Results for “plan”

Use when the next period ADS plan must be built from last period results — read the report and the audit, keep and scale winners, stop or fix losers, set new test hypotheses, split budget, plan three budget scenarios, and schedule the creative pipeline. Trigger on 'next month ads plan', 'plan ads from the report', 'what should we run next month', 'Q4 ads plan', 'next quarter media plan', 'the month ended, now what'. Also use when a report and an audit both exist and no decisions have been made. Not for — the first media plan before any data, see `54-media-plan-global`; scoring the account, see `21-ads-audit-global`; the next period CONTENT plan, see `40-next-content-plan-global`.

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Execute an implementation plan file produced by $draft-plan or $turboplan. Runs pre-implementation prep, then runs $implement to execute the steps and finalize once they are all done. Use when the user asks to "implement plan", "implement the plan", "execute the plan", "run the plan", "implement plans/<slug>.md", "start implementing the plan", or starts a fresh session to implement a previously drafted plan.

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Execute an implementation plan file produced by /draft-plan or /turboplan. Runs pre-implementation prep, then runs /implement to execute the steps and finalize once they are all done. Use when the user asks to "implement plan", "implement the plan", "execute the plan", "run the plan", "implement plans/<slug>.md", "start implementing the plan", or starts a fresh session to implement a previously drafted plan.

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Break a trusted implementation plan (or other provided context) into independently-grabbable, atomic work items, written to a single work-items.md file. Use when the user wants to convert a plan into work items, create implementation tickets or tasks, divide a plan into work units, or break the plan down into grabbable pieces. Do not use when there is no implementation plan yet or the plan is not yet trusted — use plan-implementation to produce the plan or iterative-plan-review to harden it first. Does not sequence work into demoable delivery phases — use plan-a-phased-build for that. Does not write code — use tdd to implement a work item.

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Structured planning mode for AI agents working on coding and development tasks. Produces a comprehensive plan BEFORE any code is written or changed, including task decomposition, architecture decisions, risk assessment, and approval gates. Use this skill whenever the user says "/plan", "plan mode", "plan first", "think before coding", "make a plan", or asks the agent to plan before executing. Also trigger when the user describes a complex multi-step development task and would clearly benefit from structured planning before implementation — especially for tasks involving new features, refactors, migrations, infrastructure changes, or multi-file edits. When in doubt, plan. A few minutes of planning prevents hours of rework.

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Decompose multi-step work into a revisable subgoal plan with stable step IDs, checkpointing, and plan-ahead execution. Load when a task needs explicit planning before action, subgoal decomposition, plan checkpointing, or ReCAP-style plan-ahead execution. Also triggers on "structured plan", "plan with steps", "subgoal graph", "revise the plan", "checkpoint plan", or multi-step tasks where naive one-shot execution would be fragile. Skips trivial single-step tasks. Pairs with dynamic-routing on failure. Distinct from process-decomposer (registry/triage) and problem-to-plan (doc deliverables).

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Carry out a phased implementation plan with subagents assigned to implementation, verification, pattern checks, and code review. Each phase must pass verification before changes are committed and pushed, with context prepared for the next phase.

• Execute a plan created with make-plan
• Delegate implementation work by phase
• Verify changes and review code before committing

Research a coding task before implementation and turn it into a detailed work plan with decisions, dependencies, and verification steps. The plan is written after approval, while code changes remain a separate workflow.

• Planning a large change before implementation
• Turning a vague request into concrete tasks
• Preparing a plan for another agent to execute

Research existing code, documents, and relevant sources before producing a practical plan for implementation, migration, debugging, or evaluation. The plan explains key decisions, ordered steps, and validation checks, giving users something concrete to review before execution.

• Plan implementation steps before changing code
• Design a phased migration with rollback steps
• Break work into separate commits or pull requests

Carry out an engineering plan through stepwise code changes, dependency impact checks, tests, and focused commits. It also handles small, bounded tasks directly, checking the code relationship index before relying on its results.

• Implementing an existing engineering plan
• Making a small change across one or two files
• Testing each step before committing

Use German-language guidance to organize multi-step agent work in Markdown files. Store plans, findings, and progress by task, then revisit those records to resume research, review decisions, and track unfinished phases.

• Managing research with German-language guidance
• Recovering task context after a break
• Separating plans for concurrent tasks

Organize multi-step research and project work in Continue using Markdown files for plans, findings, and progress. When invoked, the skill reads saved planning context to support resuming work and reviewing earlier decisions.

