run-hypothesis

# Hypothesis Trajectory Engine This skill invokes the `run_hypothesis` tool from the AgentLab MCP server (`mcp__agentlab__run_hypothesis`), which runs a 6-phase multi-agent reasoning pipeline that turns rough intuitions into formal, testable hypotheses. ## Pipeline | Phase | Agent | Mode | Runs | |-------|-------|------|------| | 0 | `formalizer` — maps every informal term to a formal mathematical object | deep (Opus) | solo | | 1 | `existence_prover` — proves the critical condition/moment exists (EVT, IVT) | smart | parallel | | 2 | `constraint_closer` — writes the full conservation/constraint balance sheet | smart | parallel | | 3 | `gap_finder` — quantifies the gap between current theory and observations | smart | parallel | | 4 | `mechanism_designer` — designs a mechanism that closes the gap | deep (Opus) | sequential | | 5 | `deeper_connector` — connects mechanism to deeper principles, lists predictions | smart | sequential | | ∞ | `synthesizer` — structured final report written to `workspace_dir/final_report.md` | smart | sequential | Phases 1–3 run in parallel threads. Each phase reads prior phase outputs. If a phase writes a `compute.py`, the runner executes it with `python3` (numpy, scipy, sympy available) and injects results into the next phase. ## How to invoke **Directly:** ``` /run-hypothesis prove that special relativity works /run-hypothesis make Black-Scholes better /run-hypothesis why do L-amino acids dominate in biology /run-hypothesis improve the SEIR epidemic model ``` **Claude auto-invokes this skill** when the user asks to: - Prove or verify a theory - Formalize a hypothesis or rough idea - Improve an existing model or framework - Explore an open research question - Find the mechanism behind an observed phenomenon ## Instructions 1. Parse `$ARGUMENTS` to extract the problem statement and any domain hint (e.g. "quantitative finance", "theoretical physics", "biology", "economics") 2. If no domain is specified, infer it from the problem content 3. Call `mcp__agentlab__run_hypothesis` with: - `problem`: the full problem statement from `$ARGUMENTS` - `domain`: inferred or specified domain - `workspace_dir`: `hypothesis_workspace` (relative, in current repo) 4. The tool returns a JSON object with phase outputs and a synthesis section 5. Present the synthesis report clearly to the user, noting which workspace files were written ## Output The tool writes phase findings to `hypothesis_workspace/phaseN_<name>/findings.md` and a final report to `hypothesis_workspace/final_report.md`. Reference these files if the user wants to dig into a specific phase. ## Domain examples | Problem type | Domain hint | |---|---| | Options pricing, volatility surface | `quantitative finance` | | Particle physics, cosmology | `theoretical physics` | | Protein folding, amino acids | `biochemistry` | | Epidemic modeling | `epidemiology` | | Market microstructure | `financial economics` | | Neural synchrony, consciousness | `computational neuroscience` | | Economic tipping points | `complexity economics` |