菜单
This skill should be used when the user asks to "write a post", "check my voice", "look up contact", "prepare for meeting", "weekly review", "track goals", or mentions personal brand, content creation, network management, or voice consistency.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management.
This skill should be used when the user asks to "fine-tune on books", "create SFT dataset", "train style model", "extract ePub text", or mentions style transfer, LoRA training, book segmentation, or author voice replication.
Production-grade techniques for evaluating LLM outputs using LLMs as judges. Use when implementing LLM-as-judge, comparing model outputs, creating evaluation rubrics, mitigating evaluation bias, or building automated quality assessment pipelines.
Transforms external RDF context into agent mental states (beliefs, desires, intentions) using formal BDI ontology patterns. Use when modeling agent mental states, implementing BDI architecture, transforming RDF to beliefs, or building cognitive agents with neuro-symbolic AI integration.
Strategies for compressing context when agent sessions exceed limits. Use when compressing context, summarizing conversation history, implementing compaction, reducing token usage, or optimizing tokens-per-task for long-running agent sessions.
Predictable degradation patterns as context length increases, including lost-in-middle phenomena and context poisoning. Use when diagnosing context problems, fixing lost-in-middle issues, debugging agent failures, or understanding attention patterns and context confusion.
| name | digital-brain-skill |
| description | This skill should be used when the user asks to "write a post", "check my voice", "look up contact", "prepare for meeting", "weekly review", "track goals", or mentions personal brand, content creation, network management, or voice consistency. |
A structured personal operating system for managing digital presence, knowledge, relationships, and goals with AI assistance. Designed for founders building in public, content creators growing their audience, and tech-savvy professionals seeking AI-assisted personal management.
Important: This skill uses progressive disclosure. Module-specific instructions are in each subdirectory's .md file. Only load what's needed for the current task.
Activate this skill when the user:
Trigger phrases: "write a post", "my voice", "content ideas", "who is [name]", "prepare for meeting", "weekly review", "save this", "my goals"
The Digital Brain follows a three-level loading pattern:
| Level | When Loaded | Content |
|---|---|---|
| L1: Metadata | Always | This SKILL.md overview |
| L2: Module Instructions | On-demand | [module]/[MODULE].md files |
| L3: Data Files | As-needed | .jsonl, .yaml, .md data |
Formats chosen for optimal agent parsing:
.jsonl): Append-only logs - ideas, posts, contacts, interactions.yaml): Structured configs - goals, values, circles.md): Narrative content - voice, brand, calendar, todos.xml): Complex prompts - content generation templatesJSONL files are append-only. Never delete entries:
"status": "archived" instead of deletingdigital-brain/
├── identity/ → Voice, brand, values (READ FIRST for content)
├── content/ → Ideas, drafts, posts, calendar
├── knowledge/ → Bookmarks, research, learning
├── network/ → Contacts, interactions, intros
├── operations/ → Todos, goals, meetings, metrics
└── agents/ → Automation scripts
Always read identity/voice.md before generating any content.
Contains:
voice.md - Tone, style, vocabulary, patternsbrand.md - Positioning, audience, content pillarsvalues.yaml - Core beliefs and principlesbio-variants.md - Platform-specific biosprompts/ - Reusable generation templatesPipeline: ideas.jsonl → drafts/ → posts.jsonl
ideas.jsonldrafts/ using templates/posts.jsonl with metricscalendar.mdPersonal CRM with relationship tiers:
inner - Weekly touchpointsactive - Bi-weekly touchpointsnetwork - Monthly touchpointsdormant - Quarterly reactivation checksProductivity system with priority levels:
1. Read identity/voice.md (REQUIRED)
2. Check identity/brand.md for topic alignment
3. Reference content/posts.jsonl for successful patterns
4. Use content/templates/ as starting structure
5. Draft matching voice attributes
6. Log to posts.jsonl after publishing
1. Look up contact: network/contacts.jsonl
2. Get history: network/interactions.jsonl
3. Check pending: operations/todos.md
4. Generate brief with context
1. Run: python agents/scripts/weekly_review.py
2. Review metrics in operations/metrics.jsonl
3. Check stale contacts: agents/scripts/stale_contacts.py
4. Update goals progress in operations/goals.yaml
5. Plan next week in content/calendar.md
Input: "Help me write a post about AI agents"
Process:
identity/voice.md → Extract voice attributesidentity/brand.md → Confirm "ai_agents" is a content pillarcontent/posts.jsonl → Find similar successful postscontent/ideas.jsonl if not publishing immediatelyOutput: Post draft in user's authentic voice with platform-appropriate format.
Input: "Prepare me for my call with Sarah Chen"
Process:
network/contacts.jsonl for "Sarah Chen"network/interactions.jsonloperations/todos.md for pending items with SarahOutput: Pre-meeting brief with relationship context.
identity/voice.md before any content generationupdated field when modifying tracked datainteractions.jsonlposts.jsonl informs future performanceThis skill integrates context engineering principles:
agents/scripts/ follow tool design principlesInternal references:
External resources:
Created: 2024-12-29 Last Updated: 2024-12-29 Author: Murat Can Koylan Version: 1.0.0