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GitHub - dvcoolarun/taste-ai: taste - Zero-config session-taste packer for AI agents
dvcoolarun · 2026-06-19 · via Hacker News - Newest: "AI"

taste - tongue logo

Zero-config. Auto-learns. Just works.

License: MIT Bash 4.0+ Platform Works with 3 agents

97% smaller context · Auto-learns your style · Works with any agent
Compresses AI agent context from 56K tokens to 1.9K tokens. Learns your coding patterns from git history and session logs. Results vary by project size and session history.


You know the problem. You start an AI agent session. It reads your entire project: session logs, git diffs, READMEs, config files. It writes code that doesn't match your style. It wastes tokens and produces generic, bloated code.

taste puts a stop to that. It learns your patterns. It compresses your context. It makes your agents write code like you do.

Before / after

You ask for a rate limiter. Your agent reads 56K tokens of context, installs a library, writes a generic implementation, and asks about your Redis setup.

With taste:

taste
# Creates .session-doc.md with 1.9K tokens
# Agent reads your patterns, writes code your way

More examples in examples/.

Numbers

Five metrics, one goal: make your agents write better code with less context.

Metric Without taste With taste Improvement
Token usage 56,000 1,950 97% reduction
Context quality Generic Project-specific Better
Pattern learning Manual Automatic Zero-config
Agent support Single Multiple 3+ agents
Setup time Hours Seconds Instant

97% smaller context, auto-learns your style, and works with any agent. Every pattern taste learns is marked in the code with confidence scores. Reproduce it yourself: run taste learn in any project. Method and raw numbers: benchmarks/. Real-world examples: examples/.

That is the byproduct, not the pitch. These are average numbers, and they vary by project. Larger projects with more session history see better compression. Smaller projects with less history see smaller savings. And all of this is iterative: each time you run taste learn, it learns more patterns, which makes the next compression better. The rule was never "fewest tokens." It is: learn only what the project needs, and never skip validation, error handling, security, or accessibility. The context ends up small because it is necessary, not trimmed, and that is the part that stays useful. Better code quality is a side effect of learning your style, and that is the part that matters.

How it works

Before compressing context, taste learns your patterns:

1. Collect session data    → git diffs, session logs, taste config
2. Create summary          → compact format, essential information only
3. Analyze with agent      → calls opencode or claude
4. Extract patterns        → naming, architecture, imports, error handling, style
5. Update taste config     → append to TASTE.md, update .agent-taste.json

Lazy, not negligent: validation, error handling, security, and accessibility patterns are never skipped.

Install

The most effort taste will ever ask of you:

One-Line Install (Recommended)

curl -fsSL https://raw.githubusercontent.com/dvcoolarun/taste-ai/main/install.sh | bash

Manual Install

git clone https://github.com/dvcoolarun/taste-ai.git
cd taste-ai
chmod +x taste
cp taste ~/.local/bin/

Verify Installation

That was it. It would be proud. It won't say it.

Usage

Pack Context

# In any project directory
taste

# Generates .session-doc.md with compressed context

Learn Patterns

# Analyze last 3 sessions and learn patterns
taste learn

# Analyze last 5 sessions
taste learn --depth 5

# Show patterns without updating files
taste learn --dry-run

Initialize Taste Config

# Create default .agent-taste.json
taste init

Show Current Config

# Display current taste configuration
taste show

Commands

Command What it does
taste Pack session context into .session-doc.md
taste pack [file] Pack to specific output file
taste init Create default .agent-taste.json
taste show Show current taste config
taste learn Learn patterns from recent sessions (agent-assisted)
taste help Show help

Pattern Learning

taste learn analyzes your coding sessions and extracts patterns:

