Results for “logs-ingestion”

Ingests case studies, past proposals, and boilerplate from library/inbox into structured indexes. Use when the user requests to 'index proposal library', 're-index case studies', 'ingest inbox docs', or 'validate library'.

Detect intrusion attempts, unauthorized access, and privilege escalation on Linux hosts using the auditd framework with ausearch and aureport utilities.

Search archived video using natural language, ingest video files or RTSP streams, and manage ingested sources.

Parses Simplicité log files from raw .txt format, filters fields to reduce noise, and outputs structured JSON for efficient AI context usage.

Upload offline conversions, enhanced conversions for leads, click conversions, and Google Analytics events using the Data Manager API /v1/events/ingest endpoint.

Parse Apache and Nginx access logs to detect SQL injection, LFI, XSS, scanner fingerprints, and brute-force patterns using regex-based detection, GeoIP enrichment, and statistical anomaly analysis.

Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts using pandas for statistical analysis and anomaly detection.

Build an append-only log integrity chain using SHA-256 hash chaining for tamper detection. Each log entry is hashed with the previous entry's hash to create a blockchain-like structure where modifying any entry invalidates all subsequent hashes.

Analyze WAF logs from ModSecurity, AWS WAF, or Cloudflare to detect SQL injection attack campaigns, classify injection types, and generate incident reports with OWASP classification.

Pre-ingestion verification for epistemic quality in RAG systems with 9-point verification and Two-Round HITL workflow

Integrate new data sources into SIEM platforms by configuring collectors, parsers, normalization, and validation for security monitoring.

Analyze Linux system logs including auth.log, syslog, systemd journal, and auditd to reconstruct user activity, detect unauthorized access, and establish event timelines on compromised systems.

Routes runtime-log requests into an evidence packet to isolate the first actionable blocker, repeated signature, blast radius, or safest next read-only check.

Diagnose your own behavior by reading the structured logs you leave behind — turns.jsonl, events.jsonl, chat_history.jsonl, scheduler.yaml. Use when something has gone wrong (a message didn't land, a scheduled job isn't firing, a communication pattern feels off), when you need to understand a pattern over many turns, or when cost / token usage needs auditing. Covers jq query recipes and points at the debugging-jobs / debugging-communication / debugging-drift companion guides for specific failure modes.

Create patient intake forms with history and screening. TRIGGERS - Use when user needs help with intake-form related tasks.

Standards for structured logging and observability in Golang. Use when adding structured logging or tracing to Go services.

Implements structured logging and centralized log aggregation with Loki, Promtail, Elasticsearch, and Fluentd, including configuration, retention policies, and log analysis.

Team-Wiki Ingest

Collect, parse, and correlate system, application, and security logs to reconstruct events and establish timelines during forensic investigations.

Turns a pile of bugs, issues, and error reports into a ranked, actionable plan with reproduction evidence. Ingests GitHub issues via gh, TODO/FIXME scans, error logs, or a pasted list; attempts to reproduce or classify every item; scores by impact, frequency, fix cost, and regression risk; clusters duplicates; and.

Python logging with the standard library logging module and structlog. Covers log levels, handlers, formatters, structured logging, and production best practices for FastAPI/Django applications. USE WHEN: user mentions "python logging", "fastapi logging", "django logging", asks about "how to log in python", "python logging module", "logging configuration python" DO NOT USE FOR: Node.js logging - use `nodejs-logging` instead, Java logging - use `slf4j` or `logback`, structlog-specific - use `structlog` skill for deep dive

Log workouts, plan routines, track progress, and get intelligent coaching for any fitness level.

Test LLM applications for system prompt leakage using manual payloads, garak, and Promptfoo to extract embedded secrets and routing logic.

Java logging with SLF4J facade, Logback, and Log4j2 implementations. Covers configuration, log levels, structured logging, async logging, and production best practices for Spring Boot applications. USE WHEN: user mentions "java logging", "spring boot logging", "slf4j setup", asks about "how to log in java", "logback vs log4j2", "java logging best practices" DO NOT USE FOR: Node.js logging - use `nodejs-logging` instead, Python logging - use `python-logging`, Kotlin-specific logging - similar but has nuances

debug-logging

Investigate a problem, log it to the debug changelog, cross-check past issues, and suggest a non-duplicate fix.

Ingests music into a LanceDB via ncmdump-rs and sf-cli, searching and downloading from Netease Cloud Music or Bilibili, decrypting local NCM files, and extracting metadata, lyrics, and cover art.

Reads and analyzes application logs for debugging and monitoring.

Onboard into an unfamiliar repository fast before making any changes

Provides an immutable audit and provenance layer for agentic systems, recording reasoning, telemetry, and actions for debugging, compliance, and learning.

Logging in Acorn/WordPress — Log::debug(), Log::error(), Log::channel(), custom log channels, daily log files, stack channel, Slack log notifications, exception handling logging, WordPress debug.log integration, WP_DEBUG_LOG, structured logging, context arrays, monolog handlers, log levels, emergency info warning error debug, lando logs, lando ssh tail log

Security controls for RAG. Indirect prompt-injection via retrieved documents, PII detection/redaction (Microsoft Presidio, AWS Comprehend), multi-tenant isolation, ACL-aware retrieval with row-level/metadata filtering, data-leakage prevention, jailbreak hardening on retrieved context, GDPR right-to-be-forgotten in vector DBs. USE WHEN: user mentions "prompt injection RAG", "indirect prompt injection", "PII redaction", "Presidio", "ACL RAG", "row-level security", "multi-tenant RAG isolation", "GDPR vector DB", "right to be forgotten", "jailbreak", "data leakage RAG" DO NOT USE FOR: hallucination detection - use `rag-guardrails`; tenancy scaling patterns - use `rag-production`; audit tracing schema - use `rag-observability`

Detects defense evasion techniques in endpoint logs, including log tampering, timestomping, process injection, and security tool disabling, using Sysmon, EDR telemetry, and SIEM queries.

Caching strategies across the RAG stack. Semantic caching with GPTCache and LangChain, Redis-based embedding-similarity cache, cache key design, TTL/invalidation, partial caching (cache retrieval only), provider-native prompt caching (Anthropic, OpenAI), and hierarchical L1/L2 caches. USE WHEN: user mentions "semantic cache", "GPTCache", "LLM cache", "prompt caching", "Redis vector cache", "cache invalidation for RAG", "reduce LLM cost", "latency reduction LLM" DO NOT USE FOR: retrieval accuracy - use `rag-patterns`; groundedness checks - use `rag-guardrails`; incremental indexing - use `rag-production`

JNDI injection playbook. Use when Java applications perform JNDI lookups with attacker-controlled names, especially via Log4j2, Spring, or any code path reaching InitialContext.lookup().