Results for “cache-estimation”

Implements multi-level caching with strategies for invalidation, stampede prevention, and performance monitoring.

Estimates the memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub, using HTTP Range requests without downloading weights.

cache-usage

Exploit web cache mechanisms to serve malicious content to other users by poisoning cached responses through unkeyed headers and parameters during authorized security tests.

Exploit path normalization discrepancies between CDN caching layers and origin servers to cache and retrieve authenticated content.

SUPERSEDED by persistent-cache-gap-filling (v2.8.0). Cache data during symbol selection for instant repeat runs.

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`

Evaluates a containerized framework for deploying distributed big data workloads, measuring execution time and cloud cost scaling from four to eight nodes.

HTTP Caching

Evaluation of BaSiC illumination correction caching - NOT RECOMMENDED for sparse markers. Trigger: optimizing BaSiC, caching illumination correction

Improve cache efficiency, latency, and throughput for AMS deployments using the Adobe Dispatcher Apache HTTP Server module and related HTTPD configuration.

Analyzes .NET project code coverage and CRAP (Change Risk Anti-Patterns) scores to identify risk hotspots, methods blocking coverage gains, and prioritize where to add tests.

Detects and triages explicitly-unbounded AEM queries (p.limit=-1 or setLimit(-1)) that cause OOMs, safely capping only where provably safe and escalating others for human pagination.

Project estimation and time management for agent-orchestrated builds.

Configure Next.js cache layers, invalidation, and cache-component APIs. Use when choosing `fetch` caching, `use cache`, tags, or stale-data debugging in Next.js.

Benchmark, load test, capacity plan, and cache with k6, JMeter, Locust, and pgbench. Use when the user says "slow", "performance", "load test", "stress test", "how many users can it handle", "capacity", "cache", "benchmark", "k6".

Trigger: database schema, indexing, cache asidePattern, Redis caching, ORM models, Drizzle schema. Scope: Database query tuning, indexing, and data cache designs. Boundary: Excludes user identity validation.

Maps external dependencies and I/O boundaries, generates chaos tests for failure modes like timeouts and connection errors, runs them, and reports resilience gaps with fixes.

The default, because it fails open: when the cache is unavailable, reads still

Measure performance baselines, detect regressions before and after PRs, and compare stack alternatives using browser, API, and build benchmarks.

llama.cpp local GGUF inference + HF Hub model discovery.

Consume APIs from the frontend with TanStack Query, fetch, or SWR, including caching, refetching, optimistic updates, and error handling.

Local RAG indexing with 97% storage reduction via anchor-based lazy recomputation. Graph-based selective embedding storage for memory-efficient semantic code search.

Plan a sprint with capacity estimation, story selection, dependency mapping, and risk identification. Use when preparing for sprint planning, estimating team capacity, selecting stories, or balancing sprint scope against velocity.

Extract and analyze browser history, cookies, cache, downloads, and bookmarks from Chrome, Firefox, and Edge for forensic evidence of user web activity.

Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.

Estimates GPU memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub using HTTP Range requests, without downloading weights locally.

GPU-accelerated correlation matrix computation with persistent SQLite caching to eliminate bottleneck at correlation calculation during symbol selection

Searches a local-first memory index and returns the top matching entries with paths and headers.

Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.

Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.

Measures performance baselines, detects regressions before and after PRs, and compares stack alternatives.

Token budget analytics and ROI scoring for AI agent actions. Track token cost per fix, detect wasteful loops, auto-route to fast tier when burn rate is too high. Integrates with token-budget-guard.sh hook. Triggered by /cost-report, "how much did that cost", "token usage", "am I wasting tokens".