Skip to main content
ClaudeWave
Skill1.3k repo starsupdated 3d ago

account-plan

Build a structured account plan for any key customer or target account. Use when asked to create an account plan, key account strategy, strategic account review, or territory plan. Produces a complete account plan with relationship map, growth opportunities, risks, and 90-day action plan.

Install in Claude Code
Copy
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/account-plan && cp -r /tmp/account-plan/exports/openclaw/account-plan ~/.claude/skills/account-plan
Then start a new Claude Code session; the skill loads automatically.

SKILL.md

# Account Plan Skill

Produces a structured account plan — the document that separates account managers who grow accounts from those who just service them.

## Required Inputs
- **Account name**
- **Current ARR / revenue**
- **Contract renewal date**
- **Key contacts** (names, roles, relationship strength)
- **Products/services currently in use**
- **Known opportunities or expansion areas**
- **Known risks**
- **Planning horizon** (6 / 12 / 24 months)

## Output Structure

---

# Account Plan: [Account Name]
**Account Manager:** [Name] | **Period:** [Date range]

---

### Account Snapshot

| Metric | Current | Target (EOY) |
|---|---|---|
| ARR / Revenue | £[amount] | £[target] |
| NPS / Health score | [Score] | [Target] |
| Products in use | [List] | [Expansion targets] |
| Renewal date | [Date] | — |
| Risk level | Low / Medium / High | — |

---

### Relationship Map

| Name | Title | Influence | Relationship | Notes |
|---|---|---|---|---|
| [Name] | [Role] | Decision maker / Influencer / User | Strong / Neutral / Weak | [Insight] |

**Relationship gaps:** [Who we do not have access to that we should]
**Executive sponsor:** [Do we have one? If not — who could become one?]

---

### Why They Stay (Retention Anchors)
[2-3 specific reasons this account renews. If the list is short, that is the risk signal.]

---

### Growth Opportunities

| Opportunity | Product | Est. Value | Timeline | Next Action |
|---|---|---|---|---|
| [Opportunity] | [Product] | £[value] | [Q/Year] | [Specific action] |

**Whitespace:** What products do we have that this account does not use, and why?

---

### Risks and Mitigation

| Risk | Likelihood | Impact | Mitigation | Owner |
|---|---|---|---|---|
| [Risk] | H/M/L | H/M/L | [Action] | [Name] |

---

### 90-Day Action Plan

| Action | Why | Owner | Due |
|---|---|---|---|
| [Specific action] | [Why it matters] | [Name] | [Date] |

**Next QBR / EBR:** [Date — if no EBR cadence, flag as a risk]

---

### Success Criteria
At end of [period]:
- Renewed at or above current ARR
- [Expansion opportunity] progressed to [stage]
- Health score moved from [current] to [target]

## Anti-Patterns

- [ ] Do not list only executive contacts in the relationship map — champions and day-to-day users are often more influential on renewal decisions
- [ ] Do not set growth opportunity estimates without a basis — even rough ARR values prevent the plan from being treated seriously
- [ ] Do not treat "no known risks" as acceptable — if no risks are identified, the plan hasn't been scrutinised honestly
- [ ] Do not write 90-day actions as vague aspirations ("strengthen the relationship") — each action must specify a call, meeting, or deliverable with a named owner

## Quality Checks

- [ ] Relationship map identifies decision-makers, influencers, and any relationship gaps
- [ ] Risks all have mitigation actions and named owners
- [ ] Growth opportunities include estimated value (even roughly)
- [ ] 90-day actions are specific (not "have a call" — what call, with whom, to achieve what)
- [ ] Success criteria are measurable at the end of the planning period
ai-ethics-reviewSkill

Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.

ai-product-canvasSkill

Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.

design-handoff-briefSkill

Transform feature briefs into structured design briefs that give designers the context they need before opening Figma. Use when asked to write a design brief, create a design handoff, brief a designer on a new feature, or translate a PRD into design requirements. Produces a brief with user goal, emotional context, success criteria, constraints, edge cases, and out-of-scope boundaries.

experiment-designerSkill

Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags — or a results interpretation with ship/iterate/kill recommendation.

multi-source-signal-synthesiserSkill

Synthesises user signals from multiple research sources into a unified, weighted insight brief. Use when you have data from interviews, support tickets, NPS verbatims, app reviews, or sales calls and need to reconcile contradictions, surface the underlying need behind requests, or answer 'what are users really telling us'. Produces ranked insights with confidence ratings, source weighting rationale, divergent signal analysis by user segment, and a research gap identification section.

data-analysis-standardSkill

Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.

product-health-analysisSkill

Interpret product metrics against goals and surface actionable signals. Use when asked to analyse product health, review key metrics, investigate a performance issue, produce a health report, or assess product-market fit signals. Produces a structured health report with RAG status, trend analysis, root cause hypotheses, and prioritised actions.

retention-analysisSkill

Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions.