「ost」の検索結果

Requires OFOX_API_KEY — create one at https://app.ofox.ai. Generate cheap vertical 9:16 drafts in one priced batch, pick a winner off the contact sheet, then re-render only that one. The cheap tier is the point — several times cheaper per second, so a whole set can cost less than one flagship clip. Use when a user wants Shorts/Reels/TikTok raw material rather than one finished video, e.g. "give me 5 vertical clips to choose from", "a few Reels drafts for this product", "some cheap options before we commit", or "batch me some 9:16 takes". Do not use when one finished clip is wanted — go straight to the scenario skill (seedance-ad-creative, ugc-ads, seedance-short-drama, seedance-product-video), which is also where this skill gets the prompt it drafts.

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Requires OFOX_API_KEY — create one at https://app.ofox.ai. Shared execution layer for the Ofox video generation API (api.ofox.ai) — creates a video job, polls it to completion, downloads the finished mp4 from a persistent CDN URL, and reports the real cost. This is a library skill, not a standalone user-facing one — it is invoked by scenario skills such as seedance-short-drama, seedance-ad-creative, and seedance-product-video, which build model/prompt/resolution choices for a specific use case and then call into this skill's script rather than re-implementing the API calls. Load this skill directly only when a user explicitly names the Ofox video API, asks to call it with specific low-level parameters, or asks to debug/resume a stuck or failed Ofox video job by job id — for a plain scenario request ("make me a short drama scene", "generate a cinematic ad clip"), use the relevant scenario skill instead, which itself depends on this one.

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。

Requires OFOX_API_KEY — create one at https://app.ofox.ai. Shared execution layer for the Ofox image API (api.ofox.ai) — validates parameters client-side, sends one synchronous request, base64-decodes the result, saves it to a file, and reports the real usage token counts and the computed dollar cost. Does both text-to-image (generate) and editing an existing image you supply as a local file (edit — change the background, recolour an element, alter a product photo, while leaving the rest of the picture intact). This is a library skill, not a standalone user-facing one — it is meant to be invoked by scenario skills that build model/prompt/size choices for a specific use case and then call into this skill's script rather than re-implementing the API calls — image-edit owns "change this image so that...", product-image owns a set of product images to choose between, and seedance-anime-drama owns character images for a video pipeline. Load this skill directly only when a user explicitly names the Ofox image API, asks to call it with specific low-level parameters, asks to debug a failed Ofox image request, or wants a plain "generate an image of..." from text — which is the one common case no scenario skill covers.

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。

Outreach triggered by funding events with post-funding needs analysis and personalized drafts

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。

Analyze closed deals and lost opportunities for patterns

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。

Generates platform-native social media posts for LinkedIn, X/Twitter, Instagram, and Reddit. Supports thought leadership, case study snippets, hot takes, engagement questions, carousel outlines, and threads.

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Extract warm prospects from competitor and industry post engagers across LinkedIn, Twitter, and blogs

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Generates contextual replies to comments, mentions, and DMs on social posts. Classifies engagement as lead signal, customer question, partnership opportunity, or troll/spam. Uses brand voice from agency.config.json.

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An innate exploratory drive that guarantees no graph node remains permanently invisible to an agent population. After a warm-up period (step > 3), each agent fires a coverage scan with probability |unseen_nodes| / |all_nodes|. When triggered, the agent teleports directly to the globally unseen node with the highest belief uncertainty, works it, and returns control to the normal action-selection loop. Probability is self-annealing: it starts near 1.0 when almost everything is unseen and decays toward 0.0 as coverage saturates, requiring no manual schedule. The mechanism is "innate" (hard-coded into the agent step loop) as distinct from the "adaptive" EFE path, which requires local neighbors and learned precision weights. Together they form a two-layer exploration architecture: adaptive for locally-guided foraging, innate for topological completeness.

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Manage DAG execution lifecycles including start, stop, pause, resume, and cleanup. Activate on 'execution lifecycle', 'stop execution', 'abort DAG', 'graceful shutdown', 'kill process'. NOT for cost estimation, DAG building, or skill selection.

