noui-generalize

AI Community
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
AI
Compatible agents
  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Gemini CLI
  • +20
Trust score
88 / 100 · community maintained
Author / version / license
@tomevault-io · no license declared
Token usage
Heavy
Setup complexity
Manual integration
External API key
Required · Vendor-specific
Operating systems
Docker
Runtime requirements
Python · Docker
Permissions
  • Read-only
  • Write / modify
  • Shell exec
Network behavior
External requests
Install commands
26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools,默认拥有全部工具权限。

Output preview noui-generalize.preview
---
name: noui-generalize
description: Use this skill when the user wants to generalize a recorded MCP workflow, rename tools to readab…
category: ai
runtime: Python / Docker
---

# noui-generalize output preview

## PART A: Task fit
- Use case: Use this skill when the user wants to generalize a recorded MCP workflow, rename tools to readable names, replace raw API params with natural-language parameters, fix bot detection issues (Akamai, Cloudflare, PerimeterX), rewrite operations to use CDP browser execution, or make a generated MCP server usable by Claude Code. Triggers on "generalize a recorded workflow", "rename MCP tools", "replace raw API params with readable names", "make the MCP usable by Claude Code", "generalize tool signatures", "I can't use the MCP tools", "Akamai is blocking", "429 with valid cookies", "TLS fingerprinting", "CDP fetch", "execute from inside the browser", "anti-bot workaround", "tabby credentials are empty", or "browser is authenticated but API calls fail"..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Works for both MCP and Skill outputs / Critical Rules (Never Violate) / Phase 0 — Diagnose Execution Strategy” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Use this skill when the user wants to generalize a recorded MCP workflow, rename tools to readable names, replace raw API params with natural-language parameters, fix bot detection issues (Akamai, Cloudflare, PerimeterX), rewrite operations to use CDP browser execution, or make a generated MCP server usable by Claude Code. Triggers on "generalize a recorded workflow", "rename MCP tools", "replace raw API params with readable names", "make the MCP usable by Claude Code", "generalize tool signatures", "I can't use the MCP tools", "Akamai is blocking", "429 with valid cookies", "TLS fingerprinting", "CDP fetch", "execute from inside the browser", "anti-bot workaround", "tabby credentials are empty", or "browser is authenticated but API calls fail".”.
- **02** When the source has headings, the agent prioritizes “Works for both MCP and Skill outputs / Critical Rules (Never Violate) / Phase 0 — Diagnose Execution Strategy” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; may access external network resources; requires Vendor-specific API keys.

## Running Rules
- read files, write/modify files, run shell commands; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.
Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

  • Core job: Use this skill when the user wants to generalize a recorded MCP workflow, rename tools to readable names, replace raw API params…
  • Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
  • Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

  • Structure: The skill is organized around “Works for both MCP and Skill outputs”, “Critical Rules (Never Violate)”, “Phase 0 — Diagnose Execution Strategy”, “0a. Find and test the server”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
  • Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
  • Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

  • Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
  • Invocation: Name noui-generalize directly; if the source includes slash commands, start with the command and then add task context.
  • Validation: Start small and check whether the result follows “Works for both MCP and Skill outputs / Critical Rules (Never Violate) / Phase 0 — Diagnose Execution Strategy” before expanding.

Boundaries And Review

  • Dependencies: Prepare Vendor-specific API keys before running a full task.
  • Permissions: Declared permissions include read / write / shell-exec; ask the agent to state file, command, and rollback boundaries before acting.
  • Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Discussion

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