┌─────────────────────────────────────────────────────────┐
│              Data Collection                            │
│  - Last 3-5 session logs                               │
│  - Last 3-5 prompt logs                                │
│  - Git diffs (last 3-5 commits)                        │
│  - Current taste config                                │
└─────────────────────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────┐
│              Summary Creation                           │
│  - Compact format (18KB typical)                       │
│  - Token-efficient structure                           │
│  - Essential information only                          │
└─────────────────────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────┐
│              Agent Analysis                             │
│  - Calls opencode or claude                            │
│  - Uses pattern extraction prompt                      │
│  - Returns structured patterns                         │
└─────────────────────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────┐
│              Pattern Extraction                         │
│  - NAMING conventions → TASTE.md                       │
│  - ARCHITECTURE patterns → TASTE.md                    │
│  - IMPORTS style → TASTE.md                            │
│  - ERROR_HANDLING patterns → TASTE.md                  │
│  - STYLE preferences → TASTE.md                        │
│  - BANNED_PATTERNS → .agent-taste.json                 │
└─────────────────────────────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────┐
│              Auto-Update                                │
│  - Positive patterns → TASTE.md                        │
│  - Banned patterns → .agent-taste.json                 │
│  - Preserve existing patterns                          │
│  - Avoid duplicates                                    │
└─────────────────────────────────────────────────────────┘

Pattern Categories

Category What It Learns Output Location
NAMING Function naming conventions (snake_case, camelCase, etc.) TASTE.md
ARCHITECTURE Project structure patterns, dependency management TASTE.md
IMPORTS Import style, ordering, lazy vs eager imports TASTE.md
ERROR_HANDLING Try/catch patterns, error propagation TASTE.md
STYLE Code formatting, function length, comments TASTE.md
BANNED_PATTERNS What NOT to do, with reasons .agent-taste.json

Banned Patterns

taste learns both positive patterns (what to do) and negative patterns (what NOT to do). Banned patterns are extracted from user corrections, past mistakes, and feedback.

Example banned patterns:

{
  "banned_patterns": [
    "--single-process_Chromium_flag_on_macOS (reason: causes crashes, documented failure)",
    "hardcoding_connection_URLs_or_env_specific_values (reason: caused 'Queue service unavailable' failure)",
    "jumping_to_implementation_before_design_alignment (reason: wasted work when pricing model wasn't confirmed)",
    "removing_comments_during_code_rewrites (reason: user explicitly called out and expects preservation)",
    "using_browser_only_Node_APIs_in_subprocess (reason: ErrorEvent caused ReferenceError)"
  ]
}

Why banned patterns matter:

  • Specific - Not generic ("don't use classes") but concrete ("don't use --single_process_Chrome_flag")
  • Actionable - Clear reasons that explain WHY it's banned
  • Learned from mistakes - "was replaced with page.setContent" shows historical context
  • Platform-aware - "crashes on macOS" shows environment-specific knowledge

How banned patterns work:

  1. Agent extracts BANNED_PATTERNS from session data
  2. Patterns are written to .agent-taste.json as a JSON array
  3. When you run taste, banned patterns are included in .session-doc.md
  4. Agents read the banned patterns and avoid those patterns

Confidence Scores

Each pattern includes a confidence score (0-1):

  • 0.9-1.0: Very high confidence (seen multiple times)
  • 0.8-0.9: High confidence (seen consistently)
  • 0.7-0.8: Medium confidence (seen occasionally)
  • 0.6-0.7: Low confidence (seen once or twice)
  • <0.6: Not included (insufficient evidence)

Iterative Learning

Each time you run taste learn, it:

  1. Reads your latest session logs and git diffs
  2. Analyzes patterns with an AI agent
  3. Extracts structured patterns with confidence scores
  4. Updates your taste config (TASTE.md, .agent-taste.json)
  5. Preserves existing patterns and avoids duplicates

The more you use taste, the better it learns your style. Pattern confidence increases as it sees the same patterns across multiple sessions.

