PAIUpgrade

---
name: PAIUpgrade
description: Extract system improvements, monitor Anthropic ecosystem, and mine internal reflections for Algorithm self-improvement. USE WHEN upgrade, check Anthropic, new Claude features, mine reflections, internal improvements, reflection insights, algorithm upgrade, improve the algorithm.
context: fork
---

## Customization

**Before executing, check for user customizations at:**
`~/.opencode/PAI/USER/SKILLCUSTOMIZATIONS/PAIUpgrade/`

If this directory exists, load and apply any PREFERENCES.md, configurations, or resources found there. These override default behavior. If the directory does not exist, proceed with skill defaults.

# PAIUpgrade Skill

Universal system upgrade skill with two modes:
1. **Analysis Mode** - Analyze ANY content to identify system improvement opportunities
2. **Monitoring Mode** - Proactively monitor Anthropic ecosystem and YouTube for updates


## Notification

**When executing a workflow:**


1. **Output text notification**:
   ```
   Running the **WorkflowName** workflow from the **PAIUpgrade** skill...
   ```

## Workflow Routing

Route to the appropriate workflow based on the request.

**When executing a workflow, output this notification directly:**

```
Running the **WorkflowName** workflow from the **PAIUpgrade** skill...
```

| Workflow | Trigger | File |
|----------|---------|------|
| **CheckForUpgrades** | "check for upgrades", "check sources", "any updates", "check Anthropic", "check YouTube" | `Workflows/CheckForUpgrades.md` |
| **ResearchUpgrade** | "research this upgrade", "deep dive on [feature]", "further research" | `Workflows/ResearchUpgrade.md` |
| **ReleaseNotesDeepDive** | "analyze release notes", "deep dive release" | `Workflows/ReleaseNotesDeepDive.md` |
| **FindSources** | "find upgrade sources", "find new sources", "discover channels" | `Workflows/FindSources.md` |
| **MineReflections** | "mine reflections", "check reflections", "what have we learned", "internal improvements", "reflection insights" | `Workflows/MineReflections.md` |
| **AlgorithmUpgrade** | "algorithm upgrade", "upgrade algorithm", "improve the algorithm", "algorithm improvements" | `Workflows/AlgorithmUpgrade.md` |

---

## When to Activate This Skill

### Check for Upgrades Triggers
- "check for upgrades", "check upgrade sources"
- "any new updates", "what's new"
- "check Anthropic", "check YouTube"
- "any new Claude features"

### Research Triggers
- "research this upgrade", "dig deeper on this"
- "further research on [feature]"
- "analyze release notes", "deep dive the latest release"

### Source Discovery Triggers
- "find upgrade sources", "find new sources"
- "discover new channels", "expand monitoring"

### Contextual Triggers
- After reading interesting technical content, articles, or documentation
- When discovering new tools, libraries, or techniques
- During competitive analysis or research into other systems
- After watching technical talks, tutorials, or demonstrations
- When exploring new AI/LLM capabilities or patterns

---

## Part 1: Content Analysis Mode

**Universal Input -> System Upgrade Recommendations**

Takes ANY content type and performs deep thinking analysis to extract insights and identify concrete system infrastructure improvement opportunities.

### Analysis Dimensions

Analyzes content across 10 dimensions:
- **Architectural Patterns** - Could improve the system's structure
- **Tool/Library Innovations** - New integrations to consider
- **Workflow Optimizations** - Better processes and patterns
- **Agent Enhancements** - Improved agent designs or capabilities
- **Performance Techniques** - Speed and efficiency gains
- **UX Improvements** - Better user experience patterns
- **Security Enhancements** - Stronger security approaches
- **Integration Opportunities** - New services or APIs to connect
- **Automation Possibilities** - More automation opportunities
- **Testing Strategies** - Better testing and quality approaches

### Supported Content Types

- URLs (articles, blog posts, documentation, GitHub repos)
- Files (markdown, code, PDFs, transcripts, text)
- YouTube videos (automatic transcript extraction)
- Raw text or code snippets
- Research papers
- Tool documentation

### Output Format

**"No Gaps Found" is a VALID and often CORRECT output.**

If analysis shows the system already implements everything in the content:
- Say "No gaps found - we already do this"
- Briefly note what the content covers and how the system addresses it
- **STOP.** Do not generate recommendations.

