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Create log entries with timestamps. Use this when documenting discoveries, research findings, meetings, code reviews, articles, videos, or significant events during development.

o3-cloud By o3-cloud schedule Updated 12/31/2025
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name: log description: Create log entries with timestamps. Use this when documenting discoveries, research findings, meetings, code reviews, articles, videos, or significant events during development.

Log Findings Skill

This skill provides instructions for documenting analysis findings, deep dives, research, and code reviews to the docs/log directory in a consistent, discoverable format.

Purpose

The docs/log directory serves as a searchable knowledge base of all graph analysis, infrastructure findings, and architectural decisions. Each log entry is a standalone markdown document that can be referenced, tagged, and discovered over time.

Quick Start

1. Choose Your Filename

Use the pattern: YYYY-MM-DD-descriptive-title.md

Examples:

  • 2025-12-23-scheduled-tasks-analysis.md
  • 2025-12-23-ecs-env-vars-research.md
  • 2025-12-23-kafka-ui-service-analysis.md

Naming conventions:

  • Use ISO date (YYYY-MM-DD) for chronological sorting
  • Use kebab-case for the title portion
  • Be specific and descriptive (avoid generic titles like "findings" or "notes")

2. Add Frontmatter

Every log entry must start with YAML frontmatter. Use the TEMPLATE.md file as reference:

---
summary: A brief (1-2 sentence) summary of what this log entry covers
event_type: deep dive | meeting | research | code | code review
sources:
    - https://example.com/resource-url
    - https://github.com/user/repo/path
    - /path/to/local/file.md
tags:
    - tag1
    - tag2
    - tag3
---

Frontmatter Fields Explained

summary (required)

  • 1-2 sentences describing the log's content
  • Used in directory listings and search
  • Example: "Deep analysis of scheduled task infrastructure - 6 ECS tasks + 1 state machine orchestrating trap/device monitoring"

event_type (required)

  • One of: deep dive, meeting, research, code, code review
  • Classifies the type of work documented
  • deep dive: Technical analysis of a system/component
  • meeting: Outcomes from meetings or discussions
  • research: Investigation into a problem or exploration
  • code: Code implementation or changes
  • code review: Review of code changes and feedback

sources (required - list)

  • URIs pointing to actual resources (websites, APIs, files, repositories)
  • Each entry must be a valid URI: URLs, file paths, repository links, etc.
  • Do not include descriptive text or method descriptions—those belong in the content
  • Examples:
    • https://github.com/markburgess/SSTorytime/tree/main
    • https://aicoding.leaflet.pub/3malrv6poy22a
    • /path/to/local/file.md (for local files)
    • https://www.youtube.com/watch?v=VIDEO_ID (for video)
    • https://example.com/api/endpoint (for APIs)

Important: Speaker names, article titles, and metadata go in the content section (after frontmatter), not in sources. Sources are only URIs.

# ✓ Correct
sources:
    - https://www.youtube.com/watch?v=ShuJ_CN6zr4

# ✗ Incorrect
sources:
    - https://www.youtube.com/watch?v=ShuJ_CN6zr4
    - Speaker: Eno Reyes, CTO at Factory AI

Then in the content:

**Speaker:** Eno Reyes, CTO at Factory AI
**Duration:** 15 minutes
**Source:** AI Engineer Conference

tags (required - list)

  • Technology stack, components, and topics
  • Use lowercase, kebab-case, plural where appropriate
  • Examples:
    • neo4j, cloudwatch, ecs, state-machines
    • scheduled-tasks, automation, monitoring
    • infrastructure, security, performance

3. Write the Content

After the frontmatter, write the main content using markdown.

Recommended Structure

For Deep Dives:

# [Title]

## Overview
- Brief context and scope

## Inventory/Summary
- List of resources/findings

## Detailed Analysis
- Breakdowns, diagrams, dependencies

## Key Observations
- What's important to know

## Recommendations
- Next steps, improvements

## Data Quality Notes
- Gaps, limitations in what was analyzed

## Related Analysis
- Links to other log entries

For Research:

# [Title]

## Question/Goal
- What were we investigating?

## Findings
- Key discoveries

## Implications
- How this affects the system

## Open Questions
- What else needs investigation?

## References
- Sources consulted

4. Include Useful Content

Good log entries include:

  • Tables: Resource inventories, comparisons, metrics
  • Code blocks: Cypher queries, configuration examples, command snippets
  • Diagrams: ASCII art dependency flows, architecture diagrams
  • Lists: Categorized findings, ordered recommendations
  • Data: Metrics, counts, statistics with before/after comparisons

Example tables:

| Module | Purpose | Event Rule | Event Target |
|--------|---------|------------|--------------|
| scheduled-task-amount-lost | Track amounts | event_rule | ecs_scheduled_task |

Example Cypher queries:

MATCH (n:aws_lambda_function)
RETURN n.address, n.arn, n.runtime
ORDER BY n.address

5. Extracting Information from Media Sources

When documenting video content and other media, use tools to extract structured metadata:

YouTube Videos with yt-dlp

Use yt-dlp to extract video metadata without needing to watch the entire video:

# Extract key metadata from a YouTube video
yt-dlp --dump-json "https://www.youtube.com/watch?v=VIDEO_ID" 2>/dev/null | jq '{title, description, uploader, duration}'

Example output:

{
  "title": "Making Codebases Agent Ready – Eno Reyes, Factory AI",
  "description": "Agents are eating software engineering...",
  "uploader": "AI Engineer",
  "duration": 933
}

Use yt-dlp when:

  • Logging conference talks, tutorials, or educational content
  • Extracting speaker information and affiliations
  • Capturing video descriptions with key points or links
  • Duration helps gauge content scope

