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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_truthcalls). - 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.
- 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.
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-rigorquantPreset (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 presetStart 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.
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
scripts/provision-lean.sh). See mcp/jacobian.md.
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
env/ pinned uv compute lane (sympy/cvxpy/hypothesis/…)
mcp/jacobian.md escalation lane wiring
docs/architecture.md grilled decision record + sources
studies/ one study folder per task (Mode B; this checkout's
live studies — not shipped in the npm bundle)
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.jsonat the repo root. - Multiple studies per repo —
studies/<slug>/study.json; the roster isstudies/*/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.
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.