「memory」の検索結果

Signals scout that watches a PostHog project's most-viewed dashboards and insights for recent anomalies — sudden bursts, drops, flat-lines, and trend breaks at the daily or hourly level. It discovers what the team actually looks at (view counts, dashboard access), curates a durable watchlist in the scratchpad, and balances re-checking known high-value insights (exploit) against discovering new ones (explore) across runs, since no single run can cover a busy project. Anomalies are scored by robust deviation from each insight's own seasonality-matched baseline; it emits a finding only when a move clears the confidence bar, otherwise it updates the baseline memory and closes out empty. Self-contained peer in the signals-scout-* fleet.

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Focused Signals scout for PostHog projects running A/B experiments. Watches running experiments for validity threats (sample ratio mismatch, multi-variant contamination, exposure stalls, mid-run flag mutations) and lifecycle drift (zombie experiments running long past their useful life, decided-but-still-running experiments, ended experiments whose flags still serve multiple variants). Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

日本語の概要は準備中です。原文の説明を表示しています。

Focused Signals scout for PostHog projects using feature flags. Watches the flag roster and the `$feature_flag_called` evaluation stream for contradictions between a flag's configured state and its real traffic: evaluation cliffs on healthy flags, ghost flags (code calling keys that no longer exist), response-distribution shifts with no corresponding flag edit, and flag debt (stale, fully-rolled-out, or dead flags still burning evaluations). Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

日本語の概要は準備中です。原文の説明を表示しています。

Focused Signals scout for PostHog setup health. Reads the project's active health issues — the deterministic findings of PostHog's own health checks (no live events, outdated SDKs, missing reverse proxy, absent web vitals, ingestion warnings, failing data-warehouse models, and more) — and decides which are genuinely worth surfacing. Unlike a one-signal-per-issue push, it bundles kind-clusters into a single finding, weights by real blast radius (cross-referencing actual event volume and reach), and prioritizes issues an agent can resolve via the MCP. Emits only above the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

日本語の概要は準備中です。原文の説明を表示しています。

Follow-up scout for the Signals inbox itself. Watches reports that recently transitioned to resolved (an implementation PR merged) and, after a deployment soak window, re-measures the underlying problem to check the fix actually held — plus a strictly-gated escalation check on recently dismissed reports. Emits findings only when a shipped fix demonstrably didn't hold; confirmations and unverifiable verdicts become durable memory and an empty close-out. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

日本語の概要は準備中です。原文の説明を表示しています。

Focused Signals scout for PostHog projects using logs. Watches for volume bursts, severity-distribution shifts, service silence, fresh message patterns, and trace-correlated bursts via the logs ingestion pipeline. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

日本語の概要は準備中です。原文の説明を表示しています。

Focused Signals scout for PostHog projects running Replay Vision scanners — the standing LLM probes that watch session recordings and write `$recording_observed` events. Watches two promises: that enabled scanners are actually observing (throughput / success-rate cliffs, exhausted quota — a silent watch gap), and that what the scanners see in aggregate gets surfaced (a monitor's `yes`-rate or a scorer's score stepping away from its own baseline, a classifier tag or a recurring summarizer theme concentrating across many sessions). It is the agentic pull complement to the per-session push path: scanners with `emits_signals` already emit one signal per session into this same inbox, so this scout never repeats them — it adds the cross-session shape the per-session probe can't see. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet.

日本語の概要は準備中です。原文の説明を表示しています。

Focused Signals scout for PostHog projects with web traffic. Watches the acquisition and site-health layer the web analytics product reports on: per-channel session volume diverging from the site's own rhythm (an acquisition source silently collapsing or surging), attribution breakage (paid/campaign traffic reclassifying into Direct or Unknown when tagging breaks), landing pages that break (bounce-rate steps, 404 spikes, entry-path cliffs), and page-performance regressions (web vitals p75 steps). Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet.

日本語の概要は準備中です。原文の説明を表示しています。

Focused Signals scout for PostHog projects running surveys. Watches active surveys for score regressions (NPS / CSAT / rating drops), response-volume drops, abandonment spikes, and targeting drift, AND aggregates open-text responses into recurring themes the team should know about (clusters of complaints, praise, feature requests). Emits findings only when a theme or anomaly clears the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

日本語の概要は準備中です。原文の説明を表示しています。

Focused Signals scout for PostHog projects using revenue analytics. Watches the derived revenue product for upstream failures (Stripe sync stalls, capture regressions), config drift (missing subscription property, currency mix surprises, broken Stripe↔person joins, deferred-revenue gaps), and goal-miss escalations. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

日本語の概要は準備中です。原文の説明を表示しています。

Focused Signals scout for PostHog projects using session replay. Watches two promises the replay product makes: that sessions are actually being recorded (capture integrity — recording volume vanishing while site traffic doesn't), and that the friction evidence inside recordings gets seen (rage-click / dead-click clusters concentrating on a page or element, error-after-interaction cohorts, recurring replay vision themes nobody aggregates). Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet.

