Results for “rule”

Use to assemble evidence that customers are getting good outcomes from support, at the level a board or regulator expects, rather than reporting activity metrics. Trigger for "evidence our customers get good outcomes", "consumer duty evidence from support", "board report on customer outcomes", outcomes-based regulatory reporting, or a request to show harm is not occurring.

Use to find where support can drive adoption, referral and retention without turning into a sales channel, and to measure whether it worked. Trigger for "can support drive growth", "support-led growth programme", "should agents mention other features", advocacy and referral from support, adoption nudges in support conversations, or a support team being given a revenue target.

Use to find the activation blockers visible in support contacts from new customers, before they show up in retention numbers months later. Trigger for "why aren't new customers activating", "what do new customers contact us about", onboarding drop-off, time-to-value analysis, first-90-days support experience, or a retention problem that traces back to activation.

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.

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.

Tend the Allium garden. Use when the user wants to write, edit, update, add to, improve, clarify, refine, restructure, fix or migrate Allium specs. Covers adding entities, rules, triggers, surfaces and contracts, fixing syntax or validation errors, renaming or refactoring within specs, migrating specs to a new language version, and translating requirements into well-formed specifications. Pushes back on vague requirements.

Use this skill when the user asks about CMV oilfield operations — the 24-hour restart exemption (49 CFR 395.1(d)), oilfield-specific HOS rules, hot oil hauling, specialty equipment (winch trucks, kill trucks, frac sand haulers, water haulers), Texas Railroad Commission permitting, oilfield insurance specifics, and how oilfield operations differ from standard OTR trucking. Cite 49 CFR 395.1(d).

Use this skill when the user asks about US-Canada cross-border CMV operations — CVOR (Commercial Vehicle Operator's Registration), NSC (National Safety Code), Ontario / Quebec / BC specific rules, US carrier obtaining Canadian operating authority, FAST card for drivers, customs (CBSA), Health Canada hazmat, and how Transport Canada equivalents differ from US FMCSA. Reference USMCA + Transport Canada NSC 14.

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.

Convert configured local Markdown collections to PDFs and upload the generated PDFs to Feishu/Lark Drive. Use when Codex needs to run or maintain a recurring local Markdown-to-PDF publishing workflow, inspect a private rules file, dry-run source/category mappings, invoke an external open-source Markdown PDF converter, or upload generated PDFs to a Feishu Drive folder.

Use to quantify the revenue exposed by unresolved support failures, complaints and escalations without inflating the number. Trigger for "how much revenue is at risk", "what are these support failures costing us", "revenue behind our open escalations", building a business case for fixing a support problem, or a revenue-at-risk figure that looks too large to believe.

Use to design support career progression with IC and lead tracks, skill gates instead of tenure alone, and paths that do not treat leaving the phones as the only promotion. Trigger for "career pathing", "progression framework for support", "IC track", "how do agents get promoted", "support ladder", "team lead vs senior agent", or fixing promotion bottlenecks and title inflation.

Use to define what actually predicts support performance in your organisation and build a hiring profile around validated predictors — structured work samples, not vibes. Trigger for "hiring profile", "what should we look for in support hires", "interview scorecard for agents", "reduce bad hires", "work sample test", screening criteria review, or checking whether your interview process predicts ramp and QA.

Use to design or audit the support contribution to a customer health score, so the score predicts something instead of averaging weakly-related signals into a colour. Trigger for "build a customer health score", "add support signal to health scoring", "our health scores don't predict churn", red/amber/green account scoring, or a health score nobody trusts.

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.

Maintains a project's domain vocabulary and records architecturally-significant decisions as they're made — a CONTEXT.md glossary and gated ADRs, not passive documentation read once. Use when terminology conflicts or turns fuzzy, when making a decision that's hard to reverse/surprising/a real trade-off, or when a stated domain rule doesn't match what the code actually does.

Addresses HIPAA compliance for mobile health (mHealth) applications, wearable devices, and remote patient monitoring. Covers OCR guidance on mobile device PHI, FDA-regulated mobile medical applications, FTC Health Breach Notification Rule for non-HIPAA apps, BYOD policies, and encryption requirements for ePHI on mobile platforms. Keywords: mHealth, mobile health, HIPAA mobile, wearable, remote monitoring, BYOD, mobile device management, app privacy.

4-layer recall procedure (durable → semantic → PostgreSQL → daily notes) before claiming "no context". Invoke when user explicitly asks "do you remember", "what did we discuss about X", "search history". NOT a general assistant — only fires when recall is the explicit task. Drop the prior "use proactively" rule. Triggers: "do you remember", "what did we discuss", "when did", "find that conversation about".

Use this skill when the user asks about cargo securement requirements under 49 CFR 393 Subpart I — Working Load Limit (WLL), tiedown counts, tiedown types, commodity-specific securement rules (lumber, metal coils, paper rolls, concrete pipe, intermodal containers, vehicles, large boulders, dressed lumber, building materials), CVSA out-of-service criteria, and how to calculate aggregate WLL. Cite 49 CFR 393.100-136.

