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Diagnosis of why structural materials fail — ductile overload, brittle cleavage, fatigue, creep, stress-corrosion cracking, hydrogen embrittlement, and corrosion. Covers Griffith fracture mechanics, the ductile-to-brittle transition, the S-N and Paris fatigue frameworks, fractographic interpretation, and the J.E. Gordon view that strength, stiffness, and toughness are distinct properties that must be understood together to prevent collapse.

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Distributed storage systems design and operation for cloud platforms. Covers the GFS/HDFS block-and-master pattern, object storage (Swift/S3) with consistent hashing and eventual consistency, block storage semantics, replication vs erasure coding, the CAP theorem in practice, read-repair and anti-entropy, snapshot chains, and the GFS/BigTable/Spanner evolution. Use when designing a storage subsystem, choosing between object/block/file, or reviewing a replication and consistency strategy.

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Evaluating the quality, provenance, and relevance of evidence that supports or undermines a claim. Covers source credibility, sampling quality, study design, levels of evidence (anecdote to meta-analysis), base rate integration, distinguishing primary from secondary sources, and calibrating belief to evidence strength. Use when the question is not whether an argument is valid but whether its premises are actually supported by the available data.

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用户在周期性复盘 X 内容数据、分析哪些内容类型表现好、决定下步内容策略调整时使用。四步闭环:数据收集(拉曝光/互动/收藏)→原因分析(按内容类型对比,查Hook/Body/CTA)→优化迭代("如果重来怎么做更好")→记录追踪(Notion模板闭环)。找20%贡献80%价值的内容加倍投入。周复盘聚焦数据(互动率),月复盘审视战略(变现路径)。Trigger signals:"这周发了15条怎么复盘""哪些类型互动高""下周内容策略怎么调""帮我分析数据"/"weekly content review""which content type performs better""optimize next week's content"。不适用于:单条内容生产(用x-short-content-craft)、帖子没爆焦虑心态(用x-longtail-strategy)、理解推荐算法机制(用x-foryou-algorithm)。

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用户想理解X推荐机制/为什么帖子被推荐或不被推荐/小号能不能被推荐时使用。触发词: For You算法, 推荐机制, OON, 为什么没流量, 小号能起吗, algorithm, recommendation, why no impressions。不用于: 纯写内容不问机制/心态焦虑"帖子没爆怎么办"(那是longtail-strategy)/怎么互动获曝光(那是comment-engagement)。

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用户在帖子没爆时感到焦虑、怀疑自己是否适合做 X,或连发一批内容后评估整体表现、设定增长目标时使用。核心认知:公共平台传播是长尾分布(3861 帖均值 188/中位数 42),一条没爆是预期的中位数而非失败;目标不是让每条都爆,而是系统性提高进前 10% 的概率。Trigger signals: "我连发20条只2条过百该放弃吗""怎么让每条都爆""发了这么多没水花""一条没爆就怀疑人生""是不是我不适合做X" / "only 2 out of 20 hit 100, should I quit" "how to make every post go viral" "my posts get no engagement"。不适用于:单条内容生产(用 x-short-content-craft)、数据复盘操作(用 x-data-review)、理解推荐算法机制(用 x-foryou-algorithm)。

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Maximum control over AI image generation — write structured VGL (Visual Generation Language) JSON that explicitly controls every visual attribute. Define exact object placement, lighting direction, camera angle, lens focal length, composition, color scheme, and artistic style as deterministic JSON instead of ambiguous natural language. Use this skill when you need reproducible image generation, precise control over scene composition, or want to convert a natural language image request into a structured JSON schema for Bria FIBO models. Triggers on requests for structured prompts, controllable generation, VGL JSON, deterministic image descriptions, or Bria/FIBO structured_prompt format.

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Build an interactive account org chart, leadership map, buying committee, or manager/report neighborhood around a person. Use for decision makers, reporting-line candidates, stakeholder mapping, and warm intro paths. Skip standalone email lookup and unrelated cross-company prospecting.

