EVOLUTION PATTERN
Data-Driven Skill Optimization Convention¶
When to Use
- Mature agents with usage logs
When NOT to Use
- Pre-launch intuition phase
Skill Contract（本模式的模块声明）
版本 v1.0.0 ｜ 分类 conventions ｜ 相关 evolution-gate · anti-patterns · skill-evolution
Problem¶
Skills written in a vacuum drift from reality. The agent follows a well-structured procedure, but the output quality degrades over time because the skill has no feedback loop — it never learns what actually works.
Solution¶
Embed real analytics data directly into the skill file, then use it to constrain every decision the agent makes. The skill becomes a living document that evolves with your account.
How It Works¶
1. Collect Real Data¶
Before writing or updating a skill, pull actual performance metrics from your production environment:
Article Reads Likes Type
LoopEngineering 112 13 Framework
三层记忆架构 109 11 Framework
GraphEngineering 103 15 Framework
PromptEng 76 11 Methodology
工具生态 55 5 ❌ Tool list
2. Derive Hard Constraints¶
From the data, extract three types of constraints:
Positive constraints (things that work — must do): - "XXEngineering" naming → 81 avg reads vs 57 non-series - Framework articles → 108 avg reads
Negative constraints (things that don't work — must not do): - Tool lists → 55 avg reads, never break 100 - Purely introductory content → 9 reads
Tactical constraints (execution rules): - Publish every 2-3 days (matches algorithm boost window) - Minimum 1 architecture diagram per article - End with open question + next-preview teaser
3. Bake Constraints Into the Skill¶
The raw data and derived constraints are placed at the top of the skill file, before any procedural steps. Every time the agent loads the skill, it sees "this is what worked" before it sees "this is what to do."
4. The Loop¶
┌──────────────────────────┐
│ Publish article │
└─────────┬────────────────┘
▼
┌──────────────────────────┐
│ Wait 48h (data window) │
└─────────┬────────────────┘
▼
┌──────────────────────────┐
│ Pull read/like/share #s │
└─────────┬────────────────┘
▼
┌──────────────────────────┐
│ Update constraints │
│ → Add new findings │
│ → Demote outdated ones │
│ → Remove disproven ones │
└─────────┬────────────────┘
▼
┌──────────────────────────┐
│ Next article is smarter │
└──────────────────────────┘
State File¶
Store optimization data in STATE.md under the skill directory:
# STATE.md
last_updated: 2026-07-29
articles_published: 8
current_constraints:
naming: "XXEngineering: subtitle" # mandatory
type: framework # no tool lists
frequency: every 2-3 days
min_diagrams: 1
performance_summary:
engineering_series_avg: 81
non_series_avg: 57
best_topic: agent_framework_comparison
When to Use¶
Use this convention whenever the skill produces content that will be consumed by real users or evaluated by a recommendation algorithm. The list includes:
- Social media/content publishing pipelines
- Email/newsletter drafting skills
- Code generation skills used in production
- Report/dashboard generation skills
Anti-Patterns¶
- Confirmation bias — Don't remove a finding after one bad article. Require 3+ data points.
- Overfitting — Don't optimize for 13-follower patterns if your account grows 10x. Re-check constraints every 20 articles.
- Static data — Data ages. Add a
last_validatedfield and refresh monthly.