Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
- Stars
- 14,234
- Version
- v1
- Copies
- 0
Overview
Waxell's checks found 2 findings to review, highest severity high.
- Highest severity
- high
- Findings
- 2
- What ran
- Static analysis
A clean result means nothing alarming in what is published, not a guarantee. How the checks work
We don't republish the skill's instructions. Read them on GitHub at the reviewed commit.
Security
Security scan
Checked 2 days ago
Result
Review findings Waxell's checks found 2 findings to review, highest severity high.
Checked with static analysis: servers it launches, packages it installs at runtime, scripts piped to a shell and bundled binaries. Nothing was run. How the checks work
Findings (2)
high · 2
- high
Tells Claude to install unpinned packages (dspy)
SKILL.md:29 · skill_risk:S2
- high
Tells Claude to install unpinned packages (git+https://github.com/stanfordnlp/dspy.git)
SKILL.md:32 · skill_risk:S2
Versions
Version history
Each distinct content we have seen, newest first, with the verdict it got at the time.
One version so far: v1 (1.0.0), seen 2026-11-03.
See and control which skills your people use
Inside your organization, Waxell scans each skill and plugin before anyone installs it, approves what passes and publishes it to a private marketplace, and puts every MCP tool call behind one governed URL.
30 minutes. No commitment.
SOC 2 Type II attested · ISO/IEC 27001:2022 certified · HIPAA with a BAA · aligned to the EU AI Act and NIST AI RMF
More research skills and plugins
guidanceorchestra-researchControl LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step… Review findingsResearchEngineering14k
instructororchestra-researchExtract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type… Review findingsResearchData & analytics14k
outlinesorchestra-researchGuarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers,… Review findingsResearchEngineering14k
phoenix-observabilityorchestra-researchOpen-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed… Review findingsResearchData & analytics14k