菜单
This skill should be used for personal operating-system workflows: content creation, voice consistency, relationship lookup, meeting preparation, weekly review, goal tracking, personal brand management, and network management.
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
| name | digital-brain |
| description | This skill should be used for personal operating-system workflows: content creation, voice consistency, relationship lookup, meeting preparation, weekly review, goal tracking, personal brand management, and network management. |
| version | 1.0.0 |
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
A comprehensive collection of Agent Skills for context engineering, harness engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, evaluating, or debugging agent systems that require effective context management and reliable operating loops.
This skill should be used when writing, enhancing, or evaluating the launch prompt for a long-running autonomous agent or a parallel multi-agent orchestration attacking a hard problem: pseudo-formal task briefs that define terms and an exact success predicate linguistically, enumerate non-counting outcomes, set persistence rules with explicit stop and return conditions and effort floors, manage a diverse portfolio of parallel approaches with an approach registry and blocked-route bookkeeping, and gate the return on adversarial audit. Route agent topology and coordination protocols to multi-agent-patterns, runtime control surfaces and loop governance to harness-engineering, evaluator and quality-gate construction to evaluation, judge design to advanced-evaluation, and compaction or memory mechanics to context-compression and memory-systems.
This skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI) loops, meta-harnesses, self-improving harnesses that mine their own failures and propose bounded edits, evolutionary or population-based search over agent scaffolds, acceptance gates for self-modifying systems, and agentic context evolution where the mechanism that produces context is versioned and evolved. Route governance of a single autonomous loop (locked surfaces, durable logs, rollback, novelty gates, approval boundaries) to harness-engineering, measurement and quality-gate design to evaluation, judge design to advanced-evaluation, and remote sandbox infrastructure to hosted-agents.
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Ensure thorough validation, error recovery, and transparent reasoning in research tasks with multiple tool calls
Debug and optimize AI agents by analyzing reasoning traces, context degradation, tool confusion, instruction drift, repeated task failures, and performance regressions.