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Use when running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).

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Classify every destructive/gated action an agent body can attempt (git, filesystem, network, shell, github) into block/approve/allow tiers, and audit whether the pre-tool and post-tool governance backing that classification is real: blocked actions proven zero-side-effect by a negative fixture, every denial backed by a receipt and a transcript event, every block-tier denial paired with a safe alternative, and no unmanaged or same-UID body ever marked "contained." Use when building or reviewing Agent Harbor's C5 governance gate (destructive git blocker, approval request/result, denial receipts), gating a new tool surface before it can run destructive commands, or auditing whether an existing "destructive-action blocked" claim actually has evidence behind it. NOT for proving an existing sandbox boundary actually contains an adversary once an action is isolated (use sandboxed-adversarial-test-harness), deciding where in a DAG to place a human review node or designing the approval UX itself (use human-gate-designer), or designing the general work-receipt schema across an entire agent task (use agent-work-receipt-designer).

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PgBouncer, connection optimization, and pooling strategies for database performance. Activate on: connection pool, PgBouncer, database connections, pool size, connection limit, Prisma pool, Drizzle pool. NOT for: query optimization (use data-warehouse-optimizer), database schema design (use dimensional-modeler).

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Designs and audits data retention and file compaction for an embedded SQLite datastore in a long-lived service — a unified retention registry (one declared policy per table), TTL / absolute-age / row-count-cap policies, a fail-loud coverage guard, WAL/checkpoint tuning, and auto_vacuum/incremental-vacuum so pruning actually shrinks the file. Use when a SQLite DB grows unbounded, a table has an index but no DELETE, a pruned DB never shrinks on disk, metrics are stored as raw events forever, or a new table needs bounding. Keywords retention policy, TTL, reaper, prune, VACUUM, auto_vacuum, incremental_vacuum, WAL checkpoint, freelist, DB bloat, row cap, per-tenant quota. NOT for Postgres/MySQL/server-DB partitioning or TTL, log-line rate-limiting/sampling, backup/replication, schema migrations, or query-performance indexing.

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Audit or design server-side indexes, directories, reputations, or aggregates that are DERIVED from traffic a platform witnesses (chain heads, run verdicts, heartbeats, publish activity) so that consent gates the derivation, not merely the read: no shadow index over non-consenting principals, post-consent-only evidence windows, retention bounds and delete-propagation into derived rows, activity-oracle threat modeling, and audit-logged ranking discretion. Use when building a capability directory / whois from witnessed activity, a reputation ledger from settled outcomes, vitals aggregation across tenants, or any "the yellow pages can't lie because they index signatures" feature. Keywords: shadow index, derived index, consent gates derivation, activity oracle, phone book not a log, delist propagation, ranking weight audit, k-anonymous aggregate, subpoena surface. NOT for the consent-screen UX and scope-ladder mechanics themselves (use local-first-tenancy-boundary), reputation scoring math and Sybil economics (use agent-identity-continuity-reputation), or discovery-layer architecture L1/L2/L3 (use agent-discovery-directories-guilds).

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Snowflake, BigQuery, clustering, partitioning, and materialized views for warehouse performance. Activate on: Snowflake, BigQuery, Redshift, query optimization, clustering, partitioning, materialized view, warehouse cost, query profile. NOT for: dbt model structure (use dbt-analytics-engineer), data modeling (use dimensional-modeler).

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Cloudflare Pages and Workers deployment checklist for wrangler deploys, secret audits, D1 migrations, Worker-compatible SDK checks, and browser-auth verification. Use when shipping Cloudflare changes to production. NOT for general CI/CD architecture, non-Cloudflare deployments, or post-launch UX QA.

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Use when applying Cloudflare D1 migrations, fighting Supabase migration history vs SQL execution, choosing direct psql over CLI, designing idempotent migrations, debugging schema drift between local and remote, or recovering after a half-applied migration. Triggers: supabase migration repair instructions appearing, table missing after "applied" status, "Tenant or user not found" when running psql, --remote vs --local D1 confusion, NOT NULL on a populated column, foreign key constraint failures, drift between staging and prod schemas. NOT for Mongo/document migrations, ORM-managed migrations specifically (Prisma/Drizzle have their own conventions), or pure data backfills.

