Best for
- Naming a new repository, project, tool, or product
- User is unsure what to call something
- User rejects initial suggestions as "too generic" or "too vendor-specific"
jackchuka/skills/project-namer/SKILL.md
Use when naming a project, repository, tool, or product and wanting a memorable, unique name
Decision brief
Use when naming a project, repository, tool, or product and wanting a memorable, unique name
Compatibility matrix
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/jackchuka/skills --skill "project-namer"Inspect the Agent Skill "project-namer" from https://github.com/jackchuka/skills/blob/7b0b33f68b8f11522e43622e5cb3bacd802999d2/project-namer/SKILL.md at commit 7b0b33f68b8f11522e43622e5cb3bacd802999d2. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
Ask what the project contains and does:
Naming a new repository, project, tool, or product
Ask what the project contains and does:
Identify naming constraints:
Permission review
No configured static risk pattern was detected
This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.
Evidence record
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 72/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 15 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Guide users from vague naming requirements to a memorable, unique name through structured exploration of scope, constraints, and style preferences.
digraph naming {
rankdir=TB;
"Understand scope" -> "Clarify constraints" -> "Offer style categories" -> "Generate options" -> "Iterate on feedback" -> "Converge";
"Iterate on feedback" -> "Offer style categories" [label="style shift"];
"Iterate on feedback" -> "Generate options" [label="refine"];
}
Ask what the project contains and does:
Identify naming constraints:
Present naming styles - let user pick direction:
| Style | Examples | When to suggest |
|---|---|---|
| Functional | toolkit, dev-tools, workflows | User wants clarity over personality |
| Character/Butler | friday, jeeves, pennyworth | User wants personality, memorability |
| Compound | devbox, workstation, codekit | Balance of clear + catchy |
| Metaphor | forge, lighthouse, compass | Evokes purpose without stating it |
| Coined | vercel, kubectl, nginx | Maximum uniqueness, brand potential |
Within chosen style, generate 5-8 options. For each:
Listen for signals:
When user shows interest, validate:
Good names are:
Avoid:
| Mistake | Fix |
|---|---|
| Jumping to suggestions without understanding scope | Ask what goes in it first |
| Only offering one style | Present style categories, let user choose |
| Giving up after one rejection | Rejection = information about preferences |
| Suggesting overused pop culture | Dig deeper - lesser-known references or original |
Alternatives
event4u-app/agent-config
Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.
K-Dense-AI/scientific-agent-skills
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
K-Dense-AI/scientific-agent-skills
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K-Dense-AI/scientific-agent-skills
Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.