• Resuming interrupted work in Continue
• Recording findings during research
• Tracking project phases and decisions

Turn requirements for software or other multi-step work into a structured plan with an approach, constraints, and verification steps. It also helps develop existing plans, researching decisions before handing the work over for execution.

• Planning implementation from requirements
• Breaking complex work into manageable steps
• Adding detail to an existing plan

Review development plans with external AI tools, then revise and review them again while actionable concerns remain. Choose reviewers and a cycle limit to work toward a plan with resolved review findings.

• Review a development plan before implementation
• Revise plans using feedback from multiple AI reviewers
• Use a local model server to review plans

Open files, folders, or URLs in Plannotator’s annotation interface, then have the agent address the returned comments. Use it to review Markdown, plain-text configuration files, or HTML through a shared feedback workflow.

• Reviewing a Markdown document
• Commenting on configuration files
• Opening a folder for annotation

Use when you have a finalized brainstorm-beagle spec at `.beagle/concepts/<slug>/spec.md` and need a bite-sized, TDD-driven implementation plan before any code is written. Triggers on: "write a plan", "plan this spec", "turn the spec into a plan", "now plan the implementation", "write-plan". Reads the spec, designs the file structure, decomposes work into 2-5 minute TDD steps with exact paths and commands, self-reviews against the spec, gets user approval, then writes to `.beagle/concepts/<slug>/plan.md` and offers to generate an execution handoff prompt via the subagent-prompt skill. Does NOT brainstorm specs, write code, or execute the plan — produces the plan document (and an optional handoff prompt) only.

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承認済みの実装計画（Plan）を Agent Team で並列に実装するスキル。 Plan をタスクに分割 → 依存関係を設定 → Wave ごとに並列エージェントを起動 → 完了を待って次の Wave → 最後にビルド＆テスト検証。 team-plan スキル（調査→計画）の後に使う「実装実行フェーズ」。 Use when: 計画を並列に実装したい、チームで実装を進めたい、 Wave 実行で効率よくコードを書きたい。 Triggers: "team implement", "チームで実装", "並列実装", "plan を実装して", "wave 実行", "team-implement", "エージェントで実装", "実装フェーズ", "implement the plan"

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Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.

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Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why.

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plan artifact がある時のみ、plan 中の assumption / open question が実装で解消された証拠を diff・PR 本文と突合する evidence-sufficiency 観点。解消されないまま残った前提・未記録の新規 Unknown を検出する。plan 欠損時は発火しない（Pre-execution Gate）。plan 欠損でも PR 本文に前提が inline 列挙されていれば列挙分のみ部分評価し、計画 issue の bare 参照だけなら skip する

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Tactical fast path: turn a small problem, bug report, edit request, or narrow refactor into three deliverables — a brief change-spec (docs/specs/), a detailed implementation-ready plan (docs/plans/), and a TODO.md with agent-pickable tasks and milestones. Load when the user describes a tactical problem and wants quick planning artifacts, says "plan this change", "create a TODO", "write a plan for this", "problem to plan", "break this into tasks for agents", "I want to change X — plan it", or when process-decomposer routes here after determining the user needs lightweight planning deliverables. Also triggers on "create tasks from this problem", "make this actionable", or "turn this into a plan agents can execute". For feature-sized work that needs an executable spec + constitution + cross-check gate, route to `spec-driven-development` (or `feature-spec`) instead.

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Set up Plankton hooks in Claude Code to format and lint files after each edit. Route remaining violations to Claude for fixes, verify the results, and protect linter settings from changes that weaken checks.

• Formatting and linting files after every edit
• Setting up automatic fixes for remaining violations
• Protecting linter configuration from rule weakening

Uses multiple agents with different critical roles to challenge assumptions, complexity, risks, and alternatives. Findings that withstand review go to a dedicated planner, who turns them into sequenced tasks with explicit success criteria.

• Reviewing assumptions before implementation
• Checking integration risks and edge cases
• Comparing alternative approaches

Turn observations or preliminary findings into testable research plans. The skill separates candidate hypotheses from evidence, develops competing explanations and distinguishing predictions, and helps specify measurements and analysis plans for preregistration, a research plan recorded before testing.

• Turn an observation into testable research hypotheses
• Compare competing explanations and distinguishing predictions
• Define measurements and review an analysis plan