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Decides HOW to mutate a DAG when a node fails, quality is below threshold, or new information changes the plan. Selects from mutation strategies (add node, replace agent, fork paths, loop back, downgrade model) based on failure type, cost budget, and execution history. Activate on "DAG failed how to fix", "mutation strategy", "replan on failure", "adaptive DAG", "recovery strategy", "what to do when node fails". NOT for detecting failures (use dag-quality), executing mutations (use dag-planner), or runtime execution (use dag-runtime).

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Treat tokens as a coding swarm's cost-of-goods-sold and legibility engine while accounting for finite subscription allowance when per-call price is unknown. Covers provider windows, remaining-usage evidence, burn forecasts, preemptive checkpoint/model switching, per-agent budgets, compaction, shared digests, context degradation, and spend metering. Activate on: "token budget", "context budget", "compaction strategy", "briefing as compression", "context rot", "summarization collapse", "COGS for agents", "subscription usage remaining", "five-hour or weekly limit", "burn forecast", "model switch before limit", or "/context-economics-for-agent-swarms". NOT for: memory architecture (use always-on-agent-architecture), single-prompt wording (use prompt-engineer), mechanism-design proofs (use nisan-et-al-2007-algorithmic-game-theory), physical containment (use sandboxed-adversarial-test-harness), or production rebodiment and fencing (use agent-resurrection-and-body-continuity).

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Use when wrangler deploys silently fail or produce wrong artifacts, secrets upload as empty strings, custom domain DNS is not resolving, route assignment broke after rename, observability/tail logs are needed, D1/KV/R2 bindings are missing, OAuth scope errors block a command, cookies on a redirect are not attaching, or the worker exceeds CPU time. Triggers: "Tenant or user not found", "Authentication error" on wrangler d1 push, secret length is 0 after upload, route returns origin not worker, "compatibility_date too old", 1101 errors, custom_domain assignment lost on script rename. NOT for AWS Lambda / Vercel Edge / Deno Deploy (different runtimes), Cloudflare Zero Trust, or R2-specific tuning.

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Guides Cloudflare One Zero Trust and SASE work across Access, Gateway, WARP, Tunnel, Cloudflare WAN, DLP, CASB, device posture, and identity. Use when designing, configuring, troubleshooting, or reviewing Cloudflare One deployments. Retrieval-first: use current Cloudflare docs/API schemas instead of embedded product docs.

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Conduct Critical Decision Method (CDM) interviews to elicit expert reasoning strategies (L3 knowledge) from practitioners about specific challenging past incidents. Use when capturing expertise for skill development, preserving knowledge before practitioner departure, building post-incident learning artifacts, or investigating decisions in complex sociotechnical systems. Outputs structured incident reconstructions with cues, decisions, alternatives considered, and counterfactual analysis suitable for distillation into skills or training material.

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Architecture-level decisions for putting a CDN in front of a web app's static assets, API responses, and file/media delivery: provider selection (hyperscaler vs. specialist $/GB), signed-URL / tokenized access for gating private content at the edge, cache invalidation strategy (versioned URLs vs. purge APIs), origin shield / tiered caching for high-miss-rate content, and CDN-layer geo-restriction. Use when deciding which CDN to put in front of a new or growing service, when private/paywalled files need edge delivery without an origin round-trip per request, or when purge/invalidation behavior is being designed rather than debugged. NOT for HTTP cache-control header syntax, Vary, stale-while-revalidate, or surrogate-key purge mechanics (see `cdn-cache-control-headers`); NOT for Cloudflare-specific product configuration once Cloudflare is already chosen (see `cloudflare`); NOT for video-specific transcoding, adaptive bitrate ladders, or streaming-protocol packaging (see `managed-video-streaming-pipeline`).

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。