Results for “rule”

Use to design a safe pipeline that writes derived attributes back into a helpdesk — risk scores, segments, health flags — without corrupting agent workflows or creating an unexplainable feedback loop. Trigger for "push scores back into Zendesk", "sync warehouse attributes to the helpdesk", "write our churn risk onto the ticket", reverse ETL for support, or an automation that started firing on a synced field.

Use to find cancellation, downgrade and switching intent in support conversations, and to be honest about how much of your churn support can actually see. Trigger for "which customers are at risk of leaving", "find cancellation intent", "customers asking how to export their data", "top reasons customers asked to close their account", competitor mentions, or building a churn early-warning signal from support data.

Use to build an account's cumulative support record ahead of a renewal or QBR, so the review reflects the whole relationship rather than the most recent ticket. Trigger for "prepare for this renewal", "what has this account's support experience been", "which accounts are at risk before renewal", QBR preparation, account health review, or an account that renewed badly with no warning.

Deploy and configure Xray proxy servers. Use when a user asks to set up VLESS, VMess, Trojan, or Shadowsocks proxies, configure Reality or TLS transport, deploy Xray with XTLS, set up fallback routing, manage multi-user access, configure traffic routing rules, set up CDN-based tunneling, build subscription links for client apps, monitor Xray traffic, or bypass network restrictions. Covers all major Xray protocols, transports, and deployment patterns.

Use to verify that the chain from conversation to evaluation to decision is complete and reconstructible, so a QA score or a customer outcome can be explained months later. Trigger for "can we prove why this decision was made", "is our audit trail complete", "reconstruct how this score was produced", evidencing an AI-assisted decision, or an auditor asking how a conclusion was reached.

Use to assemble a fair, evidence-backed coaching pack for a support agent's one-to-one from QA evaluations and conversation history. Trigger for "prepare a coaching session for X", "what areas does X need to improve", "areas of markdown for this agent", "what coaching opportunities stand out", "build a coaching agenda from these tickets", or preparing a weekly or monthly agent review.

Use to find where support agents cross from information into regulated advice or a personal recommendation, and to design the boundary so they can still be helpful. Trigger for "are agents giving advice", "where's the line between information and advice", "should agents recommend a product", guidance versus advice boundary, agents answering "what would you do", or a complaint that an agent recommended something.

Use to build a defensible cost per contact and cost to serve by driver, channel or customer segment, for automation business cases and budget decisions. Trigger for "what does a ticket cost us", "cost per contact", "what would automating this save", "cost to serve by segment", building a business case for a bot or a process change, or a savings claim that looks too good.

Use to surface fraud, scam and financial-crime signals that customers describe to support before detection systems see them, and to check whether agents recognised and routed them. Trigger for "are customers reporting scams", "new scam pattern targeting our customers", "did we spot the fraud signals", authorised push payment scams, "customer was coached by someone on the phone", or fraud reports arriving through support.

Use to turn a one-off support search into a standing monitor or alert that stays useful instead of being muted within a fortnight. Trigger for "alert me when a customer mentions X", "notify the team when complaints spike", "keep watching for this", "set up a daily monitor for", turning an ad-hoc sweep into something recurring, or an existing alert nobody reads any more.

Use to write CX commentary that makes a number decision-ready — what moved, why (decomposed), so what, do what — separating noise from signal and stating confidence honestly. Trigger for "write the commentary", "explain this trend", "what should we say about the drop", weekly or monthly narrative, board pack prose, "why did QA fall", metric movement write-up, or when a report has charts but no story.

Semantic-search personal knowledge (memory, plans, handoffs, skills, Codex rules) via the local RAG index at ~/.claude/rag-index/. Use when a query is fuzzy or cross-file ("how did we fix X", "what did we decide about Y", "which skill handles Z"). Complements grep (exact) and Serena (code symbols). If the user asks a recall question that doesn't map to a specific known file, reach here first.

Use to scan support conversations for a harm nobody has categorised yet — a new failure mode, a scam pattern, or a product change hurting a group of customers before it shows in any metric. Trigger for "is anything new going wrong", "scan for emerging issues", "early warning from support", horizon scanning, a new scam pattern, or preparing for a question about what you might be missing.

Use to plan and run peak-season support — what to freeze, pre-stage and measure daily so volume spikes do not become quality and attrition crises. Trigger for "peak season readiness", "holiday staffing plan", "Black Friday prep", freeze policy or KB changes before peak, pre-hire and ramp for surge, daily war room metrics, peak post-mortem, or when last year's peak broke SLA and attrition.

Use to measure whether a support bot, AI agent, or self-service channel actually reduces contact volume, and to audit a vendor's containment or deflection number. Trigger for "what's our real deflection rate", "is the bot working", "our containment rate is 70% but tickets haven't dropped", automation ROI, self-service savings, AI agent resolution rate, or designing a holdout test for a support bot.

