📦 deps(thirdparty): update snapshots

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+---
+name: doc2math
+description: Convert narrative technical documents into grounded Mathematical Problem Specifications with variables, constraints, objectives, and uncertainty.
+risk: safe
+source: community
+date_added: "2026-05-31"
+---
+
+# DOC2MATH — Document-to-Mathematics Problem Specification
+
+## When to Use This Skill
+
+- "Formalize this problem statement into math"
+- "Extract the mathematical structure from this research paper section"
+- "What variables, constraints, and objectives are in this spec?"
+- "Convert this word problem to a structured MPS"
+- "Find what's missing in this problem formulation"
+
+## Zero-Inference Protocol (Mandatory)
+
+1. **Closed World** — if it is not stated in the document, it does not exist in output
+2. **Grounding Rule** — every element must cite the exact source phrase (`"evidence"` field)
+3. **No Silent Filling** — unknown values use `null`; ambiguous types use `"ambiguous"`
+4. **Inference Tagging** — structural inferences tagged `"inferred": true` with `"inference_basis"`
+5. **MISSING Markers** — elements mentioned but insufficiently defined get `"status": "MISSING"` with `"missing_reason"`
+6. **No Hallucinated Math** — never introduce equations or values not in the source text
+
+## Limitations
+
+- Does not invent missing equations, domains, values, or assumptions that are absent from the source document.
+- Requires enough source text to cite every extracted element; sparse prompts should be returned with explicit missing-information markers.
+- Produces a formal specification, not a solved optimization model or proof.
+
+## How It Works
+
+### Step 1 — Receive Document
+
+Accept the document text, research excerpt, problem description, or specification as input.
+
+### Step 2 — Classify
+
+Identify `problem_class`: `optimization | classification | simulation | proof | estimation | other`
+
+### Step 3 — Extract MPS Components
+
+**Variables** — `id`, `name`, `symbol`, `type`, `domain`, `units`, `role`, `evidence`, `inferred`, `status`
+
+**Operators** — `id`, `name`, `symbol`, `arity`, `acts_on`, `produces`, `evidence`, `inferred`
+
+**Constraints** — `id`, `type`, `expression`, `variables_involved`, `evidence`, `hardness`, `inferred`, `status`
+
+**Objectives** — `id`, `direction` (minimize/maximize/satisfy/find/prove), `expression`, `variables_involved`, `evidence`, `inferred`
+
+**Uncertainty** — `id`, `type` (stochastic/epistemic/measurement/model/none_stated), `affects`, `characterization`, `evidence`, `status`
+
+### Step 4 — Surface Missing Information
+
+Identify what the document implies but doesn't state: `missing_information[]` with `element`, `needed_for`, `missing_reason`.
+
+### Step 5 — Validate and Score
+
+`validation_flags`:
+- `has_complete_objectives`: true/false/partial
+- `has_bounded_variables`: true/false/partial
+- `has_evidence_for_all_elements`: true/false/partial
+- `inference_count`: integer
+- `missing_count`: integer
+- `overall_formalizability`: HIGH/MEDIUM/LOW
+
+## Output Format
+
+Produce the complete MPS as a JSON object:
+
+```json
+{
+  "mps_version": "1.0",
+  "source_title": "...",
+  "problem_class": "optimization",
+  "variables": [...],
+  "operators": [...],
+  "constraints": [...],
+  "objectives": [...],
+  "uncertainty": [...],
+  "missing_information": [...],
+  "validation_flags": {
+    "overall_formalizability": "HIGH"
+  }
+}
+```
+
+## Best Practices
+
+- ✅ Apply all 6 Zero-Inference Protocol rules before outputting any element
+- ✅ Surface MISSING markers rather than silently inferring — incomplete formalization is valid output
+- ✅ Cite the exact source phrase in every `evidence` field
+- ❌ Never introduce mathematical relationships not grounded in the source text
+
+## Additional Resources
+
+- Repository: [thebrierfox/doc2math-skill](https://github.com/thebrierfox/doc2math-skill)
+- Full BYOK tool: [ace-license-server-production.up.railway.app/byok/doc2math](https://ace-license-server-production.up.railway.app/byok/doc2math)
+- Built by [IntuiTek¹](https://intuitek.ai) (~K¹) — MIT License