Results for “mem”

Archive merged feature specs into project memory with provenance, sweep discovery, and gated cleanup

Explicitly save important knowledge to auto-memory with timestamp and context. Use when a discovery is too important to rely on auto-capture.

Create a practical onboarding plan for a new team member with outcomes, first-week structure, stakeholder introductions, learning path, and manager check-ins.

Produces an attorney-ready memo from a corpus of legal documents supplied by the user. Use when a user shows up with a folder, zip, or vault of case documents and asks for a case summary, case evaluation, litigation package, intake memo, matter overview, or "can you summarize this case for me." The skill ingests the corpus into a searchable index, OCRs anything non-searchable, inventories and diagnoses the practice area, loads the appropriate practice-area playbook module(s) (PI/tort, commercial litigation, IP infringement, or user-authored extensions), iteratively searches the corpus across eight core dimensions plus any module-specific dimensions, defers specialized document clusters (depositions, medical records, discovery, liens) to dedicated sibling skills, and synthesizes a cited memo.

Perform coverage-guided fuzzing of compiled binaries using AFL++ to discover memory corruption, crashes, and security vulnerabilities.

Regenerate the cross-repo roadmap aggregate in your Claude memory directory by walking every curated repo's `docs/specs/**` frontmatter. Run at session start for cross-repo orientation or after any `spec-new`/`spec-ship` in any repo.

Provides battle-tested PyTorch training recipes for LLMs, vision, diffusion, and biomedical domains, covering training loops, optimizer selection, LR scheduling, mixed precision, and debugging.

This skill should be used when the user asks to "offload context to files", "implement dynamic context discovery", "use filesystem for agent memory", "reduce context window bloat", or mentions file-based context management, tool output persistence, agent scratch pads, or just-in-time context loading.

Creates an outcome-backed product strategy memo from an Ideal Customer Profile markdown file, pasted ICP notes, discovery artifacts, product context, or PRD context. Use when asked to create product strategy, convert an UX Discovery and ICP into strategy, define vision-to-outcome strategy, turn discovery into strategic bets, clarify target customer outcomes, evaluate roadmap strategy, or avoid feature-factory planning.

This skill should be used when the user asks to "swap tokens", "trade ETH for USDC", "exchange tokens on Uniswap", "buy tokens", "sell tokens", "convert ETH to stablecoins", "find memecoins", "discover tokens", "research tokens", "tokens to buy", "find tokens to swap", "what should I buy", or mentions swapping, trading, researching, discovering, buying, or exchanging tokens on any Uniswap-supported chain. Supports both known token swaps and token discovery workflows (discovery uses keyword search and web search — there is no live "trending" feed). Generates deep links to execute swaps in the Uniswap interface.