Expert-level skill for Jira Query Language (JQL). Use when the user asks about writing, debugging, optimizing, or understanding JQL queries; needs to filter Jira issues by complex criteria, date ranges, history, or cross-project conditions; wants to build saved filters, dashboard gadgets, or automation rules; or needs guidance on JQL performance, functions, operators, history operators (WAS/CHANGED), relative dates, role-based query patterns, or the JQL REST API.

Use to audit a CX dashboard for tiles nobody acts on and metrics nobody agrees on — assign an owner per tile, apply kill criteria, and run an actionability test before adding or keeping a chart. Trigger for "clean up the dashboard", "too many metrics", "which KPIs should we keep", dashboard audit, metrics review workshop, "nobody looks at this anymore", or before a BI migration or exec-pack refresh.

Use to recover a day in real time when volume, handle time or availability diverge from plan — reforecasting decisions, break moves, overtime, callbacks and skill flex without burning tomorrow. Trigger for "intraday management", "real-time reforecast", "we are underwater at 11am", move breaks or lunches, call people back in, flex agents between queues, ride out the spike, or when the WFM plan is wrong before close of business.

Use to compare CX performance to a published or vendor benchmark without fooling yourself — scope mismatch, survivor bias, and definition mismatch usually make external benchmarks incomparable, and internal baselines often beat them. Trigger for "how do we compare to industry", "is our CSAT good", benchmark slide for the board, vendor benchmark report, "are we above average", outsourcing RFP benchmarks, or when someone cites a round-number industry standard.

Sets up and audits the Claude Code harness for a project: CLAUDE.md, .claude/rules, skills, subagents, settings.json permissions, hooks, verification loop. Commands: kickoff (scaffold new setup), check-playbook (score an existing one), scouting-report (last scorecard), adjust (fix bloat/misplaced instructions), drill (turn advisory prose into real hooks/permissions/CI), decline (record an accepted risk), gameplan (work order with acceptance criteria before building), watch-film (check a diff against that order for scope creep/weakened tests/false claims). Use for setting up Claude Code, or on: "Claude ignores my CLAUDE.md", "it's huge and still misses things", "it said done but ran nothing", "it changed files I didn't ask about", "it weakened a test to pass", "rule, skill, or hook?", "is my setup any good". Not for code quality (code-audit), test coverage (test-assessment), one-off prompt wording (genie-proof-prompts), new skill authoring (skill-creator), or compacting a conversation (handoff).

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer

Detect and humanize AI-generated Chinese text. 20+ rule detection categories plus statistical features (sentence-length CV, short-sentence fraction, comma density, perplexity, GLTR, DivEye) plus scene-aware LR fusion (rule × 0.2 + LR × 0.8) trained on three scenes: general / academic / longform 长文本 (≥1500 字)。Unified CLI: ./humanize {detect,rewrite,academic,style,compare}. 8 style transforms (casual/zhihu/xiaohongshu/wechat/academic/literary/weibo/novel)。 Multi-paragraph rewriting (paragraph length CV、跨段 trigram 重复) plus best-of-N humanize (默认 N=10 取最低 LR)。165 replacement patterns + CiLin 同义词词林 38873 with collision blacklist。 Academic paper AIGC reduction for CNKI/VIP/Wanfang (知网/维普/万方 AIGC 检测降重)。 Pure Python, no dependencies, offline。v5.0.0 — HC3 fused 准确率 95%、学术 hero 100→35 (-65)、 工作汇报 96→13 (-83)、长篇博客 96→41 (-55)。 Use when user says: "去AI味", "降AIGC", "人性化文本", "humanize chinese", "AI检测", "AIGC降重", "去除AI痕迹", "文本改写", "论文降重", "知网检测", "维普检测", "AI写作检测", "让文字更自然", "detect AI text", "humanize text", "reduce AIGC sc

Use to analyse a support backlog and find the conversations actually at risk — never-touched tickets, work stalled on your side, customers stranded with a bot, and unassigned open work. Trigger for "report on our open tickets", "daily outstanding tickets report", "how many tickets have we not responded to", "find tickets waiting on us", "customers stuck with a bot and no human", aging backlog, or a backlog that keeps growing.

Use to check whether a conversation export, dataset or AI pipeline is leaking personal data that should have been redacted, and to measure how well the redaction actually works. Trigger for "is this export safe to share", "check our redaction", "can we use support transcripts for training", sending transcripts to a vendor or model, "is there PII in this dataset", or before opening support data to a wider audience.

Use to draw a defensible sample of support conversations for QA review, an audit or a manual analysis, so the results generalise to the population rather than to whatever was easy to pull. Trigger for "which tickets should we review", "how do we pick a sample", "is our QA sampling representative", stratified or risk-based sampling, review coverage design, or a finding based on a handful of hand-picked tickets.