Plan, execute, document, and retest authorized security assessments of AI agents and multi-agent workflows using safe adversarial cases, synthetic identities, canaries, and evidence-based findings. Use when defining red-team rules of engagement, assessing prompt injection or excessive agency, testing tool and identity boundaries, evaluating memory or cross-agent attacks, scoring a campaign, or verifying remediation in an approved environment.

Use to reconstruct what actually happened to one customer across every conversation, channel and handoff, for an escalation, complaint, post-mortem or goodwill decision. Trigger for "build a timeline for this customer", "what happened on this case", "summarize the back-and-forth before I decide", "when were they first told", escalation summaries, complaint investigations, or a case spanning several tickets.

Perform a static audit of an AL workspace for Business Central against the project's copilot-instructions.md contract. Reports findings by class (Compliance, Code quality, Test coverage, Permissions) and severity (Blocker, Major, Minor, Nit) with file:line citations. Never modifies code. Never invents rules. Use when asked to audit a workspace, run a compliance check, review before PR, or diagnose contract drift.

12-agent academic paper writing pipeline. 10 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見.

Use when support or CS is being asked to claim a share of net revenue retention, expansion or churn reduction, to work out what can honestly be attributed and what cannot. Trigger for "how much NRR can support claim", "prove support's revenue impact", "attribute retention to CS", support ROI for a board deck, or two teams claiming the same expansion.

Use to map where support conversation data actually goes — systems, vendors, countries, AI providers — and compare it against what the privacy notice and records say. Trigger for "where does our support data go", "map our data flows", "which vendors process our conversations", cross-border transfer review, ROPA accuracy for support, or a new tool that was connected without a review.

Use to analyse touch time vs elapsed time across the resolution path — queue wait, customer wait, back-office wait, and where elapsed time hides real delay. Trigger for "flow efficiency", "touch time vs elapsed time", "where is the time going", "customers waiting on us", back-office bottlenecks, idle time in cases, or end-to-end time much longer than handle time.

Use to design the process that gets a revenue signal out of support and to the account owner without turning agents into sellers or degrading support quality. Trigger for "route upsell leads from support to sales", "support-sourced pipeline process", "should agents flag opportunities", designing a CS-to-sales handoff, or a handoff programme where agents have stopped flagging.

Use to analyse average handle time, resolution time or time-in-queue without being misled by the skew, and to find where time actually goes. Trigger for "why is our AHT increasing", "our call handling times are up", "which contact reasons take longest", "how long do tickets take", handle time by agent or team, or an AHT target being set.

Use to design or audit the executive escalation path for a major account — intake, ownership, communication cadence and follow-through — so escalations are resolved rather than merely attended. Trigger for "design our escalation process for key accounts", "an executive escalation went badly", "customer went to our CEO", red account war rooms, or escalations that get attention and then quietly stall.

Use to review how call recordings are notified, stored, accessed, redacted and deleted, including the parts that differ from text conversations. Trigger for "review our call recording practices", "do we need to tell customers we're recording", "who can listen to our calls", recording retention, card details spoken on a call, or a request for a customer's own recording.

Supabase-first reference and implementation skill for Nick's app stack. Use when writing Supabase queries, auth flows, RLS policies, storage rules, realtime features, edge-function integrations, migrations, or debugging Supabase behavior. Prefer this instead of guessing Supabase APIs or patterns. Bias toward product-led implementation choices, trust-aware access control, MVP discipline, and using only the Supabase features the product actually needs.

Use to check that required disclosures, consents and notices were actually given in support conversations — the right wording, at the right point, through a channel the customer could act on. Trigger for "did we give the required disclosure", "are we capturing consent properly", "recording notification audit", "check our mandatory notices", disclosure compliance, or a complaint that a customer was not told something.

Use to review how an outsourcer, BPO or vendor handles your customer data and meets your conduct obligations, using evidence from the work rather than from their questionnaire answers. Trigger for "review our BPO's data handling", "vendor risk assessment for our outsourcer", "are our partners compliant", third-party oversight, outsourcing due diligence, or a supplier audit of a support vendor.

Use to measure how long new support agents take to reach proficiency and where their ramp stalls, so training and nesting can be targeted. Trigger for "how long until new hires are productive", "time to proficiency", "how is the new cohort doing", nesting or ramp design, comparing training cohorts, or a new hire being assessed before they have had time to ramp.

Conducts comprehensive inventory of protected health information across the enterprise per HIPAA Security Rule requirements at 45 CFR §164.308(a)(1)(ii)(A) and §164.310(d). Covers identification of all ePHI repositories, data flow mapping, classification of PHI by sensitivity, and integration with risk analysis. Keywords: PHI inventory, ePHI, data mapping, information asset, data flow, HIPAA risk analysis, designated record set.

Use to prove that a support process, policy or script change actually took effect — when, for whom, and whether behaviour followed — rather than that it was approved. Trigger for "prove we made that change", "when did this policy actually change", "did the team adopt the new process", evidencing remediation, "we told everyone" claims, or a change that was signed off and never landed.