Autoskill Cb Brand Naming Trademark Guide

SKILL.md

---
name: autoskill-cb-brand-naming-trademark-guide
description: "Generate brand names that work across languages and pass trademark checks — not just creative ideas, but names with preliminary availability screening, cultural safety checks, and domain availability. Name your next product or brand in under 10 minutes."
version: 1.1.0
tags: brand-naming, trademark-search, cultural-validation, international-naming, brand-strategy
---

# Global Brand Naming and Trademark Guide

## Overview

## Recursive Decomposition Framework for Global Brand Naming (cb‑brand‑naming‑trademark‑guide)

**Purpose**: Transform a single user request into a fully materialized deliverable that exhaustively enumerates every analytical node, sub‑node, and output artifact required to present **12 English brand name candidates** together with *meaning*, *pronunciation risk*, *internationalization risk*, *preliminary trademark risk assessment*, and a ranked recommendation of the top 3 names. The framework is deliberately deep: before any final naming table appears, the model must construct **(1) a complete inventory of all logical components**, **(2) a full hierarchy of child tasks for each component**, and **(3) populate every leaf node with concrete data (real or placeholder) before synthesis**.

### Phase 0 – Input Normalization & Assumption Layer
1. **Parse user intent**: Identify target audience (overseas developers), product type (AI‑agent evaluation platform), output quota (12 names + details).  
2. **Create assumption matrix** for any missing constraints (e.g., desired brand personality, domain length preference, prohibited phonemes). Each missing datum receives a plausible placeholder with an explicit “Assumed” flag.
3. **Log the normalized request** as a root node `REQ_ROOT`.

### Phase 1 – Node Inventory Construction (Inventory Tree)
| Node ID | Description | Required Child Nodes |
|---------|-------------|----------------------|
| INV_ROOT | Master inventory for the entire deliverable | INV_NAMING_OBJECTIVE, INV_CONSTRAINTS, INV_LINGUISTIC_SCREEN, INV_CULTURAL_RISK, INV_TRADEMARK_PRELIM, INV_OUTPUT_MATRIX |
| INV_NAMING_OBJECTIVE | Definition of naming goal and positioning | OBJ_POSITIONING, OBJ_UNIQUENESS, OBJ_SCALABILITY |
| INV_CONSTRAINTS | All hard/soft constraints (legal, phonetic, length, domain) | CONSTR_LEGAL, CONSTR_PHONETIC, CONSTR_LENGTH, CONSTR_DOMAIN |
| INV_LINGUISTIC_SCREEN | Phonetic & semantic analysis per candidate | LS_PRONUNCIATION, LS_SEMANTICS, LS_HOMOPHONE |
| INV_CULTURAL_RISK | Cross‑language negative meaning audit | CR_LANG_EN, CR_LANG_ES, CR_LANG_ZH, CR_LANG_JA, CR_LANG_KO |
| INV_TRADEMARK_PRELIM | Preliminary trademark conflict flags per jurisdiction | TM_US, TM_EU, TM_CN, TM_OTHER |
| INV_OUTPUT_MATRIX | Final tabular presentation and ranking logic | OUT_TABLE, OUT_RANKING, OUT_RECOMMENDATIONS |

**Rule 0 – Mandatory Completion of Inventory**: The model must emit the full inventory table (as above) *before* any candidate name appears. No synthesis may begin until every listed child node is instantiated.

### Phase 2 – Child‑Node Expansion & Data Population
For **each** inventory node, the model must:
1. State the node’s purpose in a dedicated paragraph.
2. Generate a sub‑inventory of its own children (minimum two levels deep).
3. Populate every leaf with concrete values (real data where available, otherwise clearly marked placeholder e.g., `Assumed: “no known trademark conflict”`).
4. Reference the parent node ID to maintain traceability.

**Example Expansion** (truncated for illustration):
- `INV_NAMING_OBJECTIVE` → expands into `OBJ_POSITIONING` (“professional, developer‑centric”) and `OBJ_UNIQUENESS` (“distinct from existing AI platform names”). Both further expand into a checklist of criteria (minimum two items each).

All expansions must be written out in full sentences; bullet lists are allowed but must contain at least three items per list to avoid compression.

### Phase 3 – Candidate Generation Engine (DEEP_GENERATE)
1. **Generate 12 raw name strings** using combinatorial rules derived from the completed objective and constraint inventories.
2. For each name, automatically create a *leaf package* containing:
   - Meaning & semantic rationale
   - Pronunciation risk score (scale 0‑5) with phonetic notes per target language
   - Internationalization risk assessment (scale 0‑5) with flagged issues per market
   - Preliminary trademark risk flag per jurisdiction (Yes/No/Assumed)
3. **Do not** pause for user clarification; any missing data is filled from the assumption matrix.

### Phase 4 – Ranking, Recommendation & Final Matrix Assembly
1. Compute a composite score for each name using weighted sums of the three risk dimensions and the trademark flag (weights are hard‑coded: Pronunciation 30%, Internationalization 30%, Trademark 40%).
2. Sort the 12 names descending by composite score.
3. Select the top 3 as **Recommended Names** and provide a short justification paragraph for each, referencing the specific risk scores that led to its ranking.
4. Assemble **OUT_TABLE**: a markdown table with columns `Name | Meaning | Pronunciation Risk | Internationalization Risk | Trademark Risk (US/EU/CN) | Composite Score`.
5. Append **OUT_RANKING** and **OUT_RECOMMENDATIONS** sections after the table, each clearly labeled.

### Phase 5 – Completion Gate & Audit Trail
- After the full output matrix is printed, emit an **Audit Log** that lists every node ID, its child count, and a confirmation flag `✓ Completed`.
- The response must end with the complete artifact; no trailing prompts such as “Let me know if you need more” or “If you want …”. Any such language may only appear *after* the full artifact and must be preceded by a line break stating **END OF ARTIFACT**.

### Global Anti‑Compression Directives (applied throughout)
- Do not use wording that limits length (“concise”, “brief”, “compact”, etc.).
- Every logical step must be *explicitly written*; implicit shortcuts are prohibited.
- Repetition is allowed when it reinforces hierarchy or traceability.
- The model must **continue** until the Audit Log confirms every leaf node has been populated.
- Visual style instructions (e.g., markdown formatting) apply only to presentation, never to reduce reasoning depth.

---
This initialization guarantees that any downstream execution will obligatorily decompose the task into at least two hierarchical layers, fully populate all required data points, and produce a maximal‑token artifact covering the entire naming evaluation workflow.

## Safety and Limitations

This skill provides a structured naming evaluation framework, not a legal trademark search or clearance opinion. Trademark conflicts, domain disputes, and naming-related legal risks require review by qualified trademark counsel in each target jurisdiction before adopting or launching a name commercially. A name that passes this framework's screening is not automatically cleared for legal use; professional trademark search and registration are required before commercial launch.

If the user asks for "available" names, return `availability unknown until checked`
unless they provide a dated source such as USPTO, EUIPO, WIPO, KIPRIS, domain
registrar, or marketplace/handle search output. Never present invented trademark
or domain results as live facts.

## Acceptance Criteria

- Produces a completed naming objective and constraint complete before evaluating any names
- Scores each candidate name on linguistic, cultural, memorability, and category-fit criteria
- Includes taboo and negative-association screening prompts covering six cultural dimensions per market
- Separates the creative naming evaluation (Steps 1–4) from the legal clearance process (Step 5)
- Produces a shortlist comparison matrix that enables a structured decision among two to four final candidates