Results for “personality-test”

AI Agent personality diagnosis and configuration system based on MBTI framework. Use when users want to (1) test/diagnose an Agent's personality type, (2) understand the gap between Agent's actual personality and user's desired personality, (3) generate configuration recommendations to adjust Agent behavior, (4) customize Agent's communication style, proactivity, reasoning approach, or execution patterns. Supports both free tier (quick assessment) and premium tier (full 93-question assessment with detailed diagnostics).

Expert in human behavior, personality theory, motivation, and cognitive patterns — builds psychologically credible characters and interactions grounded in clinical and research frameworks

Run Mom Test–style customer-discovery interviews to validate or kill an unbuilt idea — generate a non-leading interview guide, conduct or coach the conversations, and synthesize signal vs compliments. Load when the user asks to do customer discovery, run problem interviews, validate an idea with users, run a Mom Test, design an interview guide, or says "talk to customers", "validate the problem", "interview users", "Mom Test this", "did real users want it", "synthesize my interviews", "I just talked to N people". Sub-skill of `venture-exploration`. Hard-bans "would you use this?", solution-pitching, friend/family-only ICP, and treating compliments as validation. Calls `secure-*` before synthesizing any pasted external transcripts.

Audits decisions and situations for operating psychological biases using Munger's 25 tendencies framework, producing a structured analysis with countermeasures.

Benchmarks LLM bias by scoring responses to automatically generated open-ended questions across sensitive attributes, producing a composite fitness score from 0 to 5.

Run customer interviews that uncover real problems using Mom Test rules: ask about their life, not your idea; focus on past specifics; and secure commitments instead of collecting compliments.

Synthesize personas and jobs-to-be-done from evidence with confidence levels. Use when: (1) audience segmentation, (2) messaging alignment, (3) feature prioritization inputs. NOT for: stereotype-based profiling.

针对 Modular RAG MCP Server 项目的模拟技术面试 Agent。读取用户简历（可选），围绕三个方向进行最多 3 轮深度追问，结束后生成并持久化面试报告（含参考答案、包装识别点评、评分）。Use when user says '模拟面试', '面试练习', '帮我面试', 'mock interview', 'interview practice', '面试', '考我', '开始面试', or wants to practice interviewing about this project.

Creates research-backed user personas with JTBD, pain points, gains, and unexpected behavioral insights. Use when building personas from survey or interview data, or segmenting users to inform product decisions. Triggers on: user persona, user profile, customer segment, jobs-to-be-done, JTBD, persona creation, user segmentation, target user, who are our users.

Self-tuning question sensitivity + developer psychographic for gstack (v1: observational). (gstack)

Self-tuning question sensitivity + developer psychographic for gstack (v1: observational). Review which AskUserQuestion prompts fire across gstack skills, set per-question preferences (never-ask / always-ask / ask-only-for-one-way), inspect the dual-track profile (what you declared vs what your behavior suggests), and enable/disable question tuning. Conversational interface — no CLI syntax required. Use when asked to "tune questions", "stop asking me that", "too many questions", "show my profile", "what questions have I been asked", "show my vibe", "developer profile", or "turn off question tuning". (gstack) Proactively suggest when the user says the same gstack question has come up before, or when they explicitly override a recommendation for the Nth time.

Create refined user personas from research data — 3 personas with JTBD, pains, gains, and unexpected insights. Use when building personas from survey data, creating user profiles from research, or segmenting users for product decisions.

Generates personalized greetings based on the time of day and the person's name.

Interprets Culture Index survey data, behavioral profiles, and personality assessments from JSON or PDF. Supports individual profile interpretation, team composition analysis, burnout detection, hiring profiles, manager coaching, interview transcript analysis, and conflict mediation.

Defines the LEND.AI assistant persona: identity, tone, and interaction rules for a Spanish-speaking senior mentor that teaches while working, questions decisions, and requires confirmation before acting.

Embody the comedic persona of Bob Hope, delivering topical, self-deprecating, and bipartisan humor with impeccable timing and adaptability to any audience.

Grill the user relentlessly about a plan or design. Use when the user wants to stress-test a plan before building, or uses any 'grill' trigger phrases.

Stress-test ideas, plans, and decisions using structured critical reasoning across five modes: Socratic questioning, dialectic, pre-mortem, red teaming, and evidence audit.

Create detailed, actionable user personas from research data, capturing jobs-to-be-done, pain points, desired gains, and unexpected behavioral insights to guide product decisions.

Create a working customer profile from existing research, market signals, and team knowledge before deeper validation.

当个人希望提升自身在职业或社会中的不可替代性，以应对环境变化和不确定性时

去除或避免文本中的 AI 味。用于用户强制调用本 Skill，或写作/改写时提到“不要有 AI 味”“不要太假”“真情实感”“有人感”，或对已有文本/上一轮输出反馈“太像 AI”“太空泛”“太模板”“太官方”“不像我说的”等场景。

Returns the user's input back to them along with the current time, serving as a simple test skill.

Research a specific person for hyperpersonalized outreach using their public activity

Generate data-driven user personas for UX research and product design. Usage: /persona generate [options]

Embody the voice and methodology of Adam Smith for systematic, observational analysis of markets, ethics, and policy.

Expert test analysis specialist focused on comprehensive test result evaluation, quality metrics analysis, and actionable insight generation from testing activities

Talk to customers without leading them using Mom Test rules: discuss their life not your idea, ask about specifics in the past, and talk less. Use when the user mentions "customer interviews", "validate my idea", "users say they want it but dont buy", "leading questions", "The Mom Test", "customer feedback bias", or "interview script". Also trigger when preparing user research questions, interpreting ambiguous customer feedback, or designing customer discovery processes that avoid false positives. Covers commitment and advancement, avoiding compliments, and extracting signal from noise. For product-market fit, see jobs-to-be-done. For rapid prototype testing, see design-sprint.

Close the PDCA loop on this session's diff. Headed playwright-cli, fix as you go. Use when "test my changes", "PDCA this". Pixel diffs → test-visual-regression. Story CRUD → test-qa. Monkey / guest vs logged-in → test-exploratory.

Pressure-test any business idea with a five-angle adversarial panel (Critic, Champion, Analyst, Investigator, Customer) that returns a single GO/RESHAPE/KILL verdict with the cheapest test to de-risk it.

Per-page UX audit using NN/g heuristics, Intuit microcopy, and Google HEART. Use when evaluating usability, auditing microcopy, checking UX heuristics, or assessing cognitive load. Cross-page flows/IA → audit-ux-journeys. Layout/breakpoints → audit-responsive. Empty/error states → audit-ui-states.

搜索 NPC 角色。

Design a usability test plan with tasks, success metrics, participant criteria, and facilitation guide for moderated or unmoderated sessions.

Run Karpathy-style autonomous ML search on a real training repo: choose the right mode (setup, program.md, bounded loop, results interpretation, or constrained-hardware adaptation), preserve the immutable prepare.py / 300-second / val_bpb contract, and route prompt/skill eval work away to LangSmith, Promptfoo, Braintrust, or skill-autoresearch.

Analyze any text to extract a cognitive fingerprint across 12 dimensions, revealing reasoning patterns, decision styles, and thinking signatures.