**Only if genuine gaps exist**, output prioritized recommendations:
- **HIGH PRIORITY** - High impact, reasonable effort (do this soon)
- **MEDIUM PRIORITY** - Good ideas with more complexity or moderate impact
- **ASPIRATIONAL** - Interesting long-term possibilities

**What is NOT a valid recommendation:**
- "Document what we already do"
- "Formalize existing patterns"
- "Add awareness of features we have"

These are busywork, not upgrades. If the system does it, we don't need to "document" it as an upgrade.

---

## Part 2: Source Monitoring Mode

**Proactive ecosystem monitoring for PAI-relevant updates**

### Anthropic Monitoring (30+ sources)

**Sources Monitored:**
1. **Blogs & News** (4) - Main blog, Alignment, Research, Interpretability
2. **GitHub Repositories** (21+) - claude-code, skills, MCP, SDKs, cookbooks
3. **Changelogs** (5) - Claude Code CHANGELOG, releases, docs notes
4. **Documentation** (6) - Claude docs, API docs, MCP docs, spec, registry
5. **Community** (1) - Discord server

**Tool:** `Tools/Anthropic.ts`

### YouTube Monitoring

YouTube channels are configured via the **Skill Customization Layer**.
See `~/.opencode/PAI/USER/SKILLCUSTOMIZATIONS/PAIUpgrade/` for user-specific channels.

**Features:**
- Detection of new videos via yt-dlp
- Transcript extraction via **VideoTranscript** skill
- State tracking to avoid duplicate processing
- User-customizable channel list

---

## Tool Reference

| Tool | Purpose |
|------|---------|
| `Tools/Anthropic.ts` | Check Anthropic sources for updates |

## Configuration

**Skill Files:**
- `sources.json` - Anthropic sources config (30+ sources)
- `youtube-channels.json` - Base YouTube channels (empty - uses customization)
- `State/last-check.json` - Anthropic state
- `State/youtube-videos.json` - YouTube state

**User Customizations** (`~/.opencode/PAI/USER/SKILLCUSTOMIZATIONS/PAIUpgrade/`):
- `EXTEND.yaml` - Extension manifest
- `youtube-channels.json` - User's personal YouTube channels

Use `bun ~/.opencode/PAI/Tools/LoadSkillConfig.ts` to load configs with customizations merged.

---

## Core Workflow Overview

The skill has six complementary workflows:

| Workflow | Purpose |
|----------|---------|
| **CheckForUpgrades** | Monitor configured sources (Anthropic + YouTube) for new content |
| **ResearchUpgrade** | Deep dive on discovered features to understand implementation |
| **ReleaseNotesDeepDive** | Specialized research on Claude Code release notes |
| **FindSources** | Discover and evaluate new sources to add to monitoring |
| **MineReflections** | Mine internal algorithm reflections for recurring upgrade patterns |
| **AlgorithmUpgrade** | Generate section-targeted Algorithm spec changes from reflection data |

**Typical flow:**
1. Run **CheckForUpgrades** to discover new content
2. Use **ResearchUpgrade** to dig deeper on interesting items
3. Periodically run **MineReflections** to find internal improvement patterns
4. Use **AlgorithmUpgrade** for spec-level self-improvement
5. Use **FindSources** to expand monitoring over time

---

## Advanced Features

### Synergy Detection

Identifies combinations of improvements that multiply value:
- Cross-component synergies
- Cascading benefits from combined implementations
- Enablement chains (implementing X enables Y and Z)

### Trend Tracking

When analyzing multiple pieces of content over time:
- Tracks recurring themes and patterns
- Identifies emerging industry trends
- Spots opportunities before they're obvious
- Builds upgrade momentum around trends

### Gap Analysis

Compares content insights to the system's current capabilities:
- What capabilities do we lack
- What problems others solve that we face
- Future needs to prepare for
- Opportunity cost of not implementing

### Meta-Learning

The skill improves its own recommendations over time:
- Tracks which recommendations get implemented
- Learns what types of improvements are most valuable
- Refines impact/effort estimation accuracy
- Improves component mapping precision

---

## Integration Points

### With Other Skills

**parser:**
- Use for URL and content extraction
- Handles multiple content types automatically

**research:**
- For deep-dive analysis on specific topics
- When upgrade requires additional research before recommendation

**be-creative:**
- For creative application of insights
- When brainstorming unconventional approaches to implementation

**development:**
- When ready to implement recommendations
- For spec-driven development of new features

**VideoTranscript:**
- For YouTube transcript extraction
- Used in YouTube monitoring workflow

### With System Components

**History Capture:**
- Log all upgrade analyses to `~/.opencode/History/research/YYYY-MM/`
- Build searchable archive of improvement ideas
- Track implementation status over time