Creates and updates coding standards, conventions, rules, and guidelines for the current project. Use when creating new standards from scratch, converting existing documents into coding standards, or updating existing standards. Does not create architectural decision records — use architectural-decision-record for ADRs. Does not write feature or system documentation — use project-documentation for that. Does not research open-ended options — use research. Does not produce runbooks for operational scenarios — use runbook for that.

Firebase gives you a complete backend in minutes - auth, database, storage, functions, hosting. But the ease of setup hides real complexity. Security rules are your last line of defense, and they're often wrong. Firestore queries are limited, and you learn this after you've designed your data model. This skill covers Firebase Authentication, Firestore, Realtime Database, Cloud Functions, Cloud Storage, and Firebase Hosting. Key insight: Firebase is optimized for read-heavy, denormalized data. I

Firebase gives you a complete backend in minutes - auth, database, storage, functions, hosting. But the ease of setup hides real complexity. Security rules are your last line of defense, and they're often wrong. Firestore queries are limited, and you learn this after you've designed your data model. This skill covers Firebase Authentication, Firestore, Realtime Database, Cloud Functions, Cloud Storage, and Firebase Hosting. Key insight: Firebase is optimized for read-heavy, denormalized data. I

Firebase gives you a complete backend in minutes - auth, database, storage, functions, hosting. But the ease of setup hides real complexity. Security rules are your last line of defense, and they're often wrong. Firestore queries are limited, and you learn this after you've designed your data model. This skill covers Firebase Authentication, Firestore, Realtime Database, Cloud Functions, Cloud Storage, and Firebase Hosting. Key insight: Firebase is optimized for read-heavy, denormalized data. I

Firebase gives you a complete backend in minutes - auth, database, storage, functions, hosting. But the ease of setup hides real complexity. Security rules are your last line of defense, and they're often wrong. Firestore queries are limited, and you learn this after you've designed your data model. This skill covers Firebase Authentication, Firestore, Realtime Database, Cloud Functions, Cloud Storage, and Firebase Hosting. Key insight: Firebase is optimized for read-heavy, denormalized data. I

Use to design and audit how customers are told they are interacting with AI, or that AI was involved in a decision about them, and to evidence that it happened. Trigger for "do we tell customers it's a bot", "AI disclosure requirements", "should the bot say it's not human", "customer asked if they were talking to a person", AI transparency obligations, or evidencing that an AI-assisted decision was explained.

Compose a monthly investor update — pull financials from Xero, MRR/churn from Stripe, product KPIs from PostHog, customer wins from Notion CRM, prior-update context from investors.md. Drafts in founder's voice, redacts per investors.md OMIT rules, packages as a branded Gamma deck (Google Doc fallback), and dispatches a Gmail cover note per investor (or BCC list) linking to the deck. Monthly cron (5th of month for prior month) or on-demand.

Use this skill when the user asks about passenger-carrying CMVs — buses, motorcoaches, charter operations, school buses, limousines, shuttle services. Covers different HOS rules (10-hour driving / 15-hour on-duty / no 34-restart), Class C CDL with P endorsement, S endorsement for school bus, FMCSA Hours-of-Service for passenger vehicles, $5M insurance minimum, BMC-32 filing, accessibility requirements, and how passenger ops differ from property. Cite 49 CFR 395.5 + 387.33.

Use to turn an ad-hoc CX reporting request into a versioned spec that can be re-run each period and actually compared across periods. Trigger for weekly or monthly QA and support reports, "same report but for last week", "generate the weekly digest", "supervisor report for each team", a report request pasted as a long prompt for the second or third time, or when two runs of the same report disagree.

Expert knowledge for Azure Networking development including troubleshooting, decision making, architecture & design patterns, and security. Use when designing hub-spoke VNets, Azure Firewall/NSG rules, App Gateway/Front Door WAF, DDoS, or fixing Microsoft.Network errors, and other Azure Networking related development tasks. Not for Azure Virtual Network (use azure-virtual-network), Azure Virtual Network Manager (use azure-virtual-network-manager), Azure Virtual WAN (use azure-virtual-wan), Azure Network Watcher (use azure-network-watcher).

Manage projects, issues, and workflows with Jira Cloud. Use when a user asks to set up Jira projects, create and manage issues, configure workflows, build Jira integrations, automate issue transitions, set up boards (Scrum/Kanban), manage sprints, use Jira REST API v3, create webhooks, build custom JQL queries, configure permissions and schemes, set up automation rules, or integrate Jira with CI/CD pipelines. Covers project administration, issue tracking, agile boards, API automation, and Atlassian Connect/Forge apps.