/prior-art
Research existing solutions when exploring a new problem space. Use when the user mentions "prior art", "existing solutions", "what libraries exist for", or wants to understand the landscape before building.
$ npx -y skills add bendrucker/claude --skill prior-art --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/prior-art
Context preview
The summary Claude sees to decide when to auto-load this skill.
Research existing solutions when exploring a new problem space. Use when the user mentions "prior art", "existing solutions", "what libraries exist for", or wants to understand the landscape before building.
SKILL.md
prior-art.SKILL.mdname: research:prior-art
description: |
Research existing solutions when exploring a new problem space. Use when the user mentions "prior art", "existing solutions", "what libraries exist for", or wants to understand the landscape before building.
argument-hint: <topic>
disable-model-invocation: true
Research prior art for: $ARGUMENTS
Process
Search
Identify relevant sources based on context:
- **GitHub**: Search for repositories matching the problem space
- **Package registries**: npm, PyPI, crates.io, pkg.go.dev—infer from current project or query
- **Web search**: For broader landscape understanding
Run searches in parallel. Infer the ecosystem from: 1. Current project's language/framework (if present) 2. Query terms (e.g., "React hook for X" implies npm) 3. Ask only if genuinely ambiguous
Investigation
Start with 2-3 most promising projects: 1. Read README and high-level docs to assess relevance 2. Examine code only when relevance is confirmed AND implementation details matter 3. Dispatch parallel `Agent` calls per project, with explicit focus areas
If results don't satisfy the query, expand to more projects.
**Agent dispatch example:**
Investigate [project] for prior art on [topic]:
- How does it approach [specific aspect]?
- What tradeoffs does it make?
- What can we learn for our use case?
Synthesis
Gather findings and produce a recommendation:
- Identify common patterns across solutions
- Note meaningful variations in approach
- Infer intent:
- "build X" → learn patterns, inform implementation
- "library for X" → find dependencies to use directly
Output Format
Respond in the conversation with structured markdown, not files:
## Prior Art: [Topic]
### Summary
[Common patterns, key variations, recommendation based on query intent]
### Projects
#### [Project Name]
- **Repository**: [link]
- **Relevance**: [why this matters to the query]
- **Approach**: [how it solves the problem]
- **Lessons**: [what to learn from it]
#### [Next Project]
...
### Additional Projects (not deeply investigated)
- [Project]: [one-line description]
- ...
Behavior Guidelines
- **Honest reporting**: Acknowledge when prior art is sparse—don't force results
- **Research only**: Don't offer to integrate dependencies or modify the project
- **Ecosystem inference**: Derive from context, don't require explicit specification
- **Adaptive depth**: Investigate more projects if the initial batch is insufficient
Read more
name: research:prior-art description: | Research existing solutions when exploring a new problem space. Use when the user mentions "prior art", "existing solutions", "what libraries exist for", or wants to understand the landscape before building. argument-hint: <topic> disable-model-invocation: true
Research prior art for: $ARGUMENTS
Process
Search
Identify relevant sources based on context:
- **GitHub**: Search for repositories matching the problem space
- **Package registries**: npm, PyPI, crates.io, pkg.go.dev—infer from current project or query
- **Web search**: For broader landscape understanding
Run searches in parallel. Infer the ecosystem from: 1. Current project's language/framework (if present) 2. Query terms (e.g., "React hook for X" implies npm) 3. Ask only if genuinely ambiguous
Investigation
Start with 2-3 most promising projects: 1. Read README and high-level docs to assess relevance 2. Examine code only when relevance is confirmed AND implementation details matter 3. Dispatch parallel `Agent` calls per project, with explicit focus areas
If results don't satisfy the query, expand to more projects.
**Agent dispatch example:**
Investigate [project] for prior art on [topic]: - How does it approach [specific aspect]? - What tradeoffs does it make? - What can we learn for our use case?
Synthesis
Gather findings and produce a recommendation:
- Identify common patterns across solutions
- Note meaningful variations in approach
- Infer intent:
- "build X" → learn patterns, inform implementation
- "library for X" → find dependencies to use directly
Output Format
Respond in the conversation with structured markdown, not files:
## Prior Art: [Topic] ### Summary [Common patterns, key variations, recommendation based on query intent] ### Projects #### [Project Name] - **Repository**: [link] - **Relevance**: [why this matters to the query] - **Approach**: [how it solves the problem] - **Lessons**: [what to learn from it] #### [Next Project] ... ### Additional Projects (not deeply investigated) - [Project]: [one-line description] - ...
Behavior Guidelines
- **Honest reporting**: Acknowledge when prior art is sparse—don't force results
- **Research only**: Don't offer to integrate dependencies or modify the project
- **Ecosystem inference**: Derive from context, don't require explicit specification
- **Adaptive depth**: Investigate more projects if the initial batch is insufficient
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