**Todo System:**
- Can auto-generate todos from HIGH PRIORITY recommendations
- Track upgrade backlog and priorities
- Monitor progress on implementation roadmap

**Agent Delegation:**
- Can delegate research on specific upgrades to research agents
- Can parallelize implementation of multiple improvements with engineer agents

---

## Examples

**Example 1: Check for upgrades**
```
User: "check for upgrades"
→ Invokes CheckForUpgrades workflow
→ Runs Anthropic.ts tool (30+ sources)
→ Checks YouTube channels (from USER config)
→ Combines into prioritized upgrade report
```

**Example 2: Research a discovered feature**
```
User: "research the new context forking feature"
→ Invokes ResearchUpgrade workflow
→ Spawns parallel research agents
→ Searches GitHub, docs, blog for details
→ Maps to PAI architecture opportunities
→ Outputs implementation recommendations
```

**Example 3: Deep dive on release notes**
```
User: "deep dive the latest release notes"
→ Invokes ReleaseNotesDeepDive workflow
→ Runs /release-notes to capture features
→ Launches parallel research agents for each feature
→ Maps to PAI architecture opportunities
→ Outputs prioritized upgrade roadmap with citations
```

**Example 4: Find new sources**
```
User: "find new upgrade sources"
→ Invokes FindSources workflow
→ Searches for relevant YouTube channels
→ Evaluates and ranks findings
→ Outputs recommendations with add instructions
```

**Example 5: Mine internal reflections**
```
User: "mine reflections"
→ Invokes MineReflections workflow
→ Reads algorithm-reflections.jsonl
→ Clusters Q1/Q2/Q3 answers into themes
→ Outputs prioritized upgrade candidates with root causes
```

**Example 6: Propose Algorithm improvements**
```
User: "algorithm upgrade"
→ Invokes AlgorithmUpgrade workflow
→ Spawns agent to classify reflections by Algorithm section
→ Cross-references themes against current spec
→ Generates section-targeted diffs with version bump recommendation
```

---

## Key Principles

1. **Universal Input** - Accept any content type without restriction
2. **Deep Analysis** - Use extended thinking for thorough examination
3. **System-Aware** - Understand current system state and constraints
4. **Action-Oriented** - Every insight maps to concrete next steps
5. **Prioritized** - Clear ranking by impact vs effort
6. **Learning System** - Improve recommendations over time
7. **Synergy-Seeking** - Find combinations that multiply value
8. **Stack-Aligned** - Respect TypeScript > Python, CLI-First, bun > npm
9. **NO GAPS = NO RECOMMENDATIONS** - If the system already does everything in the content, say so and STOP

---

## Output Quality Standards

**Every recommendation must have:**
- Clear value proposition (why this matters)
- Concrete implementation steps (how to do it)
- Realistic effort estimate (based on system context)
- Component mapping (what parts of the system affected)
- Actionable next steps (specific tasks)

**Avoid:**
- Vague suggestions without clear value
- Recommendations without implementation path
- Ignoring stack preferences or constraints
- Aspirational ideas in high priority
- Duplicate existing capabilities without noting enhancement

---

## Workflows

- **CheckForUpgrades.md** - Monitor all configured sources for updates
- **ResearchUpgrade.md** - Deep dive on discovered upgrade opportunities
- **ReleaseNotesDeepDive.md** - Specialized research on release notes
- **FindSources.md** - Discover and evaluate new sources to monitor
- **MineReflections.md** - Mine internal algorithm reflections for recurring patterns
- **AlgorithmUpgrade.md** - Generate section-targeted Algorithm spec improvements

## Internal Improvement Pipeline

The Algorithm writes structured reflections after every Standard+ run to `~/.opencode/MEMORY/LEARNING/REFLECTIONS/algorithm-reflections.jsonl`. These two workflows close the feedback loop:

```
LEARN phase reflections → MineReflections → AlgorithmUpgrade → Algorithm spec improvements
```

**MineReflections** surfaces recurring issues from execution history.
**AlgorithmUpgrade** translates those patterns into concrete, section-targeted changes to the Algorithm spec itself, with proposed diffs and version bump recommendations.

---

**This skill embodies the system's commitment to continuous improvement and learning from the broader ecosystem while maintaining our architectural principles and preferences.**