「tune」の検索結果

Guide viriditas (greening power) meditation and philosophy practice. Covers the concept of viriditas as divine life force, structured meditation protocol, seasonal attunement, and integration with Hildegardian health and spiritual practice. Use when practicing Hildegardian contemplative spirituality, feeling disconnected from seasonal rhythms, experiencing spiritual dryness or lack of vitality, seeking a meditation that integrates body and earth, or deepening understanding of Hildegard's ecological theology.

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Select a `delaySeconds` value when scheduling a loop wakeup via the `ScheduleWakeup` tool or the `/loop` slash command. Covers the three-tier cache-aware decision (cache-warm under 5 minutes, cache-miss 5 minutes to 1 hour, idle default 20 to 30 minutes), the 300-second anti-pattern, the [60, 3600] runtime clamp, the minute-boundary rounding quirk, and writing a telemetry-worthy `reason` field. Use when designing an autonomous loop, when a heartbeat cadence is being planned, when polling cadence is being tuned, or when post-hoc review of loop costs reveals interval mis-sizing.

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Consistent agent behavior after restart — progressive identity loading, working context reconstruction from persistent artifacts, fresh-vs-continuation detection, calibration through centering and attunement, and identity verification for coherence. Addresses the cold-start problem where an agent must reconstruct who it is and what it was doing from evidence rather than memory. Use at the start of every new session, after a session interruption or crash, when agent behavior feels inconsistent with prior sessions, or when persistent memory and current context appear contradictory.

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Use when 量化调优闭环中需要生成或修改一轮调优所需的 Practice YAML，包括敏感层分析、策略决策、写出 YAML 文件和校验。

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命理圆桌会——多流派命理师（盲派、子平格局派、调候派、民国新派）对同一八字命盘进行圆桌论命：多视角总评、交叉质疑、可证伪断语、逐轮记分复盘。用户提到八字、四柱、命盘分析、批命、论流年大运、盲派/格局/调候论命、"多个命理师讨论"、验证式论命时使用本 skill；即使用户只是贴出一张八字排盘求分析，也应启用（可用轻量模式）。Multi-school Chinese BaZi (Four Pillars) roundtable reading: several fortune-tellers from rival schools analyze the same natal chart, cross-examine each other, issue falsifiable predictions, and keep score. Use whenever the user asks about BaZi, Four Pillars, Chinese astrology chart reading, or destiny analysis.

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Optimizes Atlassian Forge apps to reduce platform consumption and avoid unnecessary costs using Atlassian's "Optimise Forge platform costs" guidance. Use when the user asks to optimize Forge app costs, reduce Forge invocations, lower GB-seconds, reduce storage or log usage, tune memory, replace polling, improve scheduled triggers, reduce KVS writes, move work to the frontend, use bridge APIs, batch API calls, add caching, or evaluate Forge Remote trade-offs. By default, perform an audit first and offer to make the recommended changes after presenting the audit. Only modify files immediately when the user explicitly asks the agent to implement or apply optimizations.

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Offline time-stretch / pitch / varispeed — character modes, fine-tune presets, A/B toolkit, and the honest quality state, so an agent can pick a mode, dial it in, and ship a plugin with it.

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Diagnose whether an LLM classifier's validation-gate failure is GOLD-BOUND before spending on prompt revision or model changes. Use when: (1) a scoring pipeline over-predicts a label (precision low, recall high) and a prompt clarification is proposed to tighten it, (2) a pilot/validation gate fails and the fix candidates are prompt edits, (3) inter-rater agreement on the weak label was already low (κ < ~0.6). Core check: if gold POSITIVES share the exact feature the revision would exclude, no prompt can pass a gold-scored gate — recall craters while precision barely moves. Also documents the verified surgical-pilot design (single-section diff, tune/holdout split, pre-registered gate, perturbation check on untouched sections).

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Use when a task needs the judgment of a Control and Valve Installer/Repairer — sizing a control valve's Cv against actual process flow, diagnosing a limit-cycling control loop as valve stiction versus a bad PID tune, running an FCI 70-2 seat-leakage or API 598 pressure test, calibrating a positioner/actuator after a rebuild, or scheduling PHMSA-mandated regulator/relief-valve testing on a gas distribution system.

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Fine-tune large language models — data curation, SFT, parameter-efficient methods (LoRA), evaluation, and avoiding common training failures. Use when a base model needs task-specific behavior or domain knowledge.

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Program language models instead of prompting them — DSPy signatures, modules, and optimizers that tune prompts automatically.

