데이터 분석, 머신러닝, LLM 앱, 프롬프트 설계 (9.2천건)
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
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Builds document embedding pipelines with text chunking, embedding generation, indexing, and retrieval optimization. Use when users request "embedding pipeline", "document indexing", "text chunking", "RAG preprocessing", or "semantic indexing".
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Implements content safety filters with PII redaction, policy constraints, prompt injection detection, and safe refusal templates. Use when adding "content moderation", "safety filters", "PII protection", or "guardrails".
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Converts documents into clean, chunked datasets suitable for embeddings and vector search. Produces chunked JSONL files with metadata, deduplication logic, and quality checks. Use when preparing "training data", "vector datasets", "document processing", or "embedding data".
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Plans and implements data retention policies with archival strategies, compliance requirements, automated cleanup jobs, and cold storage migration. Use for "data retention", "data archival", "GDPR compliance", or "storage optimization".
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Creates comprehensive disaster recovery procedures with automated backup scripts, restore procedures, validation checks, and role assignments. Use for "database backup", "disaster recovery", "data restore", or "DR planning".
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Reduces LLM costs and improves response times through caching, model selection, batching, and prompt optimization. Provides cost breakdowns, latency hotspots, and configuration recommendations. Use for "cost reduction", "performance optimization", "latency improvement", or "efficiency".
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Generates deterministic seed data for development and testing with factory functions, realistic fixtures, and database reset scripts. Use for "data seeding", "test fixtures", "database seeding", or "mock data generation".
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Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one.
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Use when a draft in Turkish, English, or both reads as AI-generated and needs revision that removes machine writing patterns and translation calques while preserving meaning, citations, statistics, and the author's voice.
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Use when a complete research workflow — its instructions, skills, data boundaries, and tool connections — must be assessed for portability across AI agents or environments, so each component is explicitly mapped before migration rather than assumed to transfer.
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Use when selecting a statistical test with AI consultation, when assumptions need checking against the actual data, when an analysis plan or decision log must be written before running anything, or when results need APA-style reporting with effect sizes.
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Use when a study's materials, data, code, or preprint need an open-science release: license choice, a persistent DOI (Zenodo/OSF), README and metadata, and an access-and-embargo decision that respects ethics constraints.
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Use before sensitive, clinical, identifying, or raw interview data is placed in front of an AI tool, when transcripts or student materials need de-identification, or when a data-minimization gate must run before any analysis or drafting.
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You are helping a penetration tester exploit error-based SQL injection. The
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You are helping a penetration tester exploit UNION-based SQL injection. The
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You are helping a penetration tester exploit NoSQL injection vulnerabilities.
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Wi-Fi reconnaissance methodology — adapter selection, monitor mode and packet injection setup, regulatory domain handling, multi-band airspace mapping, hidden SSID discovery, BSSID/ESSID/channel/PMF/encryption fingerprinting, client probe analysis, vendor OUI lookup, war-driving with Kismet/airodump-ng/Wigle, and structured airspace data capture for downstream attacks. Use at the start of any wireless engagement to build the target map before active attacks; covers 2.4 GHz, 5 GHz, and 6 GHz (Wi-Fi 6E) bands and adapter compatibility for each.
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You are helping a penetration tester exploit blind SQL injection. The target
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You are helping a penetration tester exploit stacked query SQL injection
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Hunt NoSQL Injection — MongoDB operator injection ($where, $regex, $gt, $ne), CouchDB, Redis command injection, auth bypass via NoSQLi, data dump. Use when target uses MongoDB/Mongoose, CouchDB, Redis, or shows NoSQL error messages.
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Hunt Local File Inclusion (LFI), Remote File Inclusion (RFI), and Path Traversal — /etc/passwd read, log poisoning → RCE, PHP filter-chain RCE (no upload needed), php:// / data:// / zip:// / phar:// wrappers, RFI via allow_url_include, directory traversal read/write/delete. Covers OOB/blind LFI confirmation and false-positive discipline. Use when hunting file-include or path-traversal bugs on any target.
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Hunt JWT cryptographic failures — alg:none signature-stripping and RS256→HS256 key-confusion that let an attacker forge a token for any identity (e.g. an admin) without knowing a secret. Use when the app authenticates with a JSON Web Token (an `eyJ...` Bearer token in the Authorization header, a cookie, or a login response). This skill OWNS JWT signature/crypto forgery (alg:none, key confusion, kid/jku header injection); hunt-ato covers JWT as one ATO path, hunt-auth-bypass covers SSO/SAML token trust, hunt-api-misconfig covers non-crypto JWT handling. Critical when a forged token grants access to another user's data or an admin-only endpoint.
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Structured OSINT methodology framework: target definition, source selection, collection workflows, data correlation, timeline reconstruction, and reporting. Use to guide systematic OSINT campaigns or teach OSINT methodology.
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