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Found 113 Skills
Measure and optimize customer service performance using CSAT, NPS, CES, First Contact Resolution, and text mining on support tickets. Use this skill when the user needs to evaluate CS team performance, identify top complaint drivers, optimize staffing, or build CS dashboards — even if they say 'is our CS team doing well', 'what are customers complaining about', 'how many agents do we need', or 'build a CS dashboard'.
GetMoreBacklinks platform help — managed directory submission service for startups. Use when deciding whether to pay for directory submissions vs doing it yourself, comparing GetMoreBacklinks plans (Starter $87 vs Business $187), setting expectations for DR improvement timeline, evaluating if a managed submission service is worth it for your budget, or troubleshooting why directory submissions didn't improve rankings. Do NOT use for DIY directory submission strategy (use /sales-launch-directory). Do NOT use for backlink analysis or SEO audits (use /sales-semrush).
Test quality review drawing on twelve classic engineering books — with primary focus on xUnit Test Patterns, The Art of Unit Testing, How Google Tests Software, and Working Effectively with Legacy Code — that diagnoses structural problems in an existing test suite: brittleness, mock abuse, coverage illusions, slow execution, poor readability. Triggers when: user asks about test quality, shares test files for review, or expresses frustration: "tests keep breaking whenever I change anything", "our tests take forever", "I can't understand what this test is doing", "tests pass but bugs still reach production", "we have too many mocks". Do NOT trigger for: writing new tests from scratch (use the regular test-writing workflow) or testing framework/syntax questions — this skill reviews an existing suite for structural quality problems, not individual test authoring.
Explain how claude-mem captures observations, when memory injection kicks in, and where data lives. Use when the user asks "how does claude-mem work?" or "what is this thing doing?".
Searches the live web via Nimble APIs to monitor competitors and produce a structured intelligence briefing. Runs parallel searches for news, product launches, hiring signals, and funding — then compares against previous findings to highlight only what's new. Use this skill when the user asks about competitors, competitive intelligence, or what rival companies are doing. Common triggers: "what are my competitors doing", "competitor update", "competitor news", "competitive landscape", "market intel", "what's new with [company]", "track [company]", "competitor briefing", "who's making moves", "competitive analysis", "losing deals to [company]", "battlecard". Also use before board meetings or strategy sessions when the user wants competitive context. Requires the Nimble CLI (nimble search, nimble extract) for live web data. Do NOT use for single-company deep dives (use company-deep-dive), meeting prep with attendees (use meeting-prep), or non-business queries.
Check the consistency and authenticity risks of citations and references in NSFC proposal text (read-only): Verify the existence of bibkey, format issues such as BibTeX fields and DOI, and generate structured input for the host AI to evaluate item-by-item whether the text expression actually cites the literature; by default, only an audit report is output, and the proposal or .bib file is not directly modified (unless the user explicitly requests it).
Read-only crypto wallet insights via the Zerion CLI: portfolio value, token holdings, DeFi positions, transaction history, PnL, and watchlist management. Use whenever the user asks 'what's in this wallet', 'how is X doing', portfolio/PnL/positions/transactions for any address, ENS name, local wallet, or watched address. Supports x402 / MPP pay-per-call. Pair with `zerion-trading` for execution after analysis.
SaaS financial health advisor. Use when a user shares revenue or customer numbers, or mentions ARR, MRR, churn, LTV, CAC, NRR, or asks how their SaaS business is doing.
Expert guidance for working with Dagster and the dg CLI. ALWAYS use before doing any task that requires knowledge specific to Dagster, or that references assets, materialization, or data pipelines. Common tasks may include creating a new project, adding new definitions, understanding the current project structure, answering general questions about the codebase (finding asset, schedule, sensor, component or job definitions), debugging issues, or providing deep information about a specific Dagster concept.
Self-report agent issues by logging user corrections for later review, then resume with the correct skill. Use when a user says "don’t do that", "stop doing X", "always do Y", or requests self-correction.
Multi-route literature expansion + metadata normalization for evidence-first surveys. Produces a large candidate pool (`papers/papers_raw.jsonl`, target ≥1200) with stable IDs and provenance, ready for dedupe/rank + citation generation. **Trigger**: evidence collector, literature engineer, 文献扩充, 多路召回, snowballing, cited by, references, 元信息增强, provenance. **Use when**: 需要把候选文献扩充到 ≥1200 篇并补齐可追溯 meta（survey pipeline 的 Stage C1，写作前置 evidence）。 **Skip if**: 已经有高质量 `papers/papers_raw.jsonl`（≥1200 且每条都有稳定标识+来源记录）。 **Network**: 可离线（靠 imports）；雪崩/在线检索需要网络。 **Guardrail**: 不允许编造论文；每条记录必须带稳定标识（arXiv id / DOI / 可信 URL）和 provenance；不写 output/ prose。
Use vision models to self-review screenshots against design intent. Catches spacing issues, alignment problems, color inconsistencies, responsive bugs, and accessibility gaps. Use when reviewing designs, comparing implementations to mockups, or doing pre-ship QA.