dsh-rigorquant

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Unattended-within-a-session, long-running empirical/computational mathematics research for DeepSeek Harness — economics, finance, portfolio construction/optimization, simulation, computational econ/finance.

RigorQuant is an agent preset + bundled skill that turns one DSH session into a context-isolated multi-agent research lab:

• Parallel explorers propose candidate methods (subagent, blank context).
• A ground-truth track re-derives the analytic closed forms, invariants, and bounds for simplified cases — twice, by different means (two independent subagent_ground_truth calls).
• An adversary eliminates routes by counterexample only.
• A four-part check battery (closed-form equality, exact invariants, analytic bounds, statistical hardening) runs BEFORE numerical implementation.
• A meta-validator (rq_check.py) refuses a PASS whose evidence is missing: empty stage outputs, an empty derivations/, a registry with no audit-referenced passed route, or deliverables that do not compile. Its evidence checks read the audit record, not study.json — a study may not vouch for itself.
• Fixed-seed + LLN conventions for stochastic work.
• A jacobian MCP escalation lane (opt-in; Lean as a manual external lane) settles proof-critical claims before implementation.
• PASS → auto-implement and proceed; BLOCKED → 3 rounds of the same gap → strongest derivation + exact gap; BUDGET → 5 rounds → checkpoint + report.

The operating pattern adapts Shanmu Jin's Crouzeix-conjecture run (prompt, Lean audit) and Terence Tao's blueprint/equational-theories projects to numerical work. Full design record: docs/architecture.md.

"Unattended", precisely: the framework runs unattended within one live session. Crossing a session boundary disarms the goal; one human turn ("continue") re-arms it. It does not continue autonomously across restarts.

Install

Two install forms:

Bundle (skill layer) — one command, makes the rigorquant skill available to every session of a profile; the repo declares a dsh.bundle manifest so the ecosystem's dsh plugin add path works:

dsh plugin --profile web add github:linxichen/dsh-rigorquant

Preset (full framework) — the RigorQuant agent preset (persona + orchestration + tools) with the bundled skill:

git clone https://github.com/linxichen/dsh-rigorquant
cd dsh-rigorquant
./install.sh                    # installs the preset + skill + compute lane
# ./install.sh --skill-only     # or just the rigorquant skill, for any preset

Start a new DSH session and pick the RigorQuant preset. Then:

rigorquant: derive and validate a method for [problem], simplified cases first, before any numerical implementation.

Compute lane (one-time)

The pinned uv compute lane is installed at $DSH_HOME/share/rigorquant/env by install.sh (see env/README.md). The jacobian escalation lane ships disabled and pinned (jacobian@0.12.0): enable the mcp-jacobian row, and the framework asks for approval before any one-time provisioning (npx -y jacobian@0.12.0 upgrade, or the Lean toolchain via the skill's scripts/provision-lean.sh). See mcp/jacobian.md.

Repository layout

package.json                dsh.bundle manifest (dsh plugin add support)
cordis.patch.yml            bundle patch: registers the rigorquant skill
agent-presets/rigorquant/   preset composition + persona + bundled skill
  skills/rigorquant/        SKILL.md + references/ + scripts/ + schemas/
  .../scripts/rq_check.py   the meta-validator (single canonical copy)
  .../schemas/              study.json + registry.json JSON Schemas, which the
                            validator loads — so schema and checker cannot drift
env/                        pinned uv compute lane (sympy/cvxpy/hypothesis/…)
mcp/jacobian.md             escalation lane wiring
docs/architecture.md        grilled decision record + sources
tests/                      the validator's test suite (see Testing below)
studies/                    one study folder per task (Mode B; a checkout's own
                            live studies — not shipped in the npm bundle)

Testing

The validator has a test suite, and its centrepiece is a forged study — empty derivations, empty stage outputs, a one-line adversary report, and a paper whose body reads "This paper says nothing." It must FAIL. A framework whose honesty gate is not itself tested is a framework that certifies whatever it is handed.

uv sync --frozen --project env
uv run --frozen --project env python -m pytest tests/ -q

tests/test_repo_consistency.py covers the other half: one validator, one schema, documented commands that resolve, and layout blocks that match the filesystem. That is the defect class this repository actually produces.

What a green validator means: nothing declared is missing, and the deliverables build. It does not mean the mathematics is right — that stays with the check battery, the independent ground-truth track, and the adversary.

Studies

A study is one self-contained rigorquant task with an identical folder structure everywhere: durable deliverables at the study root (study.json, STUDY.md, registry.json, journal.md, derivations/, audits/, artifacts/) are meant to be committed; all scratch lives in a gitignored interim/. Two modes, implied by location:

• One study per repo — study.json at the repo root.
• Multiple studies per repo — studies/<slug>/study.json; the roster is studies/*/study.json.

Intake detects an existing study and continues it silently; a new study asks one question (mode + slug) and never asks again. See docs/architecture.md §12.

Publishing

This repo is a community DSH plugin distribution (bundle + preset + skill form): it declares a dsh.bundle manifest in package.json, is tagged dsh-plugin, and is discoverable by the ecosystem's topic-based indexes — see dsh-find-plugins and the awesome-deepseek-harness list for the conventions.

MIT License.