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wangxiaofei860208-source

Lobster Search First

by wangxiaofei860208-source · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install lobster-search-first
Description
Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Invokes the researcher agent.
README (SKILL.md)

/search-first — Research Before You Code

Systematizes the "search for existing solutions before implementing" workflow.

Trigger

Use this skill when:

  • Starting a new feature that likely has existing solutions
  • Adding a dependency or integration
  • The user asks "add X functionality" and you're about to write code
  • Before creating a new utility, helper, or abstraction

Workflow

┌─────────────────────────────────────────────┐
│  1. NEED ANALYSIS                           │
│     Define what functionality is needed      │
│     Identify language/framework constraints  │
├─────────────────────────────────────────────┤
│  2. PARALLEL SEARCH (researcher agent)      │
│     ┌──────────┐ ┌──────────┐ ┌──────────┐  │
│     │  npm /   │ │  MCP /   │ │  GitHub / │  │
│     │  PyPI    │ │  Skills  │ │  Web      │  │
│     └──────────┘ └──────────┘ └──────────┘  │
├─────────────────────────────────────────────┤
│  3. EVALUATE                                │
│     Score candidates (functionality, maint, │
│     community, docs, license, deps)         │
├─────────────────────────────────────────────┤
│  4. DECIDE                                  │
│     ┌─────────┐  ┌──────────┐  ┌─────────┐  │
│     │  Adopt  │  │  Extend  │  │  Build   │  │
│     │ as-is   │  │  /Wrap   │  │  Custom  │  │
│     └─────────┘  └──────────┘  └─────────┘  │
├─────────────────────────────────────────────┤
│  5. IMPLEMENT                               │
│     Install package / Configure MCP /       │
│     Write minimal custom code               │
└─────────────────────────────────────────────┘

Decision Matrix

Signal Action
Exact match, well-maintained, MIT/Apache Adopt — install and use directly
Partial match, good foundation Extend — install + write thin wrapper
Multiple weak matches Compose — combine 2-3 small packages
Nothing suitable found Build — write custom, but informed by research

How to Use

Quick Mode (inline)

Before writing a utility or adding functionality, mentally run through:

  1. Does this already exist in the repo? → rg through relevant modules/tests first
  2. Is this a common problem? → Search npm/PyPI
  3. Is there an MCP for this? → Check ~/.claude/settings.json and search
  4. Is there a skill for this? → Check ~/.claude/skills/
  5. Is there a GitHub implementation/template? → Run GitHub code search for maintained OSS before writing net-new code

Full Mode (agent)

For non-trivial functionality, launch the researcher agent:

Task(subagent_type="general-purpose", prompt="
  Research existing tools for: [DESCRIPTION]
  Language/framework: [LANG]
  Constraints: [ANY]

  Search: npm/PyPI, MCP servers, Claude Code skills, GitHub
  Return: Structured comparison with recommendation
")

Search Shortcuts by Category

Development Tooling

  • Linting → eslint, ruff, textlint, markdownlint
  • Formatting → prettier, black, gofmt
  • Testing → jest, pytest, go test
  • Pre-commit → husky, lint-staged, pre-commit

AI/LLM Integration

  • Claude SDK → Context7 for latest docs
  • Prompt management → Check MCP servers
  • Document processing → unstructured, pdfplumber, mammoth

Data & APIs

  • HTTP clients → httpx (Python), ky/got (Node)
  • Validation → zod (TS), pydantic (Python)
  • Database → Check for MCP servers first

Content & Publishing

  • Markdown processing → remark, unified, markdown-it
  • Image optimization → sharp, imagemin

Integration Points

With planner agent

The planner should invoke researcher before Phase 1 (Architecture Review):

  • Researcher identifies available tools
  • Planner incorporates them into the implementation plan
  • Avoids "reinventing the wheel" in the plan

With architect agent

The architect should consult researcher for:

  • Technology stack decisions
  • Integration pattern discovery
  • Existing reference architectures

With iterative-retrieval skill

Combine for progressive discovery:

  • Cycle 1: Broad search (npm, PyPI, MCP)
  • Cycle 2: Evaluate top candidates in detail
  • Cycle 3: Test compatibility with project constraints

Examples

Example 1: "Add dead link checking"

Need: Check markdown files for broken links
Search: npm "markdown dead link checker"
Found: textlint-rule-no-dead-link (score: 9/10)
Action: ADOPT — npm install textlint-rule-no-dead-link
Result: Zero custom code, battle-tested solution

Example 2: "Add HTTP client wrapper"

