Results for “coaching”

Use whenever the user wants to find, shortlist, vet, or enrich US management consultancies — strategy, operations, executive coaching, leadership development, org-development/change management, PMO/program management, sales/revenue operations consulting. Triggers on "find me three top strategy consultancies in California", "shortlist boutique ops-consulting firms with healthcare experience", "we need an executive coach for our new CEO", or "pull contact info for these 10 consulting firm domains", even when described indirectly (post-merger integration help, change-management partner, fractional COO). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Skip in-house strategy hires, "help me build a strategy" do-the-work asks, framework comparisons (Lean vs Agile, BCG matrix, etc.), academic/MBA-program questions, life/career coaching for individuals, non-US firms, individual freelancers.

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Use when a manager, dispatcher, or safety director needs to have a coaching conversation with a driver — about a safety event (harsh brake, hard turn, distraction), an HOS violation, a customer complaint, a missed delivery, repeated minor incidents, or to deliver positive recognition. Covers conversation structure, language, documentation, and escalation.

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Design prices, plan tiers, and billing units around customer value and willingness to pay. Use it to plan price increases and review whether a pricing page clearly explains its offers to both buyers and AI agents.

• Set an initial price for a new product
• Define plan tiers and billing units
• Plan a gradual price increase

Plan AI application work across six areas: prompt evaluation, context allocation, retrieval-based answer generation, agent security, evaluation pipelines, and product strategy. This overview explains the approaches; the individual workflow packages, test cases, and CI templates are not included.

• Planning prompt evaluation before deployment
• Allocating context capacity and designing retrieval-based answers
• Planning an authorized AI agent security assessment

Check your gbrain setup and receive prioritized suggestions for overdue updates, pending migrations, stalled jobs, and other maintenance issues. Findings include repair commands, while any fixes require your approval.

• Reviewing your gbrain setup
• Getting a weekly maintenance check
• Prioritizing updates and stalled jobs

Analyze meeting transcripts to understand speaking share, verbal habits, listening, and facilitation. Receive feedback grounded in specific quotes, practical suggestions for future meetings, and comparisons across periods when enough meeting data is available.

• Reviewing speaking share and turn-taking in meetings
• Identifying filler words and indirect phrasing
• Improving questions and listening habits

Analyze sprint history to forecast delivery ranges, score team health, and track retrospective actions. Python tools turn structured sprint data into reports that support planning, highlight recurring problems, and show whether improvement efforts are progressing.

• Forecasting sprint workload from past delivery data
• Assessing team health and prioritizing improvements
• Tracking retrospective actions and recurring themes

Guide an AI agent through repeated failures, passive behavior, or unverified completion with strict Japanese coaching. It structures investigation, encourages different approaches, and requires evidence before reporting work as complete.

• Changing approach after repeated failures
• Requesting evidence for completed work
• Preparing a handoff for an unresolved problem

Provides a Codex CLI alias for loading the mama coaching protocol. It switches visible narration to Chinese-mother nagging while retaining verification rules, and preserves unrelated configuration fields whenever it updates the PUA settings file.

• Loading mama mode from Codex CLI
• Changing narration while retaining verification rules
• Preserving unrelated settings when updating the configuration

Interpret Culture Index behavioral profiles to understand individual traits, team balance, and possible strain from workplace adaptation. Use PDF or JSON data to inform hiring discussions, management approaches, onboarding plans, and conflict mediation.

• Understanding an individual’s behavioral profile
• Assessing team balance and missing roles
• Exploring strain from Survey and Job differences

Work through the move from product manager to Director with questions tailored to your stage. Get practical recommendations for promotion preparation, interviews, early leadership challenges, or persistent difficulties in the role.

• Identifying development priorities before a Director promotion
• Preparing experience stories and answers for Director interviews
• Addressing delegation and direction-setting challenges after promotion

Answer a short series of questions about your workflow to choose a Claude Code agent, command, skill, hook, or rule, then generate a template with the structure and file location for that component.

