metrics-briefing
Skill1 starsChanged 8 months agoMITView on GitHub
CEO-level metrics interpretation through 9-category framework (market learning, product value, usage, pipeline, retention, B2C->B2B, execution, economics, PMF). For raw analytics data queries, use your analytics skill (e.g., posthog-analytics) instead.
1 other project uses a version of this, 1 of them edited.
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- CodeAlive-AI/ceo-ai-osedited, first seen May 18, 2026
Other versions1 project uses a version of this.
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Edited · 1
- CodeAlive-AI/ceo-ai-os★ 944% same
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How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 17
Do they share the same painful problem?
- Distinctive: line 18
Do they get value quickly?
- Distinctive: line 19
Do they come back?
- Distinctive: line 127
Follow output preferences from USER.md (language, format, platform constraints).
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Metrics Briefing
Metrics analysis through CEO Bible Section G — 9 categories. An analytics tool covers some; the rest come from conversations, CRM, and the founder's observations.
Principles (Bible G)
- All metrics by target wedge. Segment first, aggregate second.
- Behavior > opinions. Usage, money, time spent > compliments.
- Pre-PMF metrics answer 5 questions:
- Are we talking to the right people?
- Do they share the same painful problem?
- Do they get value quickly?
- Do they come back?
- Can we win them again without heroics?
- MRR, CAC, LTV are guardrails, not the steering wheel. Until there is repeatability.
9 CEO Metrics Categories
1. Market Learning (MANUAL — ask the founder)
| Metric | Definition | Source |
| Qualified conversations/week | 20+ min conversations with target roles; explored problem, trigger, workaround, buyer, urgency | Manual — track directly, target 8-12/week |
| Repeated pain rate | Same problem + trigger + workaround recur / total qualified conversations | Manual — from conversation notes |
| Objection concentration | Losses/stalls by top 3 reasons / all losses | Manual — from CRM/notes |
If conversations < 8/week -> flag hard. This is the main leading indicator.
2. Product Value (Analytics tool: partial)
| Metric | Definition | Source |
| Median TTFV | Time from signup to first meaningful success | Analytics — time between signup and first meaningful interaction event |
| Activation rate | Target accounts hitting critical event sequence within X days | Analytics — accounts reaching "meaningful result" milestone / total signups |
| Outcome attainment | Activated accounts achieving promised business result | Analytics — meaningful result → return usage conversion rate |
3. Usage / Engagement (Analytics tool: yes)
| Metric | Definition | Source |
| Retained active target accounts (week 4/8) | Accounts still performing core workflow | Analytics — accounts reaching "weekly active user" milestone |
| Workflow depth | Core workflow at target frequency, multiple use cases | Analytics — core action frequency, feature breadth per account |
| Second-bite rate | Users repeating core action within X days | Analytics — meaningful result → return usage conversion rate |
4. Sales / Pipeline (MANUAL — CRM/conversations)
| Metric | Definition | Source |
| ICP pipeline created | New opportunities from target wedge per week | Manual — CRM |
| Meeting -> opportunity | New opps / qualified first meetings | Manual — CRM |
| Opportunity -> win rate | Closed-won / closed in target wedge | Manual — CRM |
| Median sales cycle | Days from first meeting to signature | Manual — CRM |
5. Retention / Expansion (Analytics tool: partial)
| Metric | Definition | Source |
| Healthy account rate | Paid accounts with green usage + outcome + active champion | Analytics — accounts at regular/power usage stage, grouped by company |
| Expansion pull rate | Healthy accounts requesting more seats/team rollout | Analytics — accounts transitioning into team/multi-user stages |
| Gross logo retention | Paying logos retained once cohorts mature | Partial analytics — inferred from sustained usage stages; verify manually |
6. B2C -> B2B Conversion (Analytics tool: yes — key category)
| Metric | Definition | Source |
| Target-signup rate | Signups from target roles/domains / total signups | Analytics — filter signups by verified identity + connected integrations |
| Activated -> PQL rate | Activated users hitting enterprise triggers | Analytics — power users reaching multi-team or advanced usage signals, grouped by company |
| PQL -> sales conversation | PQLs that book real sales call | Partial analytics + manual CRM tracking |
| Same-company emergence | Active companies with >1 user or team invite | Analytics — companies with multiple active users or team invitation events |
7. Team Execution (MANUAL)
| Metric | Definition | Source |
| Experiment cycle time | Days from hypothesis to readout | Manual — from hypothesis tracker |
| Hypothesis-linked roadmap % | Work tied to hypothesis / total work | Manual — from backlog |
| Belief-change rate | Experiments ending in keep/kill/reframe / total | Manual — from weekly reviews |
8. Unit Economics (MANUAL)
| Metric | Definition | Source |
| Net burn / runway | Monthly cash burn; cash / burn | Manual — finance |
| Core workflow contribution margin | (Revenue - variable delivery costs) / revenue | Manual — finance |
| Revenue concentration | Top 3 customers / total ARR | Manual — finance |
9. PMF Detection (MIX)
| Metric | Definition | Source |
| Wedge repeatability score | Last 10 wins matching same ICP/problem/buyer/onboarding / 10 | Manual — assessment from CRM and deal notes |
| Durable revenue ratio | Revenue from accounts past trial + healthy >90 days / total | Partial analytics — inferred from sustained regular/power usage stages |
| Organic pull index | Target-cohort referrals, unsolicited inbound, expansion requests | Partial analytics — sharing/referral events as signals; verify manually |
Analytics Implementation
See your analytics skill (e.g., posthog-analytics) for implementation details and query recipes — funnel event names, attribution model, company grouping, and morning briefing queries.
Vanity Metrics (DO NOT report as progress)
- Total traffic, total signups, total "active users" mixed
- Feature count shipped
- Total pipeline across all segments
- PR hits, likes, followers
- Total MRR without durability/health filters
- B2C DAU that never clusters into accounts
- "AI usage" with no evidence of business outcome
Metrics That Matter LATER (after PMF)
CAC payback, LTV/CAC, quota attainment, forecast accuracy, mature NRR, Rule of 40, hiring efficiency.
Output Format (max 15 lines)
Metrics for [period]:
Learning: X conversations (target 8-12), pain rate Y%
Product: TTFV Xh, activation Z%
Usage: retained W4 X%, second-bite Y%
B2C->B2B: X signups -> Y activated -> Z PQL, same-co X
Pipeline: X opps, win rate Y%
PMF: repeatability [low/growing], organic pull [none/emerging]
Burn: $X/mo, runway Y months
Conclusion: [what this means for the current stage]
Action: [concrete action]Follow output preferences from USER.md (language, format, platform constraints).
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