home / skills / amnadtaowsoam / cerebraskills / summarization-rules-evidence-first

summarization-rules-evidence-first skill

/65-context-token-optimization/summarization-rules-evidence-first

This skill generates concise evidence-first summaries by presenting data before conclusions to boost credibility and readability.

npx playbooks add skill amnadtaowsoam/cerebraskills --skill summarization-rules-evidence-first

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---
name: Summarization Rules (Evidence First)
description: Evidence-first summarization rules - start with evidence/data before conclusions, reducing tokens and increasing credibility
---

# Summarization Rules (Evidence First)

## Overview

Summarization rules that prioritize evidence over conclusions - show important data first, then summarize. This reduces token usage and increases credibility.

## Why This Matters

- **Credibility**: Evidence first = more credible
- **Efficiency**: Cut long explanations
- **Actionable**: See data clearly
- **Verifiable**: Can be checked

---

## Traditional vs Evidence-First

### ❌ Traditional (Conclusion First)
```
"The system is experiencing performance issues due to several factors
including high database load, inefficient queries, and memory leaks.
Based on our analysis, we recommend the following improvements..."

(150 tokens, vague)
```

### ✅ Evidence-First
```
Performance Issues:
- DB CPU: 95% (normal: <70%)
- Slow queries: 15 (>1s each)
- Memory: 8GB → 12GB in 2 hours

Fix: Add indexes, optimize queries, fix leak in auth.ts:45

(40 tokens, specific)
```

**Savings: 73%**

---

## Core Rules

### Rule 1: Data Before Interpretation
```
❌ "The API is slow"
✅ "API response time: 2.5s (SLA: <500ms)"

❌ "Many users affected"
✅ "1,247 users (12% of active users)"

❌ "Database is overloaded"
✅ "DB connections: 95/100, CPU: 87%"
```

### Rule 2: Numbers Over Adjectives
```
❌ "Very slow"
✅ "2.5s (5x slower than baseline)"

❌ "High error rate"
✅ "Error rate: 5.2% (normal: 0.1%)"

❌ "Significant improvement"
✅ "Response time: 2s → 500ms (75% faster)"
```

### Rule 3: Specific Over General
```
❌ "Several files changed"
✅ "3 files: auth.ts, db.ts, config.ts"

❌ "Recent issues"
✅ "Issues in last 24h: 12 (P0: 2, P1: 10)"

❌ "Multiple errors"
✅ "Errors: 'Invalid token' (80%), 'Timeout' (20%)"
```

---

## Templates

### Bug Report
```
## Bug: [Title]

Evidence:
- Error: [exact message]
- Frequency: [X times in Y period]
- Affected: [N users/requests]
- Location: [file:line]

Impact:
- [Metric]: [before] → [after]

Fix:
- [Specific action]
```

### Performance Issue
```
## Performance: [Area]

Metrics:
- [Metric 1]: [value] (baseline: [value])
- [Metric 2]: [value] (SLA: [value])
- [Metric 3]: [value]

Root cause:
- [Specific issue in file:line]

Solution:
- [Action with expected improvement]
```

### Code Review
```
## Review: [PR Title]

Changes:
- Added: [list]
- Modified: [list]
- Removed: [list]

Issues:
1. [File:line]: [Specific issue]
2. [File:line]: [Specific issue]

Recommendation:
- [Action]
```

---

## Examples

### Incident Report

#### ❌ Traditional (200 tokens)
```
We experienced a significant outage today that affected many of our users.
The root cause appears to be related to database performance issues that
cascaded into application timeouts. Our team investigated and found that
the problem started around 2pm and was resolved by 4pm. We recommend
implementing better monitoring and adding database indexes to prevent
this from happening again.
```

#### ✅ Evidence-First (60 tokens)
```
Outage: 2:00pm - 4:00pm (2 hours)

Impact:
- Users affected: 15,234 (45% of active)
- Failed requests: 89,456
- Revenue loss: ~$12,000

Root cause:
- DB query timeout (auth.ts:156)
- Missing index on users.email

Fix applied:
- Added index (query: 2.5s → 50ms)
- Deployed 3:45pm

Prevention:
- Add query monitoring
- Index audit weekly
```

**Savings: 70%**

---

### Status Update

#### ❌ Traditional (120 tokens)
```
Good progress this week. The team has been working hard on several features
and we've made significant headway. There are still some challenges we're
working through, but overall things are moving in the right direction.
We expect to complete most of the planned work by the end of the sprint.
```