• Choosing how to implement a Claude Code workflow
• Creating a template for a reusable team procedure
• Planning automatic checks or persistent behavior rules

Practice one programming concept through a small exercise matched to your goals, experience, and available time. Get feedback based on observable results, with hints or explained solutions and a harder variation when you are ready.

• Practicing a programming concept in 10–15 minutes
• Understanding display width for CJK characters and emoji
• Tracing event order and cleanup

Manage for output using Grove's "High Output Management": a manager's output is their organization's output, raised by high-leverage activities. Use when the user mentions "high output management", "managerial leverage", "one-on-ones", "1:1 agenda", "OKRs", "performance review", "task-relevant maturity", "delegation", "meeting overload", "new manager", "how do I run a 1:1", or "just got promoted to manager". Also trigger when structuring a manager's calendar and meeting cadence, designing team metrics, running planning, coaching delegation, or preparing performance reviews. Covers leverage, production principles, meetings as the medium of management, decisions, OKRs, and task-relevant maturity. For intrinsic motivation, see drive-motivation. For a company operating system, see traction-eos.

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Self-coaching analysis of your own behavior across meetings — talk-time ratio, filler words, hedging language, monologue length, energy patterns, and (when meetings are tagged via /minutes-tag) what your behavior in winning meetings looks like vs losing ones. Use this whenever the user says "how did I do", "review my last meeting", "mirror", "self-review", "show my patterns", "coach me", "where am I weak", "talk time", "am I improving", "what do I do in meetings I win", "feedback on me", or asks for any kind of personal feedback on their own meeting behavior. This is the rare skill that gives the user a mirror to their own habits — surface it whenever they show curiosity about their own performance, even if they don't use the word "mirror".

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Design Thinking method coaching knowledge across all nine methods including per-method techniques, deep expertise, and industry context (energy, financial services, healthcare, manufacturing, nonprofit and social impact, pharmaceuticals and life sciences, professional services, public sector, retail and CPG)

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Coach a UX practitioner through problem framing, running a design critique, or making an evidence-backed case to a skeptical stakeholder. Use when the practitioner has a live UX task and wants to think it through rather than receive an answer.

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Produce evidence-labelled UX needs, journey, structure, inclusion, and engineering-handoff assets. Use when a practitioner needs a durable UX artifact rather than coaching.

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Experiment design reference for problem-class framing, Minimum Viable Experiment coaching, hypothesis formation, vetting and red flags, and experiment readiness. Use when translating a stated business outcome into candidate data-science problem classes, or when framing, vetting, scoping, or evaluating an experiment of any kind, including data feasibility, architecture, LLM, performance, use-case, UX, prototyping, and hardware experiments.

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Data-driven Scrum Master for sprint health scoring, Monte Carlo velocity forecasting, retrospective analysis, capacity planning, and Tuckman team coaching. Use when facilitating sprint planning, diagnosing velocity, or running retrospectives.

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Generate structured reflection prompts for a specific teaching experience or professional learning challenge. Use when debriefing lessons, journaling, or preparing for coaching conversations.

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Design a focused lesson observation protocol with specific look-fors and evidence collection methods. Use when planning peer observations, coaching visits, or developmental classroom visits.

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Design multi-informant assessment approaches for dispositional competencies like curiosity or resilience. Use when assessing character strengths or competencies that written tests cannot capture.

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When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' 'monetization,' 'how much should I charge,' 'my pricing is wrong,' 'pricing page,' 'annual vs monthly,' 'per seat pricing,' 'should I offer a free plan,' 'pricing page teardown,' 'pricing page audit,' 'is my pricing page AI-readable,' or 'can AI read my pricing.' Use this whenever someone is figuring out what to charge, how to structure their plans, or wants to audit a pricing page (for humans and for the AI agents that shortlist tools). For in-app upgrade screens, see paywalls. For offer construction (bonuses, guarantees, value framing, naming) on services/courses/coaching/high-ticket B2B, see offers.

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