteration 1 variables. Accumulated findings grow in the REPL heap — zero orchestrator tokens.
Report — uniform JSON, same principle as /autonomous (every field filled):
{ "hypothesis": "H-001", "result": "supported | refuted | inconclusive", "artifact_path": "continuum/research/attention-scale/artifacts/H-001-iter1.md", "source_count": 5, "synthesis_preview": "3 papers support specialization increasing with scale...", "confidence_delta": +0.3, "new_questions": ["Does this hold for MoE architectures?"], "bloks_written": ["bloks learn transformers "attention heads specialize linearly with log(params)""], "corrections": [], "ouros_vars_created": ["papers_H001", "synthesis_H001", "sources_H001"] }
synthesis_preview ~300 chars. Full synthesis in the artifact file. ouros_vars_created tells the next iteration what variables are available to --load. The orchestrator carries forward: artifact_path, confidence, new_questions. Not raw findings.
VALIDATE
After each iteration's workers complete: artifact check: do artifact files exist at reported paths? confidence calibration: read artifacts, is confidence justified by evidence? consistency: do findings from parallel workers contradict? coverage: what % of hypothesis space is at confidence >= 0.6?
Update research_contract.json: hypothesis status, confidence, source counts. Write: continuum/research/{topic}/validation/iteration-{N}.json
EVOLVE — THE LOOP
Decision gate: ALL hypotheses confidence >= 0.8 → SYNTHESIZE FINAL Context budget >= 75% → SYNTHESIZE FINAL (save room for handoff) User says stop → SYNTHESIZE FINAL Otherwise → increment iteration, loop to PLAN with new hypotheses
SYNTHESIZE FINAL: Load ouros session: --list-vars to see all accumulated research variables. Write final synthesis program that reads all findings_* vars and produces: continuum/research/{topic}/findings.md — telegraphic artifact Structure: question, findings per hypothesis, confidence, open questions, sources. This is what /autonomous consumes, handoffs reference, user reads. Persist: research_contract.json has full state for /resume_handoff.
STATE
continuum/research/{topic}/ research_contract.json hypotheses + lifecycle + iteration history findings.md final synthesis (what others consume) artifacts/ per-hypothesis per-iteration evidence files reports/ worker reports (compact JSON) validation/ iteration validation results ouros session state REPL heap (all raw data, persisted by harness)
RESUME
Read research_contract.json. Show: hypotheses N/total, avg confidence, iterations done. Load ouros session: --list-vars. Show accumulated variable count. Continue from last EVOLVE decision gate.
RULES
Never research directly — workers run Ouros programs Ouros is the research medium — results stay in heap, only artifacts cross back Workers share via sessions — --load for sequential, --fork for parallel Hypotheses before search — know what you're looking for Primary sources — papers, docs, repos. Not blogs, not LLM summaries. Confidence is earned — evidence count + quality justifies the score Knowledge accumulates — Ouros session grows, orchestrator context stays flat Each iteration deeper not wider — wider = new hypotheses, deeper = more evidence Write to bloks — future sessions benefit: bloks learn {topic} "{finding}" Budget-aware — synthesize at 75% context, hard stop at 85% Max iterations: focused 5, exploratory 10, comparative 7. Then force-synthesize.
