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Guides AI agents through Senzing entity resolution workflows using the Senzing MCP server. Covers data mapping to Senzing format, SDK code generation (Python, Java, C#, Rust, TypeScript/Node.js), documentation search, error troubleshooting, sample data access, reporting and visualization, SDK setup guides, and V3-to-V4 migration. Use when working with entity resolution, record matching, record linkage, deduplication, Senzing SDK integration, data mapping for Senzing ingestion, or troubleshooting Senzing error codes. Also use when the user mentions matching records across data sources, finding duplicate entities, identity resolution, master data management, or needs to resolve who is who across datasets.

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Batch-generate images via OpenAI Images API. Random prompt sampler + `index.html` gallery.

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Use this skill when a user asks to polish, rewrite, humanize, de-AI, localize, or oralize Chinese writing, including short-video scripts, articles, social posts, speeches, newsletters, and personal-IP content. The skill diagnoses “AI味”, preserves the user's meaning, adapts to optional style samples, replaces translation-like phrasing with natural Chinese expression, and outputs a polished version plus concise revision notes.

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Run evidence-disciplined Xiaohongshu social listening for foreign pharma employee pain points. Use when the user asks to collect and analyze notes/comments about pharma companies, aliases, roles, compliance, medical affairs, or AI topics, then produce a cited and scored sampled-insight report. Includes independent-evidence counting, MediaCrawler batch collection, and CAPTCHA/461 fallback to Agent Reach/OpenCLI.

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Locally modified fixture.

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Shared fixture from the repository.

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Use this skill when the implementation status column of a statement of applicability may not match reality, and the team asks "is our SoA accurate", "check the implemented column", "do we have evidence for every implemented control", "reconcile the SoA with our evidence", "pre-audit evidence check" or "the auditor found a control marked implemented with no records". Samples the records behind every control marked implemented, grades the evidence, proposes a corrected status for each row and writes a reconciliation table plus an evidence gap list with owners.

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Use this skill when a team suspects its policies describe a company that does not exist, before an audit or customer review, or asks "do our policies match what we actually do", "check the policy against reality", "paper versus practice", "will the auditor find gaps in our policies", "our policy says quarterly but we never do it" or "align policies with how we work". Turns each policy into testable statements, samples the systems and records that show the practice, classifies every gap and proposes whether to fix the policy or the practice, with owners and priorities.

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Use this skill when an auditor, assessor or regulator has sent a request list and the team asks "turn the auditor's request list into tasks", "who needs to provide what", "build the evidence tracker", "split the PBC list by owner", "what exactly do they want for this request" or "we have two weeks to send everything". Normalises each request into owned, dated tasks naming the exact artefact, period and source system, separates population requests from sample requests, batches the clarification questions, and writes a tracker plus one brief per owner.

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Use this skill when a company needs a written risk assessment method before it builds or rescores a register, or when the current method produces registers where everything scores 3. Triggers: "write our risk methodology", "define likelihood and impact scales", "what is our risk appetite", "risk acceptance criteria", "who can accept a risk", "our auditor says the method is not repeatable", "risk matrix for our ISMS". Drafts a short method with likelihood and impact anchors in the company's own money, hours and records, a scoring rule, an acceptance threshold with an authority ladder, reassessment triggers, and a calibration test on five sample risks.

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Runs an 11-step data cleaning and variable screening pipeline for pre-loan credit risk models: sample analysis, OOS split, missing-rate, IV, PSI, and correlation filters. Use when preparing raw credit data for scorecard or default-prediction modeling.

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Profiles native macOS and iOS apps from the command line only: xctrace recording, Time Profiler samples, hotspot ranking, and symbolication with atos and vmmap. Use for headless or SSH-based performance analysis without opening the Instruments GUI.

