Skip to main content
ClaudeWave
Skill963 estrellas del repoactualizado 4d ago

investor-update

The investor-update skill generates a structured monthly or quarterly investor update formatted for early-stage and growth investors. Use it when preparing progress reports for your board, investor newsletter, or funding communications. The skill produces a complete update including metrics tables, highlights, challenges, priorities, and specific asks, following a format designed to build credibility through clear storytelling and honest problem disclosure.

Instalar en Claude Code
Copiar
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/investor-update && cp -r /tmp/investor-update/plugins/pm-business/skills/investor-update ~/.claude/skills/investor-update
Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

SKILL.md

# Investor Update Skill

This skill writes a complete investor update — structured for clarity, honest about challenges, and specific about asks. Output follows the format preferred by most early-stage and growth investors.

## Required Inputs

Ask the user for these if not provided:
- **Company name and stage** (Seed / Series A / Series B / etc.)
- **Period covered** (month or quarter)
- **Key metrics this period** (revenue, MRR, users, churn, burn, runway — whatever's relevant)
- **Biggest wins**
- **Biggest challenges or misses**
- **Specific asks from investors** (intros, advice, talent, partnerships)
- **What's coming next period**
- **Tone** (formal / conversational — most investors prefer conversational)

## Output Structure

---

**[Company Name] — [Month/Quarter] Update**
*[Date]*

---

Hi [Investor names or "all"],

[One or two sentence opener — a specific highlight or honest framing of the period. Don't open with "Hope you're well." Open with the most important thing that happened.]

---

## The Numbers

| Metric | This Period | Last Period | Change |
|---|---|---|---|
| [MRR / ARR] | [Value] | [Value] | [+/- %] |
| [Active users / customers] | | | |
| [Churn rate] | | | |
| [Burn rate] | | | |
| [Runway] | | | |
| [Other key metric] | | | |

[1–2 sentences of narrative on the numbers — what's the story behind the movement? Don't just repeat the table.]

---

## Highlights

**[Highlight 1 — 4–6 word title]**
[2–4 sentences. What happened. Why it matters. Be specific — name the customer, the number, the milestone.]

**[Highlight 2]**
[2–4 sentences]

**[Highlight 3 — optional]**

---

## Challenges

[This section is what separates trustworthy updates from self-promotional ones. Investors know you have challenges. Being direct builds trust.]

**[Challenge 1]**
[2–4 sentences. What the problem is. What you've tried. What you're doing about it. Don't spin — investors see through it.]

**[Challenge 2 — if applicable]**

---

## Focus for Next [Month/Quarter]

[3–5 bullet points. What you're concentrating on next period and why. Keep it tight — not an exhaustive roadmap.]

- [Priority 1]
- [Priority 2]
- [Priority 3]

---

## Asks

[Be specific. "Let me know if you can help" is not an ask. These should be actionable items an investor can act on immediately.]

1. **[Ask type: e.g. Intro]** — [Specific request. e.g. "Looking for an intro to procurement leads at mid-market SaaS companies. Happy to share a warm intro note."]
2. **[Ask type: e.g. Advice]** — [Specific question you want input on]
3. **[Ask type: e.g. Talent]** — [Specific hire you're looking for — title, key requirements]

---

[Closing line — 1 sentence. Forward-looking or a genuine thanks. Not "as always, let me know if you have questions."]

[Signature]
[Name]
[Company]
[One way to reply — email / Calendly / reply to this thread]

---

## Writing Rules

- Updates should take an investor 3–4 minutes to read. If it's longer, trim it.
- Never lead with process ("This month we focused on...") — lead with outcomes
- Challenges section must be honest. A missing challenges section signals the founder isn't self-aware or isn't being transparent.
- Metrics table must include comparison to last period — a number without context is meaningless
- Asks must be specific enough that an investor knows within 5 seconds if they can help
- No jargon or buzzwords ("synergies," "crushing it," "hockey stick") — plain language only

## Quality Checks

- [ ] Opens with a specific highlight or honest framing (not a pleasantry)
- [ ] Numbers include period-over-period comparison
- [ ] Challenges section is present and honest
- [ ] Asks are specific and actionable
- [ ] Total length is skimmable in 3–4 minutes
- [ ] No spin or buzzwords

## Anti-Patterns

- [ ] Do not omit challenges or bad news — sanitised updates erode investor trust faster than bad results do
- [ ] Do not bury the lead — use BLUF structure and put the most important news in the first paragraph
- [ ] Do not send an update without a clear "Ask" section — investors who want to help need to know how
- [ ] Do not use buzzwords or spin — investors see hundreds of updates and will see through vague positive language
- [ ] Do not report metrics without a comparison baseline — numbers without context (vs. last period or target) are meaningless

## Example Trigger Phrases

- "Write an investor update for [month/quarter]"
- "Draft a monthly update for our investors based on these notes: [paste notes]"
- "Help me write a board update for Q[N]"
- "Write our Series A investor newsletter"
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.