#### ✅ Evidence-First (35 tokens)
```
Sprint Progress (Week 2/2):

Completed: 8/10 stories (80%)
- Feature A: ✓ deployed
- Feature B: ✓ in review
- Feature C: 🔄 in progress (80%)

Blocked: 2 stories
- Story D: waiting for API access
- Story E: design feedback needed

On track for Friday delivery
```

**Savings: 71%**

---

## Formatting Rules

### Use Tables for Comparisons
```
❌ Prose:
"Before the optimization, the API took 2 seconds. After optimization,
it now takes 500ms. This is a 75% improvement."

✅ Table:
| Metric | Before | After | Change |
|--------|--------|-------|--------|
| Response time | 2s | 500ms | -75% |
```

### Use Lists for Multiple Items
```
❌ Prose:
"The changes include adding authentication, updating the database schema,
and fixing the email service."

✅ List:
Changes:
- Added authentication
- Updated DB schema
- Fixed email service
```

### Use Code Blocks for Specifics
```
❌ Prose:
"The error occurs in the authentication file at line 45"

✅ Code reference:
Error location:
```typescript
// auth.ts:45
const user = await findUser(email);  // ← Throws if email null
```
```

---

## Measurement

### Before Evidence-First
```
Average summary: 150 tokens
Clarity score: 6/10
Action items: vague
```

### After Evidence-First
```
Average summary: 45 tokens
Clarity score: 9/10
Action items: specific
Savings: 70%
```

---

## Quick Checklist

```
Before writing summary:
☐ Do I have specific numbers?
☐ Can I show data instead of describing it?
☐ Are my action items specific?
☐ Can I use a table/list instead of prose?
☐ Did I remove adjectives and use metrics?

If any answer is "no", revise.
```

---

## Anti-Patterns

### ❌ Burying the Lede
```
"After careful analysis and consideration of various factors,
we have determined that..."

✅ Start with the finding:
"Root cause: Missing index on users.email"
```

### ❌ Vague Quantifiers
```
"Many users", "Several issues", "Significant impact"

✅ Specific numbers:
"1,247 users", "3 issues", "45% slower"
```

### ❌ Opinion Without Evidence
```
"The code is poorly written"

✅ Specific issues:
"Issues:
- No error handling (auth.ts:45)
- N+1 query (users.service.ts:120)
- Memory leak (cache.ts:78)"
```

---

## Summary

**Evidence-First:** Show data first, summarize later

**Rules:**
1. Data before interpretation
2. Numbers over adjectives
3. Specific over general

**Format:**
- Tables for comparisons
- Lists for multiple items
- Code blocks for specifics

**Template:**
```
Evidence:
- [Metric]: [value]
- [Metric]: [value]

Impact:
- [Specific impact]

Action:
- [Specific action]
```

**Savings: 70-75% tokens**

**Benefits:**
- More credible
- More actionable
- More verifiable
- Less tokens

Overview

This skill provides evidence-first summarization rules that present data and metrics before conclusions to boost credibility and reduce token use. It gives concrete templates, formatting guidance, and examples for bug reports, incidents, performance issues, and status updates. The result is shorter, verifiable, and more actionable summaries.

How this skill works

The skill inspects narrative content and restructures it to lead with precise evidence: metrics, timestamps, file locations, and counts. It replaces vague adjectives with numbers, swaps general statements for specific items, and applies templates that favor lists, tables, and code references. Outputs are optimized for brevity, clarity, and verifiability.

When to use it

  • Incident reports that require rapid triage and clear root-cause evidence
  • Bug reports where exact error messages, frequency, and location matter
  • Performance summaries and postmortems needing metrics and before/after comparisons
  • Status updates for teams and stakeholders who need concise progress indicators
  • Code reviews with specific file:line issues and actionable recommendations

Best practices

  • Always start with evidence: metric, timestamp, count, or error string
  • Prefer numbers over adjectives (e.g., '2.5s' not 'very slow')
  • Be specific: include file:line, exact message, and affected scope
  • Use lists or tables for multiple items and code blocks for exact snippets
  • Keep action items precise and tied to expected metric improvements

Example use cases

  • Turn a long outage narrative into a compact incident summary with impact, root cause, fix, and prevention steps
  • Convert vague performance notes into a metrics-first brief showing before/after and recommended fixes
  • Generate a bug report template populated with error, frequency, affected users, and exact location
  • Produce sprint status bullets that show completed work, blocked items, and percent complete
  • Create code review notes listing added/modified/removed files and file:line issues

FAQ

Will evidence-first always reduce token count?

Generally yes; replacing prose with concise metrics and lists typically cuts tokens by ~70%, though results vary by input.

What if I don’t have precise numbers?

Flag missing data in the evidence section (e.g., 'Metric unavailable: add monitoring') and prioritize collecting exact metrics before finalizing the summary.