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Validate Commodore 64 SID 6581/8580 sound emulation in EMULATORS, including $D400-$D7FF register decoding, voices, frequency, pulse width, control bits, ADSR/envelope behavior, oscillator sync/ring/noise/test, filters, volume DAC/sample clicks, paddles, read-only registers, mirrors, and reSID integration. Use when Codex reviews or edits SID, Sound, audio wrapper, register, filter, waveform, envelope, or mapped SID behavior.

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Read a media file's real structure with ffprobe through media_run - the JSON invocation and how to read it, what every family of field means, how to count frames, sample without decoding everything, detect HDR and interlacing, and diagnose a missing duration, a stream with no frames or a container that lies. Covers when media_probe's own report is already enough.

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Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

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Detect sandbox evasion techniques in malware samples by analyzing timing checks, VM artifact queries, user interaction detection, and sleep inflation patterns from Cuckoo/AnyRun behavioral reports

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Executes malware samples in Cuckoo Sandbox to observe runtime behavior including process creation, file system modifications, registry changes, network communications, and API calls. Generates comprehensive behavioral reports for malware classification and IOC extraction. Activates for requests involving dynamic malware analysis, sandbox detonation, behavioral analysis, or automated malware execution.

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Analyzes bootkit and advanced rootkit malware that infects the Master Boot Record (MBR), Volume Boot Record (VBR), or UEFI firmware to gain persistence below the operating system. Covers boot sector analysis, UEFI module inspection, and anti-rootkit detection techniques. Activates for requests involving bootkit analysis, MBR malware investigation, UEFI persistence analysis, or pre-OS malware detection.

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Analyzes malicious Linux ELF (Executable and Linkable Format) binaries including botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure. Covers static analysis, dynamic tracing, and reverse engineering of x86_64 and ARM ELF samples. Activates for requests involving Linux malware analysis, ELF binary investigation, Linux server compromise assessment, or container malware analysis.

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Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.

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Batch-generate images via OpenAI Images API. Random prompt sampler + `index.html` gallery.

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Prepares a DRAFT internal audit pack from the audit plan: scope as stated, criteria with clauses quoted from the documents provided, document requests per auditee with due dates, open interview questions per process and role, previous findings to follow up and a readiness checklist. Never pre-judges conformity, writes findings or selects the sample; the lead auditor decides. Use when the user asks to "prepare the internal audit", "build the audit pack from this plan", "draft the document requests and interview questions" or "get us ready for the supplier audit". Do not use for evidence requests built from a control list, use control-evidence-request-pack instead; to chase actions from earlier audits, use corrective-action-tracker. Drafts for human review; never approves, authorises or signs off.

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Turns a control list and an audit scope into draft evidence requests grouped by control owner, each with the control as stated, the audit period, evidence examples (commonly requested) and a proposed due date derived from the fieldwork dates. Never judges whether evidence is sufficient, never selects the sample and never sends a request; the auditor decides. Use when the user asks to "prepare the evidence requests", "draft the PBC list", "build the evidence request pack for the audit" or "what do we need to ask each control owner for". Do not use for document requests and interview questions built from an audit plan, use audit-prep-pack instead; to check claims in a report against sources, use claims-evidence-map. Drafts for human review; never approves, authorises or signs off.

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Designs a pre-launch evaluation plan for a declarative agent from its instructions text, skills, capabilities, knowledge sources and sample requests: a behaviour contract of testable statements quoted from the text, test prompts by scenario family and tier with setup, expected behaviour, pass and fail criteria and red flags, a red-flag catalogue, a blank scoring sheet, a coverage table and a go or no-go checklist the owner completes and decides. Use when the user asks to "write test cases for my agent", "how do I evaluate this agent before launch", "build a test plan for the agent", "what should I check before publishing this agent" or "give me a go no-go checklist". Do not use for finding injection or leakage exposures in the instructions, use agent-instructions-red-team instead; do not use for reviewing one SKILL.md, use skill-file-reviewer instead. Drafts for human review; never approves, authorises or signs off.

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