Math Modeling Preset for DeepSeek Harness

A mathematical modeling preset for DeepSeek Harness, developed on top of dsh-anchored-standard.

It keeps the proven two-phase anchored bootstrap from dsh-anchored-standard:

1. First request: exact Minimal-mode anchor — Minimal persona, bash + str_replace_editor, no injected workspace/skill context.
2. After the first durable promotion signal: a resident toolset that includes math_code, discovery tools, and a rigorous mathematical modeling protocol.

Features

• Anchored-standard compatibility

  • First-round tool schema is the Minimal pair: bash, str_replace_editor.
  • First-round system prompt is the Minimal persona, untouched.
  • First-round auto-injected context is suppressed.
  • Promotion is triggered by the first tool/call or the first assistant/message, whichever comes first (promoteOn: either).
  • Promotion state is derived from durable session events, so resume/reload keeps the correct phase.
  • Compaction-aware phase reset is preserved.

• Math workbench tool: math_code

  • Executes Python code with the scientific Python stack:
    • numpy — numerical arrays and linear algebra
    • scipy — scientific computing, ODE/optimization/interpolation
    • sympy — symbolic derivation, ODE solving, simplification
    • matplotlib — data visualization (Agg backend, saves PNG/SVG)
    • pandas — tabular data processing
    • openpyxl / xlrd — Excel read/write
    • pypdf / pdfplumber — PDF text/table extraction
    • statsmodels / scikit-learn — statistical/data-driven models
  • Returns text output and absolute paths to saved figures.
  • read_image is kept in the promoted resident catalog when the host provides it, so the model can inspect generated plots.

• Rigorous mathematical modeling protocol

  • MATH_PROTOCOL.md is injected as a one-time hint after promotion.
  • The hint resolves the file's absolute path inside the preset directory, so it works even when the current workspace does not contain a math-modeling/ folder.
  • Requires:
    • first-principles modeling
    • complete derivations with motivation for every step
    • multiple modeling perspectives
    • ODE/PDE formulation and explicit vs implicit finite-difference analysis
    • stability, consistency, convergence, and conservation checks
    • boundary-condition inversion / model selection when data are incomplete
    • Pareto multi-objective optimization
    • solver acceptance gate before inversion/optimization
    • unit self-checks, numerical constraint tolerance, and reproducible results

• Long-running task support

  • Background job tools (job_list, job_output, job_kill) are kept in the promoted resident catalog when the host provides them.
  • bash/math_code descriptions tell the model to script long scans and poll logs instead of blocking on one call.

• Jupyter / WSL workbench

  • workbench.ipynb is a ready-to-use notebook template.
  • setup-workbench.sh creates a local virtual environment with the full Python math stack.

Installation

Copy the whole math-modeling directory as a standalone preset id:

dsh_home="${DSH_HOME:-$HOME/.dsh}"
mkdir -p "$dsh_home/.agent-presets"
test ! -e "$dsh_home/.agent-presets/math-modeling"
cp -R math-modeling "$dsh_home/.agent-presets/math-modeling"

Restart DeepSeek Harness, create a blank session, and select Math Modeling (experimental).

Do not switch an active session from a different preset to this one. Create a fresh session.

Workbench Setup

In this repository (or after copying the preset to a project that contains math-modeling/):

bash math-modeling/setup-workbench.sh
math-modeling/.venv/bin/jupyter lab math-modeling/workbench.ipynb

The setup script creates math-modeling/.venv, plus artifacts/, results/, and logs/, and installs:

numpy scipy sympy matplotlib pandas
openpyxl xlrd pypdf pdfplumber
statsmodels scikit-learn
jupyter nbformat ipykernel

If you already have a Python environment with these packages, set MATH_MODELING_PYTHON to that interpreter, or configure pythonPath in the math-tools row of agent.cordis.yml.

How It Works

Phase 1: Minimal anchor

• The first model request is intentionally minimal:
  • Minimal system prompt
  • bash + str_replace_editor
  • no AGENTS.md digest
  • no available-skills catalog injection

This preserves the trajectory anchor measured by dsh-anchored-standard.

Phase 2: Promoted math modeling

After the first durable tool/call or assistant/message, the resident catalog becomes:

• bash
• str_replace_editor
• math_code
• dev_tool_search
• skill_search
• skill_load
• read_image (when available)
• plus any tools explicitly unlocked through dev_tool_search

At the same time, a one-time math-protocol hint tells the model to read math-modeling/MATH_PROTOCOL.md before starting a modeling task.

Compaction behavior

After compaction/end, the session falls back to a controlled phase:

• bash + str_replace_editor
• math_code
• the configured compactionTools

until a new durable promotion signal appears past the compaction boundary.

Updating an Installed Preset

If you already copied math-modeling/ to ~/.dsh/.agent-presets/math-modeling and later update this source, sync the installed copy with:

bash math-modeling/sync-installed.sh

Then restart DeepSeek Harness so the new preset.yml description and files are loaded.

Testing

From the repository root:

npm test

The test suite covers:

• first-request Minimal bootstrap
• promotion from tool/call or assistant/message
• resident catalog including math_code
• compaction phase reset
• math_protocol hint injection
• math_code tool registration and execution flow

Project Structure

math-modeling/
├── README.md
├── LICENSE
├── NOTICE
├── preset.yml
├── agent.cordis.yml
├── MATH_PROTOCOL.md
├── math-tools.mjs          # math_code DSH tool
├── math-protocol.mjs       # post-promotion protocol hint
├── tool-bootstrap.mjs      # anchored two-phase bootstrap
├── compaction-epoch.mjs    # epoch-aware promotion state
├── instruction-hint.mjs
├── dev-tool-search.mjs
├── skill-search.mjs
├── custom-bash.mjs
├── requirements.txt
├── setup-workbench.sh
├── sync-installed.sh
├── write_notebook.py
├── workbench.ipynb
├── artifacts/          # intermediate data
├── results/            # problem*.json / summaries
├── logs/               # long-running job logs
└── test/
    ├── math-tools.test.mjs
    ├── math-protocol.test.mjs
    └── math-bootstrap.test.mjs

Credits

This preset is based on dsh-anchored-standard, including its Minimal-anchored bootstrap, resident-tool discovery pattern, durable promotion tracking, and compaction-aware phase logic.

License

MIT. The preset composition is derived from the DeepSeek Harness Standard preset; the original DeepSeek copyright and MIT notice are retained in NOTICE, and the MIT license is in LICENSE.