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When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," or "tracking plan." For A/B test measurement, see ab-test-setup.

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Review existing tests against the software and contracts they actually exercise. Use to assess whether a test has justified unique protection, detects consequential faults, tolerates valid refactors, earns its lifecycle cost, or deserves strengthening, replacement, consolidation, or removal consideration. Reviews are read-only and evidence-backed, including TDD and regression tests.

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Use when finding real role-holders at known company domains from an ICP, especially prompts like 'find all job titles at these companies', 'find qualified titles', 'find RevOps or marketing-ops buyers', or when exact title discovery should precede paid people search.

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Deprecated alias. This skill was renamed to `deepline-scoring`. Use when a user or script still invokes `/niche-signal-discovery`: immediately load and follow the `deepline-scoring` skill instead. Triggers: ICP analysis, niche signals, won vs lost analysis, differential signals, signal discovery, ICP signal report, account scoring signals, lead scoring, first-party signals, buyer signals.

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Build a Total Addressable Market list: size the audience, source companies from ICP filters, then find contacts at those companies at scale.

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DRAFT. Design and harden a standing Luma registration/unsubscribe webhook that enriches guests and writes to CRM or customer DB — with backfill and versioning. Not installed by deepline skills.

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Resolve LinkedIn profile URLs from name + company with strict identity validation to avoid false positives.

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Use this skill when a human needs to review a Deepline Play result and hand feedback, labels, or approval back to the agent for revision, evaluation, comparison, or bounded iteration. Triggers on “put this run in a Sheet,” “review these results,” “read my feedback,” “make this a standing rule,” “never regress on this case,” “compare these runs,” “keep improving,” or “add the revised run to the same spreadsheet.” Skip ordinary Play authoring or execution with no human review loop, generic spreadsheet work, and one-off CSV export.

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Deprecated alias. This skill was renamed to `deepline-research`. Use when a user or script still invokes `/deepline-pre-research`: immediately load and follow the `deepline-research` skill instead. Triggers: pre-research, source discovery, provider strategy, research data sources, ScrapeCreators, X/Twitter data, Reddit comments, public and private datasets, CRM data, workflow data, custom language, messaging language, pain language.

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Convert a Clay table configuration into local Deepline scripts. Handles extraction (MCP or script), documentation, action mapping, script generation, and parity validation against Clay ground truth.

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Run a quick Deepline demo with a maintained Play.

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DRAFT. Build anonymizable host-prep one-pagers for must-meet guests at an in-person event: rank, enrich, light research, warm-intro angles, PDF pack. Not installed by deepline skills. No real customer data in repo samples.

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ACCESS-GATED beta. Deepline Monitors are provider event feeds (job posts, email replies, funding, intent) that stream into your warehouse and trigger plays. Only use if you have monitor access: run `deepline monitors status` first; if it reports no access, do NOT use this recipe — tell the user to contact the Deepline team.

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Send feedback or bug reports to the Deepline team. Use when the user asks to report feedback or share a session, and proactively when a Deepline Play is disproportionately slow, appears stuck, or an ordinary outcome requires avoidable product steps.

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Use when the user wants a last30days-style pre-research pass in Deepline: discover the critical public, private, CRM, workflow, social, and web data sources for a research/enrichment job; compare provider coverage; estimate Deepline credit cost; recommend the source plan before building or running the workflow; or build custom language/messaging from buyer, competitor, community, and CRM evidence. Triggers: pre-research, source discovery, provider strategy, research data sources, ScrapeCreators, X/Twitter data, Reddit comments, public and private datasets, CRM data, workflow data, custom language, messaging language, pain language.

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Build, publish, and verify a Deepline Play as a durable state machine over Customer DB tables, including a small paid pilot. Invoke this skill explicitly when the user asks to build an engine.

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