Skip to main content
ClaudeWave
Skill279 estrellas del repoactualizado 6d ago

bug-fix-brief

The bug-fix-brief skill generates a structured Bug Fix Brief document that captures bug corrections with root cause analysis, reproduction steps, fix options, and implementation checklist. Use this skill when documenting bug fixes in a standardized format across a project's documentation directory.

Instalar en Claude Code
Copiar
git clone --depth 1 https://github.com/giuseppe-trisciuoglio/developer-kit /tmp/bug-fix-brief && cp -r /tmp/bug-fix-brief/plugins/developer-kit-core/skills/bug-fix-brief ~/.claude/skills/bug-fix-brief
Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

SKILL.md

# Bug Fix Brief (BFB)

## Overview

This skill generates a Bug Fix Brief (BFB): a structured document in `docs/bfb/` that uniformly captures every bug fix with root cause, repro steps, fix options, and checklist.

## When to Use

- User asks to create a BFB
- User wants to document a bug fix in a structured way
- After identifying the root cause of a bug and before implementing the fix

**Trigger:** "create BFB", "document bug", "bug fix brief", "document fix"

## Instructions

### Phase 1: Gather Information

Check existing numbering:
```bash
ls docs/bfb/ 2>/dev/null || echo "Directory does not exist"
```

Ask the user for:
- BFB number (or propose next sequential)
- Concise title (3-5 words, kebab-case)
- Issue link (e.g. #1287)
- Environment (Prod/Stg/Dev) + version
- Observed vs expected behavior
- File/function/line of the cause

### Phase 2: Generate Template

Complete the full BFB template:

```markdown
## BFB-XXX: [Title]

**Reference:** [Issue link]
**Environment:** [Env] `vX.Y.Z`
**Date:** YYYY-MM-DD

---

### 1. Bug
- **Observed:** [wrong behavior]
- **Expected:** [correct behavior]

### 2. Repro
```
1. ...
2. ...
→ [error/output]
```

### 3. Cause
`path/file.ext` — `function()` @ line N
[Why it happens, max 3 lines]

### 4. Decision
| Option | Fix | Choice |
|--------|-----|--------|
| A | [desc] | ✅/❌ |
| B | [desc] | ✅/❌ |

**Rationale:** [why]

### 5. Fix
- [ ] [change 1]
- [ ] [test]
- [ ] [verify repro]

### 6. Notes
[recurring patterns, links, warnings]
```

### Phase 3: Ask Confirmation

Show the generated BFB and ask with AskUserQuestion:
- "Create the BFB"
- "Edit before creating"
- "Cancel"

### Phase 4: Write to Disk

Only after approval:
```bash
mkdir -p docs/bfb
```

Write to `docs/bfb/BFB-XXX-title.md`

## Examples

**Input:** "create BFB for login email null crash"

**Final output:**
```markdown
## BFB-042: Login crash with null email

**Reference:** #1287
**Environment:** Prod `v2.4.1`
**Date:** 2026-05-02

---

### 1. Bug
- **Observed:** App crashes if email field is empty
- **Expected:** Error message "Email required"

### 2. Repro
```
1. Open login screen
2. Tap "Login" without entering email
→ NullPointerException @ AuthManager.kt:34
```

### 3. Cause
`AuthManager.kt` — `validateEmail()` @ line 34
Missing null check on email.trim()

### 4. Decision
| Option | Fix | Choice |
|--------|-----|--------|
| A | Add safe call `?.` | ✅ |
| B | Refactor with Result type | ❌ |

**Rationale:** Option A is minimal, zero impact.

### 5. Fix
- [ ] Add `email?.trim()?.isNotEmpty() == true`
- [ ] Test `validateEmail_null_returnsFalse()`
- [ ] Verify repro

### 6. Notes
- Check other forms for missing null checks
```

## Best Practices

1. **Sequential numbering**: BFB-001, BFB-002, no gaps
2. **Concise title**: 3-5 words, kebab-case in filename
3. **Root cause**: exact file, function, line
4. **2+ fix options**: with pros/cons and rationale
5. **Verifiable checklist**: each item must be testable

## Constraints and Warnings

- **Confirmation required**: Always ask before writing
- **Max 3 lines for cause**: Stay concise
- **Directory `docs/bfb/`**: Create if it does not exist
chunking-strategySkill

Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.

prompt-engineeringSkill

>

ragSkill

Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.

aws-cloudformation-auto-scalingSkill

Provides AWS CloudFormation patterns for Auto Scaling including EC2, ECS, and Lambda. Use when creating Auto Scaling groups, launch configurations, launch templates, scaling policies, lifecycle hooks, and predictive scaling. Covers template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and best practices for high availability and cost optimization.

aws-cloudformation-bedrockSkill

Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.

aws-cloudformation-cloudfrontSkill

Provides AWS CloudFormation patterns for CloudFront distributions, origins (ALB, S3, Lambda@Edge, VPC Origins), CacheBehaviors, Functions, SecurityHeaders, parameters, Outputs and cross-stack references. Use when creating CloudFront distributions with CloudFormation, configuring multiple origins, implementing caching strategies, managing custom domains with ACM, configuring WAF, and optimizing performance.

aws-cloudformation-cloudwatchSkill

Provides AWS CloudFormation patterns for CloudWatch monitoring, metrics, alarms, dashboards, logs, and observability. Use when creating CloudWatch metrics, alarms, dashboards, log groups, log subscriptions, anomaly detection, synthesized canaries, Application Signals, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and CloudWatch best practices for monitoring production infrastructure.

aws-cloudformation-dynamodbSkill

Provides AWS CloudFormation patterns for DynamoDB tables, GSIs, LSIs, auto-scaling, and streams. Use when creating DynamoDB tables with CloudFormation, configuring primary keys, local/global secondary indexes, capacity modes (on-demand/provisioned), point-in-time recovery, encryption, TTL, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references.