Web3 PM Interview Skill

• Outcome: Pass
• Latest audit: Sep 16, 2026 · 6:56 AM UTC
• Version: 0.4.0

Overview

This is a markdown-only Web3 product-manager interview prep skill with no hidden code or persistence, though users should redact private career details and avoid real-value wallet practice.

Before installing, treat this as an interview-coaching knowledge pack. Do not paste raw recordings, private recruiter messages, compensation details, non-public company information, named interviewer analysis, or unnecessary personal data. For wallet practice, use testnets or burner wallets with trivial funds and avoid exposing seed phrases or private keys.

A.I.G

Vulnerability Patterns

• Skill Instruction Hijacking: Alters the agent's session goals or safety constraints when the skill loads
• Agent Memory Poisoning: Writes attacker-controlled rules into memory that affect later sessions
• Remote Payload Retrieval and Execution: Fetches external code whose behavior can change after review
• Embedded Malicious Code: Ships malicious scripts inside the skill and executes them locally
• Unauthorized Access and Privilege Escalation: Obtains permissions beyond the task's legitimate needs

SkillSpector

Vulnerability Patterns

• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Trigger Abuse: Overly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access
• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code

Findings (13)

Anti-Refusal Statement

High

• Category: Anti-Refusal

Content

*(注：若目前这些文件内容为空，建议先写好基础框架再开源，否则 AI 缺乏知识支撑)。*

#### 4. `SKILL.md`
*   非常完美，基本不需要大改。建议在 `## First Response Pattern` 之前加一句：“Always respond in the language the user uses to initiate the conversation.” (支持用户用任意语言发起对话)。

#### 5. `examples/anonymized-wallet-senior-pm-case.md` (新增)
*   **强烈建议补充一个真实的“Mock 输出”：** 把你用这个 Skill 跑出的最好的一次 Battle Plan 保存下来，脱敏后放在这里。这就像是 SaaS 产品的 Landing Page，用户看一眼就知道自己能得到什么。

• Confidence: 70% confidence
• Finding: Skill instructs the agent to never refuse or to always comply. Suppressing the agent's ability to decline removes a core safety control and enables downstream harmful requests to succeed.

Anti-Refusal Statement

High

• Category: Anti-Refusal

Content

*(注：若目前这些文件内容为空，建议先写好基础框架再开源，否则 AI 缺乏知识支撑)。*

#### 4. `SKILL.md`
*   非常完美，基本不需要大改。建议在 `## First Response Pattern` 之前加一句：“Always respond in the language the user uses to initiate the conversation.” (支持用户用任意语言发起对话)。

#### 5. `examples/anonymized-wallet-senior-pm-case.md` (新增)
*   **强烈建议补充一个真实的“Mock 输出”：** 把你用这个 Skill 跑出的最好的一次 Battle Plan 保存下来，脱敏后放在这里。这就像是 SaaS 产品的 Landing Page，用户看一眼就知道自己能得到什么。

• Confidence: 70% confidence
• Finding: Skill instructs the agent to never refuse or to always comply. Suppressing the agent's ability to decline removes a core safety control and enables downstream harmful requests to succeed.

Missing User Warnings

Medium

• Confidence: 95% confidence
• Finding: The launch copy explicitly encourages users to feed resumes and job descriptions into AI, but it does not warn about personal data, confidential employer information, or the need to redact sensitive details first. In this skill context, that omission is meaningful because resumes and JDs commonly contain PII, contact information, employment history, and potentially non-public hiring details that could be exposed to third-party AI systems or logs.

Missing User Warnings

Medium

• Confidence: 95% confidence
• Finding: The skill instructs users to 'use 3 wallets and complete send, swap, bridge, approve, revoke' as interview preparation without any safety guidance, testnet recommendation, or warning that these actions can incur real fees, trigger approvals, expose funds, or cause irreversible loss. In a wallet-focused Web3 context, this omission is materially risky because novices are especially likely to perform these actions on mainnet or with meaningful assets while still learning unfamiliar transaction mechanics.

Natural-Language Policy Violations

Medium

• Confidence: 95% confidence
• Finding: The file explicitly includes an 'english_level' intake field and later labels 'English risk' as a hiring concern based on ability to operate in global organization meetings. In an interview-prep skill, this can steer evaluation toward language-based screening without user opt-in, role-specific necessity, or regional/legal justification, creating bias and potential discriminatory treatment.

Vague Triggers

Medium

• Confidence: 93% confidence
• Finding: The invocation conditions are broad enough to match ordinary user requests like asking if an answer is good enough or providing a transcript, which can cause this skill to activate outside narrowly intended interview-scoring contexts. Overbroad triggering is dangerous because it can lead to inappropriate context capture, irrelevant instruction injection into unrelated conversations, or accidental override of a more suitable skill.

Natural-Language Policy Violations

Low

• Confidence: 66% confidence
• Finding: The file describes the skill as bilingual and later provides substantial Chinese-language content, but it does not explicitly state that language output is user-selectable. Because policy requires avoiding forced language or locale behavior without opt-in, this could be interpreted as an undocumented language constraint.

Natural-Language Policy Violations

Low

• Confidence: 84% confidence
• Finding: Line L07 requires `README.md` to explain content in English and Chinese, which is a natural-language locale requirement. The file does not indicate that this bilingual requirement is optional or justified by a clearly region-specific constraint, so it may conflict with language-choice policy expectations.

Natural-Language Policy Violations

Low

• Confidence: 82% confidence
• Finding: The file presents a full Chinese-language section in addition to English, but does not state whether language selection is optional or user-driven. Under the policy rule, imposing a specific language or locale without opt-in can be a natural-language policy concern, even if the intent is bilingual accessibility.

Natural-Language Policy Violations

Low

• Confidence: 86% confidence
• Finding: The tagline specifies the skill as "Bilingual" but does not indicate that language use is optional, user-selectable, or limited to a justified regional context. Under the policy, forcing a specific language or locale without opt-in is a natural-language policy concern.

Natural-Language Policy Violations

Low

• Confidence: 84% confidence
• Finding: Line L110 instructs the reader to 'Prepare English versions of core stories,' which imposes a language requirement in natural language. Because the file does not indicate this is optional, user-selected, or justified by a clearly documented role requirement, it may violate the language/locale policy constraint.

Natural-Language Policy Violations

Low

• Confidence: 89% confidence
• Finding: This markdown file is entirely written in Chinese, and nowhere indicates that the skill or review is intentionally limited to Chinese-speaking users or that an English alternative is available. Under the policy rule, forcing a specific language without user opt-in can be a natural-language policy violation.

Natural-Language Policy Violations

Low

• Confidence: 93% confidence
• Finding: This markdown file contains a natural-language recommendation: "Always respond in the language the user uses to initiate the conversation." That prescribes a locale/language behavior without explicitly offering user choice, which falls under the language policy check for natural-language policy violations.

Static analysis

No suspicious patterns detected.