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README.md

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+---
+license: cc-by-4.0
+language:
+- en
+tags:
+- code
+- question-answering
+- llm-evaluation
+- rubric-grading
+- agents
+task_categories:
+- question-answering
+- text-generation
+size_categories:
+- n<1K
+configs:
+- config_name: dspy
+  data_files: dspy.jsonl
+- config_name: openclaw
+  data_files: openclaw.jsonl
+---
+
+# msbench
+
+**msbench** is a small, high-effort benchmark of **expert-level coding questions** about real
+open-source codebases, each paired with a **gold answer** and a **weighted, source-grounded grading
+rubric**. The questions ask a model to produce working code that uses a specific library/framework
+correctly; the rubric decomposes a correct answer into discrete, checkable claims, each tied to exact
+lines of the upstream source.
+
+This release publishes **both the questions and the full rubrics** (nothing is held back), so the
+evaluation is fully transparent and reproducible.
+
+## Configs
+
+Pick a subset with the second argument of `load_dataset`:
+
+```python
+from datasets import load_dataset
+
+dspy     = load_dataset("jacobli/msbench", "dspy")        # 30 questions
+openclaw = load_dataset("jacobli/msbench", "openclaw")    # 20 questions
+```
+
+| config | questions | topics | codebase |
+|---|---:|---:|---|
+| `dspy` | 30 | 6 | [DSPy](https://github.com/stanfordnlp/dspy) |
+| `openclaw` | 20 | 4 | [OpenClaw](https://github.com/openclaw/openclaw) |
+
+## Schema
+
+Each row has six fields:
+
+| field | type | description |
+|---|---|---|
+| `id` | string | stable opaque identifier |
+| `topic` | string | coarse category (see below) |
+| `question` | string | the task prompt — asks for a self-contained, runnable solution |
+| `gold_answer` | string | a reference solution (code) |
+| `rubric` | list | weighted claims that define a correct answer |
+| `evidence` | list | source excerpts that ground the rubric |
+
+**`rubric`** — a list of claims; weights sum to **100** per question:
+
+```json
+{
+  "claim_id": "c1",
+  "claim_type": "core",          // "core" = essential; "supporting" = secondary
+  "weight": 52,                   // integer; the rubric's weights sum to 100
+  "statement": "…what must be true of a correct answer…",
+  "span_ids": ["s4", "s8"]        // evidence spans grounding this claim
+}
+```
+
+**`evidence`** — the source excerpts the grader is shown; every `span_ids` value in `rubric`
+resolves to one of these `span_id`s:
+
+```json
+{
+  "span_id": "s4",
+  "path": "dspy/teleprompt/gepa/gepa.py",   // path within the upstream repo
+  "start_line": 330,
+  "end_line": 365,
+  "excerpt": "0330:     def __init__(\n0331:         self,\n…"   // line-number-prefixed source
+}
+```
+
+Excerpts are byte-exact copies of the upstream source at the pinned commits below (each line is
+prefixed with its 1-indexed line number, e.g. `0330: `).
+
+### Topics
+- **dspy:** `gepa_optimizer_usage`, `prompt_optimization_workflows`, `rag_and_retrieval_pipelines`, `react_agents_and_tools`, `signature_schema_and_pydantic_types`, `evaluation_metrics_and_custom_eval`
+- **openclaw:** `model_fallback_and_failover_logic`, `cross_session_channel_context_and_session_behavior_requests`, `memory_core_dreaming_and_promotion_pipeline`, `new_plugin_provider_and_channel_integration_requests`
+
+## How the rubric is used for grading
+
+A judge model is shown the **question**, the **candidate answer**, the **`gold_answer`**, the
+**`rubric`**, and the **`evidence`** spans. It scores each claim independently (does the answer
+satisfy the claim?), and the question score is the **weight-weighted fraction of satisfied claims**
+(0–100). `claim_type` lets you apply an optional **conjunctive gate**: require every `core` claim to
+be satisfied or the answer scores 0. The `evidence` excerpts are the *only* code context the judge
+needs — grading does not require checking out the repositories.
+
+## Source code & attribution
+
+The `evidence` excerpts and `path` values reference these repositories at fixed commits:
+
+| codebase | repo | commit | license |
+|---|---|---|---|
+| DSPy | `stanfordnlp/dspy` | `9cdb0aac28b2a04b064e40697ccd301872cf6a43` | MIT |
+| OpenClaw | `openclaw/openclaw` | `da228660306b55a9cce3b973946f3aacfc515848` | MIT |
+
+To inspect or extend the evidence, check out the corresponding repo at the pinned commit and open
+the listed `path` at the given line range.
+
+## Licensing
+
+- **Questions, gold answers, and rubrics** (the original contributions of this dataset) are released
+  under **CC-BY-4.0**.
+- **Embedded source `excerpt`s** are derived from DSPy and OpenClaw and remain under their respective
+  **MIT** licenses; attribution is provided above.
+
+## Notes & limitations
+
+- This is a deliberately small, expert-curated set (50 questions total), not a large-scale benchmark.
+- Because both questions and rubrics are public, treat results as an **open** (non-held-out)
+  evaluation; models may be trained on this content.
+- The benchmark is grounded in specific repository snapshots; answers reflect the APIs at the pinned
+  commits.