Need: Resilient HTTP client with retries and timeout handling
Search: npm "http client retry", PyPI "httpx retry"
Found: got (Node) with retry plugin, httpx (Python) with built-in retry
Action: ADOPT — use got/httpx directly with retry config
Result: Zero custom code, production-proven libraries

Example 3: "Add config file linter"

Need: Validate project config files against a schema
Search: npm "config linter schema", "json schema validator cli"
Found: ajv-cli (score: 8/10)
Action: ADOPT + EXTEND — install ajv-cli, write project-specific schema
Result: 1 package + 1 schema file, no custom validation logic

Anti-Patterns

  • Jumping to code: Writing a utility without checking if one exists
  • Ignoring MCP: Not checking if an MCP server already provides the capability
  • Over-customizing: Wrapping a library so heavily it loses its benefits
  • Dependency bloat: Installing a massive package for one small feature
Usage Guidance
This skill is mostly coherent with its stated purpose, but it instructs the agent to read your repository and specific user config files (~/.claude/settings.json and ~/.claude/skills/) even though the package metadata doesn't declare those paths. Before installing: 1) Decide whether you’re comfortable granting the agent read access to your codebase and personal Claude config; 2) If you want to limit access, run the skill only in a sandboxed environment or with read-limited credentials; 3) Verify that the researcher subagent’s external searches (npm, PyPI, GitHub, MCP servers) are acceptable for your data-sharing/privacy policy; 4) If you need stronger guarantees, ask the skill author to explicitly declare required config paths in metadata and/or to add an option to disable local-file checks. If you control sensitive information in ~/.claude or your repo, treat this as potentially privacy-impacting and review before enabling autonomous use.
Capability Analysis
Type: OpenClaw Skill Name: lobster-search-first Version: 1.0.1 The skill bundle defines a 'research-before-coding' workflow for an AI agent, encouraging the use of existing libraries (npm, PyPI) and MCP servers. While it instructs the agent to check local configuration files like '~/.claude/settings.json' and '~/.claude/skills/' to identify existing tools, this behavior is consistent with its stated purpose of preventing redundant code implementation and does not involve data exfiltration or malicious execution.
Capability Assessment
Purpose & Capability
Name and description match the instructions: the skill guides an agent to search package registries, MCP servers, GitHub, and local repos/skills to decide whether to adopt/extend/build. The requested actions (search, evaluate, decide) are coherent with a 'search-first' workflow.
Instruction Scope
The SKILL.md explicitly instructs the agent to read local files (e.g., run ripgrep through the repo, check ~/.claude/settings.json and ~/.claude/skills/) and to invoke a researcher subagent that will search external services. Those file reads are broader access than the skill's declared metadata exposes (no config paths declared). This grants the agent access to repository source and user-specific config which may contain secrets or sensitive context; the doc does not document or justify the scope of that access.
Install Mechanism
There is no install spec and no code files — instruction-only skills are lowest-risk in terms of arbitrary code being written to disk. Nothing is downloaded or installed by the skill itself.
Credentials
The skill declares no required environment variables or credentials, which aligns with its description. However, the runtime instructions reference local config paths (~/.claude/*) and advise running repo searches; while this can be reasonable for a researcher workflow, the skill metadata does not declare those config paths, so users won't be warned in advance about local file access.
Persistence & Privilege
always is false and the skill has no install actions that would persist on disk or modify other skills. Autonomous invocation (disable-model-invocation=false) is normal for skills of this type; nothing here demands elevated or permanent platform privileges.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install lobster-search-first
  3. After installation, invoke the skill by name or use /lobster-search-first
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
- Skill name changed from "search-first" to "lobster-search-first" in metadata. - No other content changes; workflow and documentation remain the same.
v1.0.0
- Initial release of the "search-first" skill, enabling a research-before-coding workflow. - Guides users to search existing tools, libraries, and patterns before implementing custom code. - Provides detailed workflow steps, decision matrix, and search shortcuts for common categories (e.g., tooling, AI integration, data, publishing). - Integrates with planner, architect, and iterative-retrieval agents for coordinated solution discovery. - Includes practical usage examples and highlights common anti-patterns to avoid.
Metadata
Slug lobster-search-first
Version 1.0.1
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 2
Frequently Asked Questions

What is Lobster Search First?

Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Invokes the researcher agent. It is an AI Agent Skill for Claude Code / OpenClaw, with 89 downloads so far.

How do I install Lobster Search First?

Run "/install lobster-search-first" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Lobster Search First free?

Yes, Lobster Search First is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Lobster Search First support?

Lobster Search First is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Lobster Search First?

It is built and maintained by wangxiaofei860208-source (@wangxiaofei860208-source); the current version is v1.0.1.

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