“pruning”的搜索结果

Remove redundant or unsupported page content.

中文简介正在准备中。当前显示原文说明。

Evaluate any agent skill against a merged framework — Anthropic's Claude Code best practices plus Matt Pocock's writing-great-skills methodology — across 4 axes (Trigger, Structure, Steering, Pruning). Produces an evidence-cited scorecard (0–100), a weighted overall score, and diagnosed failure modes with prioritized fixes. Use when the user asks to evaluate, rate, or audit a skill ("evaluate this skill", "skill scorecard", "review SKILL.md"), or to compare two skills.

中文简介正在准备中。当前显示原文说明。

Adapts LLMs: SFT dataset loss masking, LoRA and QLoRA, full FT vs PEFT, distillation, pruning, tokenizer fragmentation. Use when fine-tuning or compressing a model.

中文简介正在准备中。当前显示原文说明。

Owns instruction files: AGENTS.md, CLAUDE.md, personal and repo rules. Use when writing, pruning, auditing them, sharing rules across Claude and Codex, or fixing ignored rules.

中文简介正在准备中。当前显示原文说明。

Safely close out completed Incan work after a PR is merged by verifying merge status, syncing the base branch, removing task-owned local worktrees/assets, deleting merged local/remote branches, pruning refs, and reporting dirty or ambiguous leftovers. Use when the user says /closeout, asks to clean up after a merged PR, or wants local RFC/issue branch/worktree cleanup.

中文简介正在准备中。当前显示原文说明。

Optimize AgentDB with quantization, HNSW tuning, caching, batch ops, and pruning.

中文简介正在准备中。当前显示原文说明。

修改/重构/Bug 修复后避免老问题复发时使用。适用于版本回归、修复后回归、重构验证。融合 Impact Analysis、Test Suite Pruning、Smoke/Sanity/Full 三层套件。

中文简介正在准备中。当前显示原文说明。

Audit, classify, and selectively forget stored memories. Covers memory enumeration and classification by type/age/access frequency, staleness detection for outdated references, fidelity checks using external anchors, a decision tree for selective withdrawal, tombstoned deaccession so removed content stays recoverable, counter-memory inoculation for failed strategies that would otherwise be re-derived, preemptive filtering rules for what should never become memories, and an audit trail so forgetting itself is reviewable. Use when memory has grown large and uncurated, when project state has shifted significantly since memories were written, when retrieval quality has degraded, or as periodic maintenance alongside manage-memory.

中文简介正在准备中。当前显示原文说明。

Create, track, switch, sync, and clean up Git branches. Covers naming conventions, safe branch switching with stash, upstream synchronization, and pruning merged branches. Use when starting work on a new feature or bug fix, switching between tasks on different branches, keeping a feature branch up to date with main, or cleaning up branches after merging pull requests.

中文简介正在准备中。当前显示原文说明。

Monitor, cap, and recover from context accumulation in agentic systems. Covers per-cycle cost tracking, context window auditing, budget caps with enforcement policies, emergency pruning when approaching limits, and progressive disclosure integration to minimize token spend on routing. Use when running long-lived agent loops (heartbeats, polling, autonomous workflows), when context windows are growing unpredictably between cycles, when API costs spike beyond expected baselines, when designing new agentic workflows that need cost guardrails from the start, or when post-mortem analysis reveals a cost incident caused by context accumulation.

中文简介正在准备中。当前显示原文说明。

Maintain the 8 essential garden hand tools through sharpening, handle care, rust prevention, and seasonal storage. Covers bypass secateurs, hori-hori, hand fork, trowel, pruning saw, sharpening stone, watering can, and soil rake. Use after each garden session for quick cleaning, monthly during the growing season for sharpening and oiling, at end of season for winter storage preparation, before spring for pre-season readiness checks, or whenever a tool feels dull, stiff, or shows rust.

中文简介正在准备中。当前显示原文说明。

Periodic housekeeping and session lifecycle management. Use when performing cleanup, pruning, or session commands.

中文简介正在准备中。当前显示原文说明。

Use when optimizing token usage, KV cache efficiency, or context window management for LLM agents. Keywords: context optimization, KV cache, prompt caching, token budget, semantic pruning, lost-in-the-middle.

中文简介正在准备中。当前显示原文说明。

Use when preparing a desktop or web app update for commit, packaging, installer validation, GitHub publication, release asset upload, or old-release pruning. Protects user data and verifies that published artifacts match the tested build.

中文简介正在准备中。当前显示原文说明。

Build a bookmark system you'll actually use: capture, tagging, retrieval, and regular pruning. Use when bookmarks pile up unfindable or you keep losing useful links.

中文简介正在准备中。当前显示原文说明。

Reorganize the user's X and LinkedIn network with review-first pruning, add/follow recommendations, and channel-specific warm outreach drafted in the user's real voice. Use when the user wants to clean up following lists, grow toward current priorities, or rebalance a social graph around higher-signal relationships.

中文简介正在准备中。当前显示原文说明。

Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.

中文简介正在准备中。当前显示原文说明。

Decides how far ahead to plan, what to discount future payoffs by, and which backlog items can be dropped without analysis - using receding-horizon planning, an explicit discount factor, and branch-and-bound pruning against the incumbent. Use when building a roadmap, prioritising a backlog, arguing about short-term versus long-term, setting quarterly or annual plans, or when planning has become an end in itself.

中文简介正在准备中。当前显示原文说明。

Use when adding, auditing, pruning, archiving, restoring, or reviewing Agent Notes in deepseek-harness; checks every new note for superseded active records, classifies implemented notes by future decision value, deletes rejected notes that no longer prevent a tempting fallacy, and applies the frozen archived/{kind} triplet and manifest rules.

中文简介正在准备中。当前显示原文说明。

Use when Kubernetes Server-Side Apply reports field-manager conflicts, controllers overwrite fields, or force-conflicts risks pruning/defaulting side effects; diagnose ownership read-only before choosing relinquish, partition, or explicit transfer.

中文简介正在准备中。当前显示原文说明。

Reduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.

中文简介正在准备中。当前显示原文说明。

Integrate on-device AI using Foundation Models framework, Core ML, and open-source LLM runtimes on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM). Use when building tool-calling AI features, working with guided generation schemas, converting models, or running on-device inference.

中文简介正在准备中。当前显示原文说明。

Token budgeting discipline for all agents. Treats tokens as a resource from session start — not just when the buffer hits 60%. Covers prompt compression, context hygiene, handoff packing, and HOT memory pruning.

中文简介正在准备中。当前显示原文说明。

Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.

中文简介正在准备中。当前显示原文说明。