---
layout: default
title: Clew taste-1
nav_order: 14
---

# Clew taste-1

**Local-first preference-learning runtime for ClewCode.**

Clew taste-1 learns the user's coding taste and working style from accept/reject/edit/test/lint signals and manual rules. It combines symbolic rules, semantic preference scoring, and contextual bandit optimization to adapt ClewCode's output to the user's preferences.

It does **not** fine-tune the base LLM. All learning is local, online, and preference-based.

## What It Learns

- **Code style**: formatting, naming conventions, preferred patterns
- **Architecture**: module structure, dependency direction, layering
- **Tooling**: preferred build tools, linters, test frameworks
- **Testing**: test style, coverage expectations, mocking patterns
- **Naming**: variable/function/class naming conventions
- **Security**: safe patterns vs unsafe patterns
- **Performance**: efficient algorithms, caching, resource management
- **UI patterns**: component structure, state management, styling approach
- **Workflow**: commit style, review preferences, deployment habits

## What It Does Not Do

- Does not fine-tune or modify the base LLM
- Does not send taste data to remote services (all data stays local)
- Does not automatically block edits without showing a reason
- Does not replace memory, skills, or CLAUDE.md files

## How It Differs from Other Systems

| System | Purpose | Storage | Scope |
|--------|---------|---------|-------|
| **CLAUDE.md** | Project instructions | `.claude/` files | Static project rules |
| **Memory** | Cross-session facts | `memdir/` | Recall |
| **Skills** | Reusable procedures | Plugin/skill system | Task automation |
| **taste-1** | Learned preferences | `.clew/taste1/` | Adaptive preference optimization |

- Rules are explicit user preferences
- Memory is facts about the user and project
- Skills are reusable task procedures
- Taste is learned, weighted preference signals

## Storage Paths

| Data | Path |
|------|------|
| Project profile | `.clew/taste1/profile.json` |
| Project event log | `.clew/taste1/events.jsonl` |
| Project packages | `.clew/taste1/packages/` |
| Global profile | `~/.clew/taste1/profile.json` |
| Global packages | `~/.clew/taste1/packages/` |

## Commands

| Command | Description |
|---------|-------------|
| `/taste1` | Show status panel |
| `/taste1 status` | Print status |
| `/taste1 learn <rule>` | Add a manual rule |
| `/taste1 forget <id>` | Remove a rule by ID |
| `/taste1 profile` | Show full profile with rules |
| `/taste1 events` | Show recent learning events |
| `/taste1 decay` | Apply confidence decay |
| `/taste1 eval` | Run profile self-evaluation |
| `/taste1 export` | Export high-confidence rules |
| `/taste1 import <file>` | Import rules from file |
| `/taste1 on` | Enable taste-1 |
| `/taste1 off` | Disable taste-1 |

## Configuration

Settings in `settings.json`:

```json
{
  "taste1": {
    "enabled": true,
    "autoLearn": true,
    "injectPrompts": true,
    "validateEdits": true,
    "minConfidence": 0.55,
    "maxInjectedRules": 8,
    "decayEnabled": true,
    "banditEnabled": true,
    "neuralScoringEnabled": true
  }
}
```

## Architecture

Clew taste-1 consists of several subsystems:

1. **Signal Collection** — Captures accept, reject, edit, test, lint, and tool signals from
   user interactions and converts them into learning events.

2. **Reward Model** — Maps signal types to numeric reward values (accept = +1.0, reject = -1.0,
   test pass = +0.4, etc.) and computes edit distance rewards dynamically.

3. **Symbolic Engine** — Compiles high-confidence rules into checkable constraints.
   Rules with confidence >= 0.85 can block edit acceptance. Rules below
   threshold warn without blocking. Never blocks silently.

4. **Neural Scorer** — Provider-agnostic lexical similarity scoring (Jaccard/TF-IDF)
   that scores candidate output against active rules. Extensible to use
   embedding APIs when available.

5. **Contextual Bandit** — Epsilon-greedy bandit with 6 strategy arms (minimal,
   strict_style, architecture_first, test_first, safety_first, refactor_heavy).
   Selects optimal strategy based on feedback and context features.

6. **Prompt Injection** — Injects a compact `<clew_taste1>` block into the system
   prompt with relevant rules. Max 8 rules by default, filtered by confidence
   and sorted by relevance.

7. **Decay Engine** — Gradual confidence reduction for unused rules (half-life
   based, default 30 days). Prevents stale preferences from persisting.

8. **Event Log** — Append-only JSONL file storing all raw learning events for
   auditability and regression analysis.

## Continuous RL (Local Online Preference Optimization)

Clew taste-1 implements **local online preference optimization**:

- **Online**: learns continuously from each interaction, no batch training
- **Local**: all computation and storage stays on the user's machine
- **Preference-based**: optimizes for user preferences via reward signals
- **Multi-objective**: balances style, correctness, safety, and efficiency

The learning loop:

1. User interacts with ClewCode (accepts, rejects, edits output)
2. Signal collector captures the interaction as a typed event with reward
3. Bandit arm updates based on reward signal
4. Rule confidence adjusts based on positive/negative evidence
5. Prompt injection adapts to reflect current learned preferences
6. Decay gradually reduces stale rule confidence

This is **not** LLM fine-tuning. The base model's weights never change.
taste-1 adapts the prompt context and edit validation, not the model itself.

## Privacy

- All taste data stays local by default
- Profiles and event logs are stored in `.clew/taste1/` or `~/.clew/taste1/`
- No data is sent to remote services
- Export/import is explicit and user-initiated
- Event logs are append-only for auditability

## Integration Points

| Point | Status | Description |
|-------|--------|-------------|
| Prompt injection | Implemented | System prompt taste block via `TastePromptInjector` |
| Edit validation | No-op stub | Ready for `PreAcceptEdit` hook wiring |
| Accept/reject signals | No-op stub | Ready for tool approval flow wiring |
| Test/lint signals | No-op stub | Ready for `PostToolUse` output analysis |
| Tool result signals | No-op stub | Ready for `PostToolUse` hook wiring |
| Settings config | Implemented | `taste1.*` in `settings.json` |

## Example Usage

```
/taste1 learn Use const instead of let
/taste1 learn Prefer named exports
/taste1 learn Never use any type
/taste1 status
/taste1 profile
/taste1 eval
/taste1 export
```