本文へ移動
cccskills
無料GitHub で公開

pipeline-health-analyzer

Analyze pipeline health, identify stalled deals, predict close probability, and suggest actions to move deals forward. Improves forecast accuracy and prevents revenue leakage. Use when deals get stuck or forecast accuracy is poor.

インストール方法を見る

含まれるファイル(6)

  • SKILL.md2.9 KB
  • references/email-templates.md1.2 KB
  • references/examples.md1.5 KB
  • references/forecasting.md3.1 KB
  • references/output-template.md8.5 KB
  • references/stage-analysis.md3.0 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Pipeline Health Analyzer

Identify pipeline risks, predict deal outcomes, and prescribe specific actions to accelerate stalled opportunities.

Contents

  • references/output-template.md - Full report skeleton to populate.
  • references/stage-analysis.md - Per-stage deep-dive patterns (Discovery, Demo, Proposal) and why deals stall.
  • references/forecasting.md - Forecast tables, probability calibration, AI re-scoring, scenario planning.
  • references/email-templates.md - Re-engagement email templates for stalled and dark deals.
  • references/examples.md - Trigger phrases, a worked example request, and best practices.

Workflow

  1. Obtain the pipeline data. Request a CSV export with deal name, stage, value, rep, deal age, days in current stage, last activity date, close date, and probability if not provided.
  2. Compute pipeline-by-stage metrics: deal count, total value, average deal size, average days in stage, and stage-to-stage conversion rates. Compare each against historical benchmarks.
  3. Score each deal across six health dimensions: stage velocity, engagement level, qualification depth, stakeholder coverage, competitive position, and 30-day momentum.
  4. Identify stalled and at-risk deals. Flag deals exceeding benchmark time in stage, with no recent activity, with slipped close dates, or with single-threaded contacts.
  5. For each critical deal, determine the root cause and prescribe prioritized actions (immediate, this-week, backstop). Pull re-engagement copy from references/email-templates.md.
  6. Build the forecast: categorize deals (Commit/Best Case/Pipeline/Upside), calculate weighted and risk-adjusted value, check probability calibration against actual close rates, and re-score outliers. Follow references/forecasting.md.
  7. Model best-case, expected, and worst-case scenarios against quota, with a mitigation plan for the downside.
  8. Produce strategic recommendations across immediate, short-term, and long-term horizons.
  9. Assemble the report using references/output-template.md. Use plain status words (Healthy / At Risk / Critical) and trend words (Up / Flat / Down); never use emoji.
  10. Close with the report card, next-review date, and week-over-week KPIs to track.

Operating Principles

  • Run weekly, not monthly; pipeline health degrades fast.
  • Base assessments on activity metrics, not rep intuition.
  • Disqualify dead deals early; a flowing pipeline is healthy.
  • Always end with concrete next steps, not just analysis.
  • Coach with the insights; do not weaponize them against reps.

See references/examples.md for trigger phrases, a worked example, and the full best-practices list.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.

日本語の概要は準備中です。原文の説明を表示しています。

OneWave-AI/claude-skills3392026年10月2日 更新

Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.

日本語の概要は準備中です。原文の説明を表示しています。

OneWave-AI/claude-skills3392026年10月2日 更新

Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.

日本語の概要は準備中です。原文の説明を表示しています。

OneWave-AI/claude-skills3392026年10月2日 更新

Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.

日本語の概要は準備中です。原文の説明を表示しています。

OneWave-AI/claude-skills3392026年10月2日 更新

Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.

日本語の概要は準備中です。原文の説明を表示しています。

OneWave-AI/claude-skills3392026年10月2日 更新

Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.

日本語の概要は準備中です。原文の説明を表示しています。

OneWave-AI/claude-skills3392026年10月2日 更新

OneWave-AI のスキルをすべて見る

このスキルの問題を報告する