データ・AIのスキル

Optimize the description prompts an AI agent reads to learn its built-in tools (the `.md` files under prompts/tools/). Two halves: (1) measure how much of a prompt is already inferable from the tool's JSON parameter schema + name, to prune redundancy with evidence; (2) house authoring rules for what belongs in a tool prompt vs what stays in code. Use when auditing, trimming, writing, or reviewing tool prompts, deciding what schema field descriptions already cover, or testing schema-vs-prompt overlap before deleting prompt lines.

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Write system prompts, tool docs, and agent definitions. Project tag conventions + RFC 2119 keywords + dense compression. Use when authoring or editing any prompt the model reads.

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Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Supports both basic forecasting and advanced covariate forecasting (XReg) with dynamic and static exogenous variables. Automatically checks system RAM/GPU before loading the model, validates dataset fit before processing, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model and handle your specific dataset.

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Detect Living Off the Land Binaries (LOLBins/LOLBAS) abuse including certutil, regsvr32, mshta, and rundll32 via process telemetry, Sigma rules, and parent-child process analysis with Sysmon endpoint data. Use when hunting for adversaries abusing built-in Windows binaries to download, execute, or proxy malicious code while evading traditional executable-based detection.

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Analyze WAF (ModSecurity/AWS WAF/Cloudflare) logs to detect SQL injection attack campaigns. Parses ModSecurity audit logs and JSON WAF event logs to identify SQLi patterns (UNION SELECT, OR 1=1, SLEEP(), BENCHMARK()), tracks attack sources, correlates multi-stage injection attempts, and generates incident reports with OWASP classification.

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Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.

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Detect and defend against indirect prompt injection hidden in web pages, documents, and images consumed by an agent, via content extraction (HTML/PDF/OCR), normalization, and scanning with LLM Guard's PromptInjection scanner or Hugging Face Prompt Guard 2. Use when an agent ingests untrusted external content and you need to screen it for injected instructions before the LLM processes it.

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Detect insider threat behavioral indicators including unusual data access, off-hours activity, mass file downloads, privilege abuse, and resignation-correlated data theft. Use when proactively threat-hunting for malicious or negligent insider activity, or when investigating a departing or disgruntled employee for potential data theft.

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Performs statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns. Uses the ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic connections with low jitter. Use when hunting for command-and-control callbacks in network data.

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Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.

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Implement User and Entity Behavior Analytics (UEBA) using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and alert on insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access. Use when building or tuning a UEBA pipeline rather than a one-off manual hunt.

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Detect command-and-control (C2) traffic tunneled over DNS from tools like Iodine, dnscat2, dns2tcp, and Cobalt Strike DNS beacon, using Shannon entropy analysis of query subdomains, ML-based DGA classification, passive DNS correlation, and Zeek/Suricata signatures. Use when investigating suspected DNS tunneling, classifying DGA domains, detecting DNS beaconing, or building DNS anomaly rules for a SOC/SIEM.

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Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs. Uses pandas for behavioral analytics and statistical baselines. Use when investigating insider threats or building user behavior analytics for data loss prevention.

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Identify poisoned training data and backdoored ML models across the pipeline using IBM's Adversarial Robustness Toolbox (activation clustering, spectral signatures, trigger reconstruction), Cleanlab for label-quality issues, and supply-chain checks like weight-hash verification and safetensors enforcement. Use before training or deploying on third-party/user-contributed data or downloaded checkpoints, during ML supply-chain reviews, or when investigating model misbehavior tied to specific inputs (suspected backdoor trigger).

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Detect DNS-based data exfiltration by analyzing Zeek dns.log for high-entropy subdomains, oversized TXT/NULL records, and anomalous query volume or patterns. Use when investigating suspected DNS tunneling, covert C2 over DNS, or data exfiltration hidden in DNS queries against network traffic captured by Zeek.

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Detects prompt injection using regex signature matching, heuristic scoring for structural anomalies, and DeBERTa-based transformer classification, flagging direct injections (system-prompt overrides, role-play escapes) and indirect injections (encoded payloads, obfuscation) per OWASP LLM Top 10 (LLM01:2025). Use for input validation layers in chatbots/agents/RAG pipelines, or for retrospectively classifying injection attempts in logs or incident investigations.

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Wires Promptfoo and DeepTeam into CI/CD for automated, repeatable red-teaming of LLM apps against OWASP LLM Top 10, OWASP Agentic, and MITRE ATLAS presets, failing the build when jailbreak or injection vulnerabilities regress. Use for continuous adversarial testing in CI/CD, a merge-blocking security gate, or comparing model/prompt versions for compliance reporting.

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Build collaborative forensic incident timelines using Timesketch to ingest, normalize, and analyze multi-source event data (including Plaso output) for attack chain reconstruction and investigation documentation. Use when reconstructing the sequence of events during an incident investigation or when multiple analysts need to jointly tag, annotate, and search a shared DFIR timeline.

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Apply bottom-up and top-down role mining techniques, including clustering algorithms and formal concept analysis, to discover optimal RBAC roles from existing user-permission assignments, consolidating overlapping roles and enforcing least privilege. Use when an identity program needs to reduce role explosion or redesign its RBAC role set from access data.

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Query a MISP (Malware Information Sharing Platform) instance via PyMISP to compute event statistics, IOC type breakdowns, threat actor galaxy clusters, and tag trends, and generate threat landscape reports with temporal trends. Use when asked to analyze threat intelligence data, summarize top threat actors or malware families, or produce a CTI landscape report from MISP events.

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Test RAG vector stores (Pinecone, Qdrant, Weaviate, Chroma, pgvector, FAISS) for embedding inversion, cross-tenant data leakage, and data poisoning per OWASP LLM08:2025. Use when performing an authorized security assessment of a RAG pipeline's retrieval layer or auditing multi-tenant vector-store isolation.

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Analyzes structured and unstructured threat intelligence feeds to extract actionable indicators, adversary tactics, and campaign context. Use when ingesting commercial or open-source CTI feeds, evaluating feed quality, normalizing data into STIX 2.1 format, or enriching existing IOCs with campaign attribution. Activates for requests involving ThreatConnect, Recorded Future, Mandiant Advantage, MISP, AlienVault OTX, or automated feed aggregation pipelines.

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Parse Apache and Nginx access logs to detect SQL injection attempts, local file inclusion, directory traversal, web scanner fingerprints, and brute-force patterns. Uses regex-based pattern matching against OWASP attack signatures, GeoIP enrichment for source attribution, and statistical anomaly detection for request frequency and response size outliers.

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Map advanced persistent threat (APT) group TTPs to the MITRE ATT&CK framework using the attackcti Python library to query STIX/TAXII data for group-technique associations, then generate ATT&CK Navigator layer files to visualize and compare defensive coverage against adversary profiles. Use when profiling an APT group's techniques, building Navigator coverage heatmaps, or assessing technique coverage gaps against a specific threat actor.

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