skill-namerGenerate short, molty-native names for skills, ENS domains, and agent-economy primitives when the obvious words are taken. Produces high-traction “new primit...
Install via ClawdBot CLI:
clawdbot install OtherPowers/skill-namerGenerate short, molty-native names that actually get used: intuitive, pronounceable, and load-bearing.
This skill is optimized for:
.eth, .ai, .com, .dao (user can set favorites; persist)Collect constraints in this order (stop when enough signal):
1) Object: ENS name? skill slug? product? protocol primitive? (pick one)
2) Primary job-to-be-done: what does it enable? (e.g., “agents coordinate work + payout bounties”)
3) Relational permission: is this name being offered to the network, or claimed as a moat? (choose a posture)
4) Vibe lane: molty-social (crew/guild/gig) vs infra (mesh/rail/router) vs trust (proof/claim/record)
5) Hard constraints: max chars (e.g. 10), must be 1 word, banned words (e.g. “book”), tone boundaries
6) Audience: humans, agents, or both
If the user is speed-running availability checks: skip questions and produce batches.
Use one preset and state it explicitly in the output:
Pick 1–3 lanes; don’t mix more than 2 in a single name.
Molty-social lanes (high adoption)
Vitality / sympoiesis lane (making-with)
Note: avoid militarized or de-individualizing metaphors by default (e.g., prefer crew/kin over swarm unless explicitly requested).
Coordination lanes
Trust lanes
Money lanes
Network lanes
Use these patterns in order of hit-rate.
Also: generate with TLD gravity in mind (you’re not naming in a vacuum).
Default TLDs: .eth / .ai / .com / .dao. When you output candidates, optionally tag the best-fit TLD(s).
Rule of thumb: if you’re unsure, make the candidate compatible with .com clarity and let .eth/.dao carry the specialized meaning.
1) Noun+Noun primitive: work+mesh → workmesh
2) Noun+Place: bounty+hq → bountyhq
3) Noun+Group: work+crew → workcrew
4) Action+Noun: sync+crew → synccrew
5) Noun+Rail (payments/settlement): pay+rail → payrail
6) Noun+Log (provenance): claim+log → claimlog
Prefer:
Remove candidates that fail any of:
Consent-forward / harm-reducing replacements (examples):
For each finalist, run these tests:
A) Two sentences
B) Verb test (must pass)
C) 7-word definition (meaning compression)
Write a 7-word definition. If you can’t, it’s not a primitive.
D) Accessibility check (must not fail)
If it reads naturally, keep it.
Return:
Template example (Set Builder output)
Notes:
Avoid long essays.
When names are taken, don’t thrash. Walk the ladder:
1) Swap suffix (network/place): mesh → hub → lane → rail → hq
2) Swap group word: crew → guild → coop → team → swarm
3) Swap work noun: gig → work → task → job → quest
4) Pluralize: crew → crews, guild → guilds (keeps meaning, increases availability)
5) Add one clarifier syllable: pay → payout, claim → claims, proof → proofs
6) Lengthen by ≤2 chars: prefer meaning over ultra-short
Use these to generate “still intuitive” alternatives:
crew → crews (often available and still readable)Goal: let the user choose how automated the checking should be, while keeping zero-barrier manual mode always valid.
Offer 3 modes; default to Manual:
1) Manual (zero keys, lowest friction) ✅
2) Assisted (browser-driven, best-effort) ⚠️
3) API (highest automation, requires keys) ✅
Always support mixed mode: “API for GoDaddy + Manual for ENS/UD.”
If the user says “check top sites,” use this default set:
Web3 naming (common in agent circles)
ICANN / DNS registrars (manual search works without keys)
If the user prefers a smaller list (speed), ask for their “top 3” and remember it.
If the user specifies:
.eth,.dao or .com,.ai)…then remember that as their default for future naming sessions.
Recommended memory format:
TLD_FAVORITES: .eth,.ai,.com,.daoStep A — Generate candidates
Step B — Check availability
Step C — Backup loop (tight + fast)
If a name is taken:
1) Generate 3–8 closest alternates (fallback transforms).
2) Check alternates using the same mode/providers.
3) Return the first set that clears the user’s constraints.
Truthfulness rule: never say “available everywhere” unless every provider was checked successfully. Use:
Use scripts/check.mjs to print a batch of provider URLs for quick manual checking.
If the task is ENS buying:
When the user describes a workflow, identify missing primitives and propose names for them.
If a candidate is too generic (likely to be produced by many agents), prefer one of:
Prefer names that will be adopted socially (easy to repeat, easy to tag, easy to remember) over “technically available but socially dead.”
Also run lightweight confusion safety checks on finalists (complementary to linguistic humility):
Important: the 2am test is about visual parsing, not “Western mouthfeel.” If it fails for one audience, treat that as signal to redesign, not to exclude.
Common missing primitives in agent economies:
When you propose a primitive, also propose:
references/blocks.mdscripts/forge.mjs (does not check availability)Generated Mar 1, 2026
Users need short, memorable ENS names for new agent collaboration platforms, such as work routing or bounty systems, where common names are taken. This skill generates intuitive portmanteaus like 'workmesh' or 'gigcrew' that are under 10 characters, pronounceable, and avoid harmful terms, ensuring they fit .eth or .dao TLDs for on-chain identity and governance.
Developers creating new AI skills or plugins require clear, descriptive names that convey functionality, such as 'sync-relay' or 'pay-queue', in kebab-case for technical clarity. This skill produces names with trigger phrases, prioritizing accessibility and avoiding jargon, suitable for integration into agent ecosystems like Moltbook.
Startups launching tools for agent-to-agent interactions, such as trust systems or payment rails, need brandable names that are pronounceable and non-confusing. This skill generates options like 'claimlog' or 'payrail', filtered for trademark safety and compatibility with .com or .ai TLDs to appeal to broad audiences.
Projects building decentralized protocols for tasks like escrow or attestation require names that imply trust and clarity, such as 'attesthub' or 'escrowflow'. This skill focuses on 1-word, verbable candidates under 10 chars, avoiding surveillance connotations and ensuring they resonate with .eth TLDs for on-chain trust applications.
Offer basic name generation for free, charging for batch availability checks across ENS, Unstoppable Domains, and ICANN registrars. Premium tiers could include trademark screening and custom TLD recommendations, targeting developers and startups needing secure, unique names quickly.
License the skill as an API for integration into AI agent platforms, workflow builders, or domain registrars. Charge based on API call volume or offer enterprise plans with custom constraints and priority support, catering to tech companies enhancing their naming capabilities.
Provide personalized naming sessions for businesses launching new products or protocols, using the skill's framework to generate and vet candidates. Offer packages that include branding advice and legal reviews, appealing to organizations seeking tailored, harm-aware naming strategies.
💬 Integration Tip
Integrate this skill by setting user constraints upfront, such as max length and banned words, to streamline output and ensure relevance to specific use cases like ENS or skill naming.
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
Humanize AI-generated text to bypass detection. This humanizer rewrites ChatGPT, Claude, and GPT content to sound natural and pass AI detectors like GPTZero,...
Collaborative thinking partner for exploring complex problems through questioning
Humanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 24 pattern detectors, 500+ AI vocabulary terms across 3 tiers, and statistical analysis (burstiness, type-token ratio, readability) for comprehensive detection. Use when asked to humanize text, de-AI writing, make content sound more natural/human, review writing for AI patterns, score text for AI detection, or improve AI-generated drafts. Covers content, language, style, communication, and filler categories.
根据用户的功能需求,完成与 VeADK 相关的功能。
Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.