日本語の概要は準備中です。原文の説明を表示しています。

How to explore and make sense of PostHog Signals scouts — the scheduled agents that scan a project and emit findings into the Signals inbox. Use when a user wants to understand what scouts they have, how each one is behaving, and whether the fleet is actually working. Covers surveying the fleet and its schedules, reading recent scout runs and drilling into a single run's reasoning, inspecting the durable scratchpad memory the fleet has built up, tracing a run to the findings it emitted, and assessing a scout's health and performance over time (cadence, success rate, emit rate, signal-to-noise). Read-only and exploratory — to write or tune a scout, use `authoring-signals-scouts` instead. Trigger on "what are my scouts doing", "how is my <x> scout performing", "show me recent scout runs", "why did this scout find/emit nothing", "what has the fleet learned", "explore scout run <id>", "is my scout working".

日本語の概要は準備中です。原文の説明を表示しています。

How to author, edit, and adapt PostHog Signals scouts — the scheduled agents that scan a project and emit findings into the Signals inbox. Use when a user wants to customize a canonical scout for their own setup (narrow its scope, retune its thresholds, add disqualifiers), tweak a scout's schedule or dry-run posture, or write a brand-new scout from scratch for a specific use case (a custom event, a product surface no canonical scout covers). Covers the scout SKILL.md anatomy, the emit contract, the dedupe + scratchpad-memory conventions, the per-team skills-store path vs the canonical in-repo path, and the emit-and-inspect test loop (with dry-run as an optional safety net). Trigger on "write/edit/customize a signals scout", "new scout for X", "tune my scout schedule", "make a scout that watches <event>".

日本語の概要は準備中です。原文の説明を表示しています。

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

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Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

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Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.

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Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

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Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

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Authors reproducible bioinformatics pipelines with Snakemake - rules wired by output-file pattern, wildcards and expand() for sample fan-out, checkpoints for runtime-unknown outputs, resource/retry escalation, and conda/container software deployment on HPC and cloud. Use when deciding rule-based (Snakemake) vs channel/dataflow (Nextflow) authoring; wiring rules by OUTPUT-file pattern rather than imperative order; using wildcards + expand() for sample fan-out and constraining them to stop silent mis-routing; adding checkpoints when the set of outputs is unknown until a step runs (dynamic DAG); diagnosing why a job reran (or did not) under the mtime-plus-provenance trigger set; escalating memory on retry for OOM-killed jobs; and porting a Snakemake 7 `--cluster`/remote-provider command to the Snakemake 8+ executor-plugin and storage-plugin model (snakemake-executor-plugin-slurm) with `--software-deployment-method`.

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Runs and configures curated nf-core community Nextflow pipelines (rnaseq, sarek, atacseq, methylseq, ampliseq, taxprofiler, fetchngs) reproducibly, pinning the pipeline revision with -r and selecting a container engine and institutional config via -profile. Use when deciding to adopt a community pipeline versus author one from scratch; picking a pipeline and pinning its -r revision; selecting -profile test/docker/singularity/conda plus an institutional config from nf-core/configs; building and validating a samplesheet CSV against the pipeline schema (nf-schema); choosing --genome/iGenomes versus custom references; configuring resources and max_memory for SLURM/AWS Batch; using -resume and -stub; and reading MultiQC outputs.

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Install, set up, verify, update, pin, uninstall, or troubleshoot the ToolUniverse plugin on OpenAI Codex. ALWAYS consult this skill for any of those — don't answer from memory, because the exact marketplace name (mims-harvard/ToolUniverse), the "codex plugin marketplace add" then "codex plugin add -m tooluniverse" flow, Codex's startup auto-upgrade behavior, the uvx tooluniverse MCP server, and the API-key env vars are easy to get wrong. Use it whenever someone wants to get ToolUniverse (or "the 1000+ scientific tools" / "the harvard tools") working on Codex, says the Codex plugin or its tools/skills won't load, hits a uvx or MCP-server startup error, asks how Codex updates it, wants to pin or remove it, or finds it running an old tool version — even if they never say the word "plugin". Not for the Claude Code plugin (use tooluniverse-claude-code-plugin), for running research with the tools, or for authoring new tools or skills.

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Automated CLI-based parallel agent execution — spawn subagents via Gemini CLI, coordinate through MCP Memory, monitor progress, and run verification

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Design multi-step AI agent architectures with tools, memory, and orchestration. TRIGGERS - Use when user wants to design AI agents, multi-agent systems, or autonomous AI workflows.

日本語の概要は準備中です。原文の説明を表示しています。