Template for video log entries:

---
summary: [Brief summary of video content]
event_type: research
sources:
    - https://www.youtube.com/watch?v=VIDEO_ID
    - "Video Title" - Speaker Name
tags:
    - video-content
    - [relevant topics]
---

# [Video Title]

**Speaker:** [Name, Title, Affiliation]
**Duration:** [MM:SS or total minutes]
**Source:** [Conference/Channel Name]

## Key Points
- [Main takeaway 1]
- [Main takeaway 2]
...

6. Reference Other Logs

Link to related analysis:

## Related Analysis

See also:
- `2025-12-23-comprehensive-graph-analysis-and-next-steps.md` - Overall graph state
- `2025-12-23-ecs-env-vars-research.md` - ECS environment variables

Complete Examples

Example 1: Infrastructure Deep Dive

Here's the structure of a well-formatted log entry:

---
summary: Analysis of RabbitMQ infrastructure - queue topology, consumer patterns, and failover configuration
event_type: deep dive
sources:
    - https://github.com/example/terraform/blob/main/modules/rabbitmq.tf
    - https://github.com/example/infrastructure/blob/main/queries/rabbitmq-analysis.cypher
    - /docs/architecture/rabbitmq-design.md
tags:
    - rabbitmq
    - message-queues
    - infrastructure
    - high-availability
---

# RabbitMQ Infrastructure Deep Dive

## Overview
Analysis of the RabbitMQ message broker infrastructure...

## Queue Inventory

| Queue Name | Type | Consumers | Durability |
|-----------|------|-----------|-----------|
| events.live | standard | 3 | durable |

Example 2: Video Content Research

Example of logging conference talk or educational video:

---
summary: Framework for measuring and improving codebase readiness for autonomous AI agents. Covers 8 categories of environment readiness.
event_type: research
sources:
    - https://www.youtube.com/watch?v=ShuJ_CN6zr4
tags:
    - ai-agents
    - video-content
    - codebase-readiness
---

# Making Codebases Agent Ready

**Speaker:** Eno Reyes, CTO at Factory AI
**Duration:** 15.5 minutes
**Source:** AI Engineer Conference

## Core Problem

Autonomous AI agents fail unreliably in production despite strong demo performance. The gap isn't model quality—it's **environment readiness**.

## Agent Readiness Framework

Eight categories determine agent success: style validation, build systems, dev environments, observability, testing, dependencies, documentation, and error handling.

(Continue with findings, implications, and recommendations)

File Organization

All log entries live in: docs/log/

docs/log/
├── TEMPLATE.md                                          # Template for new entries
├── 2025-12-23-scheduled-tasks-analysis.md
├── 2025-12-23-ecs-env-vars-research.md
├── 2025-12-23-comprehensive-graph-analysis-and-next-steps.md
└── ... (more log entries)

Tips for Effective Logging

1. Be Specific

  • Document the exact version, date, and tool versions used
  • Include sample queries and results
  • Provide concrete metrics and counts

2. Think About Discovery

  • Future developers will search by tag, date, or keyword
  • Use consistent terminology across logs
  • Reference related analyses

3. Include Data Quality Notes

  • What's missing from the graph?
  • What properties are NULL?
  • What would improve the analysis?

4. Actionable Recommendations

  • Prioritize (High/Medium/Low)
  • Explain why each recommendation matters
  • Link to implementation if completed

5. Keep It Discoverable

  • Avoid overly long summaries
  • Use clear section headers
  • Include a "Related Analysis" section
  • Tag consistently with previous logs

Common Tags to Use

Technologies:

  • neo4j, terraform, aws, kubernetes, docker, postgresql, redis, rabbitmq, elasticsearch

AWS Services:

  • ecs, lambda, cloudwatch, rds, s3, kms, iam, vpc, sqs, sns, eventbridge

Domains:

  • scheduled-tasks, monitoring, security, networking, databases, queues, caching, infrastructure

Analysis Types:

  • architecture, dependencies, permissions, performance, reliability, data-quality

Related Tools and Skills

  • query-graph: Execute Cypher queries against the Neo4j database
  • yt-dlp: Extract metadata from YouTube videos (title, description, uploader, duration, etc.)
    • Install: pip install yt-dlp
    • Usage: yt-dlp --dump-json "https://www.youtube.com/watch?v=VIDEO_ID" | jq '{title, description, uploader, duration}'
  • See also: Query patterns in SKILL.md for query-graph skill

Troubleshooting

Problem: "My log entry is too long"

  • Solution: Split into multiple focused entries (one per component/domain)
  • Link them together in "Related Analysis"

Problem: "I'm not sure what tags to use"

  • Solution: Check existing log entries in docs/log/ for tag patterns
  • Be consistent with previous entries

Problem: "I have findings from a query but don't know how to structure them"

  • Solution: Use the TEMPLATE.md as a starting point
  • Choose the most relevant frontmatter fields
  • Write in the "deep dive" or "research" style depending on your content

Problem: "yt-dlp fails to fetch YouTube video metadata"

  • Solution: Update yt-dlp: pip install --upgrade yt-dlp
  • Check network connection
  • Some videos may be region-restricted; try the --no-warnings flag to see raw error
  • If video is private/deleted, log what information you have and note unavailability in the entry

Problem: "I want to log a video but can't watch the full content"

  • Solution: Use yt-dlp to extract title, description, and uploader
  • Read the video description (often contains speaker bios, slides links, key timestamps)
  • Include a "Video Summary" section based on extracted metadata
  • Add a note: "Logged from video metadata; full content not reviewed"
Install via CLI
npx skills add https://github.com/o3-cloud/ai-knowledge-graph --skill log
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