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Read and manage the user's Threads account: profile, posts, feed, saved posts, activity, insights, social graph, search, trends, and a specific post by URL or ID. Can tune feed ranking and publish posts on request.

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Audit ML experiment tracking infrastructure for reproducibility gaps, parameter logging completeness, metric capture, artifact management, and pipeline orchestration. Covers MLflow, Weights and Biases, DVC, Sacred, Neptune, Hydra configs, model registries, and produces a reproducibility scorecard (0-30) with actionable fixes for data science teams.

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Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

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Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-management`).

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Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval-flywheel` skill).

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Use this skill to create, configure, or tune Field Service Lightning scheduling policies — including work rules (pass/fail filters) and service objectives (weighted ranking criteria). Covers the four default policies, custom policy design, work rule type selection, and objective weighting strategy. NOT for running or tuning the Optimizer itself — use admin/fsl-scheduling-optimization-design. NOT for resource skills and preferences — use admin/fsl-resource-management. NOT for resource capacity records — use admin/fsl-capacity-planning.

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Set up or tune Experience Cloud content moderation: keyword/criteria rules, member reputation system, moderation queue. Triggers: 'set up community content moderation', 'configure reputation levels', 'flagging rules Experience Cloud', 'moderation queue setup', 'keyword blocking community', 'approve community posts before publishing'. NOT for publishing or scheduling CMS articles on the site — use admin/experience-cloud-cms-content. NOT for deciding which behaviours to reward or how to name reputation tiers — use admin/community-engagement-strategy.

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Tune moflo's memory stack for speed, RAM, and index quality. Covers HNSW parameters (M, efConstruction, ef), vector quantization, batch operations, and common bottlenecks. Use when scaling past ~100k entries or when search latency regresses.

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Author and own one flow's design document (the FLOW.md objective flow) at .unikit/gamedesign/flows/FLOW-<slug>.md — the dynamics layer of the GDD (what the player does over time). Create the skeleton and walk the collaborative section-cycle to write its objectives, pacing, dependencies, and funnel events ("design the first-session flow", "map the onboarding sequence", "write the FLOW for the boss encounter"), AND revise it after approval under the delta discipline ("retune the pacing", "add a branch", "change a GOAL", "rework the onboarding") — a cue/number change is Tuning, a small step change a Tweak, restructuring a Rework; each bumps the version and logs the delta. Selects the wiring mode (linear | conditional | emergent) and re-renders the ## Flow Map [gen] / ## Funnel [gen] blocks in GAME.md. To add or detail a system use /unikit-gd-system; to edit GAME.md content use /unikit-gd-spec; to apply a multi-zone edit use /unikit-gd-apply; for a new game concept use /unikit-gd-brainstorm.

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Author and own ONE system's design document (the A–K SYSTEM GDD) at .unikit/gamedesign/systems/SYS-<slug>.md — the depth layer of the GDD, single-system scope. Create the skeleton and walk the collaborative section-cycle to write its parameters, rules, formulas, and acceptance criteria ("detail the combat system", "write the GDD for the inventory system", "fill in the rest of this system doc", "spec out the parameters of X"), AND revise it after approval under the delta discipline ("raise the damage by 10%", "tune the economy", "rework the status system", "nerf X", "change this rule") — a number change is Tuning, a small rule a Tweak, restructuring a Rework; each bumps the version and logs the delta. To add a NEW system or restructure the system map use /unikit-gd-spec; to edit GAME.md content use /unikit-gd-spec; the moment an edit also touches a second system, a content type, a flow, or GAME.md (two or more zones) use /unikit-gd-apply; to invent a brand-new game concept use /unikit-gd-brainstorm.

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Use when tuning the numbers in a game — economy curves, progression rates, difficulty pacing, drop tables, win/loss odds, time-to-X targets, and currency velocities. Triggers on "balance pass", "tune the economy", "progression curve", "difficulty curve", "drop rate", "XP curve", "TTK", "time-to-content", "balance the economy", "spreadsheet model", "economy sim", or a system spec from game-systems-designer with `TBD` placeholders. Produces an economy spreadsheet, balance pass plan, sim outputs, and per-system balance tables. Stops at the numbers — does not change system rules or pricing. For systems design see game-systems-designer; for store catalog see iap-manager.

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Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

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Structure persuasive speeches and formal arguments in forensic, legal, policy, and competitive debate styles. Use when the user needs to prepare talks, opening or closing arguments, policy pitches, public explanations, debate speeches, or mentions 'forensic speech', 'formal argument', 'persuasive speech structure', 'debate speech', 'opening statement', 'closing argument'. Turns structured claims into audience-tuned persuasive delivery.

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