Results for “log-parsing”

Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.

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

debug-logging

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

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

Reads and analyzes application logs for debugging and monitoring.

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.

Tail, read, or search a Stata log file from a previous command or background task.

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.

Log and search local text entries from the command line. Use when adding quick notes, searching past entries, or exporting a simple activity log.

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

Build ontology logs (ologs) from problem descriptions using categorical foundations. Use when designing problem taxonomies, classifying tasks for routing, building knowledge libraries, establishing formal analogies between domains via functor search, or translating between natural language and database schemas. NOT for OWL/RDF ontology work, query tuning, or graph modeling without functional-arrow discipline.

Systematically diagnose and fix software bugs by analyzing error messages, stack traces, logs, and runtime behavior across multiple languages. Use when the user requests debugging or provides relevant inputs for this workflow.

Build and debug Retrieval-Augmented Generation pipelines — chunking, embedding, retrieval, reranking

Log component for displaying log content with syntax highlighting, lazy loading, and scroll controls. Invoke when user needs to implement log viewers, terminal output, or console displays in Naive UI.

Loki is a horizontally scalable log aggregation system built by Grafana Labs. Unlike traditional log platforms that index the full text of every log line, Loki indexes only metadata labels, making it significantly cheaper to operate. It integrates natively with Grafana for querying and visualization, and uses LogQL — a query language inspired by PromQL — to filter, parse, and aggregate log streams.

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

Search and analyze your own session logs (older/parent conversations) using jq.

Structured logging strategy — levels, correlation IDs, context propagation, PII avoidance. Use when designing a logging approach, reviewing log statements, setting up log aggregation (ELK, Loki, CloudWatch), adding correlation IDs to a distributed system, or deciding what to log and what to keep out.

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.

Diagnose the current OMC session or repo state using logs, traces, state, and focused reproduction

Systematic debugging — reproduce, read the error, isolate, hypothesize, fix, verify, document. Use when tracking down a bug, unexpected behavior, or test failure — including phrases like "this doesn't work", "why is this failing", or "help me debug".

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.

Query and filter Datadog logs from the shell using the Composio CLI. Run scoped log searches, pivot across services/environments, and export structured JSON for incident investigation and diagnostics.

Logs the outcome of a skill execution to observations/<skill-name>/runs.md. Trigger on: "log this run", "skill worked", "skill failed", "this didn't work", "log the outcome", "record this", "note that", or after any skill completes with a clear success, partial, or failure outcome. Creates the observations file if it does not exist, then appends an entry with date, task description, skill used, outcome, what worked, what failed, and any error messages observed.

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`

Search and analyze conversation history stored in session JSONL files using jq and shell commands.

Capture delivery evidence, commands, changed files, blockers, and standards feedback after a work session.

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

Append-only event log — the probabilistic record of everything that happened in the project. Use whenever something notable happens that the project should remember — work items created/transitioned, decisions recorded, bindings changed, incidents occurred, releases shipped.

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