76 个（Tencent 的仓库） · 按人气排序
Extract text and tables from PDFs, fill form fields with supplied data, and merge documents. Includes a pdfplumber text extraction example and scripts for inspecting form fields and extracting text.
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Use the weknora CLI to manage knowledge bases and documents, search stored material, and ask questions grounded in it. This shared guide covers authentication, structured JSON results, error handling, and previews of changes.
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Choose the right WeKnora command to answer questions from a knowledge base or retrieve source passages. Covers cited answers, custom agents, document searches, and continuing a conversation through the command-line tool.
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Automate a logged-in Chromium browser using the plugin’s injected tools. Read pages, fill forms, manage tabs, inspect page activity, and investigate website problems or test interfaces while working with the user’s existing browser logins.
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Use the bsk command-line tool and browser extension to automate logged-in Chromium. Read pages, complete forms, export tables or lists as JSON or CSV, manage tabs, test interfaces, and investigate website failures.
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Test whether instructions planted in an agent's long-term memory or searchable knowledge base influence later behavior. Verify both a stored test marker and a resulting change in answers, permissions, or treatment of retrieved documents.
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Test whether instructions hidden in simulated documents, retrieved passages, or web pages can redirect an agent. The skill checks for unwanted behavior or sensitive disclosures and reports confirmed failures with the test dialogue as evidence.
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Submit security scans for AI services, tools, skills, and agents through AI-Infra-Guard, then retrieve progress and results. It also supports jailbreak evaluation, testing whether prompts can bypass a model’s safety restrictions.
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Test whether an agent misuses user trust by requesting secrets, making false safety guarantees, recommending unsafe actions, or impersonating authority. Confirmed issues are recorded with dialogue evidence, potential impact, and suggested improvements.
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Assess AI products, agents, MCP servers, and skill packages through authorized security testing. It models access boundaries, tests injection and tool misuse, and produces evidence-based reports with business impact, defensive successes, and concrete remediation steps.
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Tests communication between collaborating AI agents for private data exposure, impersonation, unsafe message channels, and unauthorized access to privileged tools. It records confirmed problems with conversation evidence, potential impact, and suggested security measures.
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Review agent skills before installation or during use by inspecting their files without running code. Local checks identify risks such as credential theft, permission abuse, and differences between stated purpose and actual behavior.
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Turn a supplied PDF into a concise summary of three to five bullet points. The skill reads the document text, identifies its main sections and topics, and selects the key ideas to include.
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Tests whether an AI agent accepts unverified plugins, tools, models, or updates. It checks risks introduced through external dependencies, including unsafe installation sources and missing version controls, and reports confirmed findings with conversation evidence.
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Test whether an AI agent reveals sensitive information through progressively stronger dialogue prompts. Check API keys, personal data, system instructions, and internal settings, then report findings only when sensitive content is actually accessible.
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Tests whether user messages can override an AI agent’s instructions or impersonate a higher authority. It checks for exposed internal instructions and changed behavior, then reports confirmed findings with prompts and responses as evidence.
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Test whether an AI agent stores hostile instructions in persistent memory and follows them later. The skill uses marked test instructions and follow-up dialogue to document storage confirmations or changes in subsequent responses.
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Check an OpenClaw environment for risky settings, unsafe skills, known software vulnerabilities, and privacy exposure. It combines local checks with optional online threat lookups and also supports reviewing individual skills before installation.
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Test whether a browsing AI agent follows fake security verification instructions that expose stored personal information through URL paths. Examine chained navigation, memory disclosure, and constructed URLs, then document confirmed findings with evidence and mitigation suggestions.
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Test whether an AI agent executes commands hidden in user input, evaluates untrusted code, or runs scripts from unverified sources. Record confirmed weaknesses with the test conversation, potential impact, and suggested safeguards.
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Organize AI agent security findings using the ten OWASP ASI risk categories. Map issues such as leaked credentials or unauthorized tool calls to category IDs, and assign severity levels with suggested remediation timeframes.
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Check whether a single tool outage, invalid input, or repeated retry can disrupt an entire agent workflow. Use dialogue tests to identify spreading errors, missing fallbacks, and uncontrolled retries, then document confirmed findings.
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Probe AI agents through dialogue to check for unexpected command execution, file access outside intended paths, and prompted network requests. It selects tests based on available tools and records confirmed behavior with conversation evidence and mitigation suggestions.
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Test whether an AI agent reveals another user's data or performs actions beyond the caller's permissions. Dialogue probes cover access across users or organizations, admin functions, and attempts to gain higher privileges without authorization.
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