Integration

With opencode

# Before starting opencode
taste

# Feed to opencode
opencode .

With Claude Code

# Before starting claude
taste

# Feed to claude
claude .

With Any Agent

# Generate context
taste

# Agent reads .session-doc.md automatically

Example Output

taste Output

# TASTE BOUNDARIES
Source: `.agent-taste.json`
```json
{
  "flavor": "Functional TypeScript, strict types, zero dependencies",
  "banned_patterns": ["classes", "any", "console.log"],
  "style": "Implicit returns, max 20 lines per function"
}

RECENT WORK

Branch: main

Last 5 commits:
abc1234 refactor: extract auth to /core
def5678 feat: add token validation

Changed files (last commit):
src/auth.ts | 12 +++---
src/utils.ts | 5 +++-

taste: Analyzing last 3 sessions... taste: Collecting session data... taste: Summary created: 18818 bytes taste: Calling opencode for analysis...

LEARNED PATTERNS (last 3 sessions):

NAMING:

  • functions_describe_action_verbs (confidence: 0.9)
  • classes_use_PascalCase_Prefixed (confidence: 0.9)
  • variables_underscore_separated_snake_case (confidence: 0.8)

ARCHITECTURE:

  • Python_FastAPI_fronts_with_Node_subprocess_backend_via_stdin_stdout_bridge
  • async_job_queue_with_redis_backend_and_RQ_worker
  • dual_storage_PDF_disk_and_Redis_cache

IMPORTS:

  • lazy_import_inside_endpoint_to_avoid_side_effects
  • from_stdlib_then_third_party_then_local_grouped
  • explicit_imports_not_star_imports_used

ERROR_HANDLING:

  • log_then_raise_precise_HTTPException_with_detail
  • check_rate_limit_before_database_operation
  • refund_credits_by_saving_values_before_session_closes

STYLE:

  • short_direct_corrections_fix_agent_behavior_precisely
  • comment_preservation_expected_across_rewrites

BANNED_PATTERNS:

  • --single-process_Chromium_flag_on_macOS (reason: causes crashes, documented failure)
  • hardcoding_connection_URLs_or_env_specific_values (reason: caused 'Queue service unavailable' failure)
  • jumping_to_implementation_before_design_alignment (reason: wasted work when pricing model wasn't confirmed)
  • removing_comments_during_code_rewrites (reason: user explicitly called out and expects preservation)
  • using_browser_only_Node_APIs_in_subprocess (reason: ErrorEvent caused ReferenceError)

Updated: TASTE.md, .agent-taste.json


## Auto-Capture

taste learn automatically captures your current session if no recent session files exist:

```bash
# If docs/session-*.md doesn't exist or is older than 2 hours
taste learn

# It will:
# 1. Capture git history + diffs
# 2. Capture current taste config
# 3. Create docs/session-[YYYY-MM-DD-HHMM].md
# 4. Create prompts/prompt-[YYYY-MM-DD-HHMM].md
# 5. Analyze patterns and update taste config

What it captures:

  • Git history (last 3 commits + diffs)
  • Current taste config (.agent-taste.json or TASTE.md)
  • Last 3-5 session log summaries

What it does NOT capture:

  • Terminal histories
  • Agent session logs
  • Full file contents

Token Optimization

Before taste:

Raw context:
- Session logs: ~40,000 words
- Git diffs: ~10,000 words
- README.md: ~1,000 words
- Session notes: ~500 words
Total: ~51,000 words (~66,000 tokens)

After taste:

Compressed context:
- Taste config: ~120 words
- Git summary: ~200 words
- Session notes: ~200 words
- Agent config: ~500 words
Total: ~1,500 words (~1,950 tokens)

Savings:

  • Words: 51,000 → 1,500 (97% reduction)
  • Tokens: 66,000 → 1,950 (97% reduction)

Configuration

Global Config

Create ~/.config/taste/base.json for global settings:

{
  "flavor": "Standard idiomatic development",
  "banned_patterns": [],
  "style": "Prefer clarity over brevity"
}

Project Config

Create .agent-taste.json in your project root:

{
  "flavor": "Functional TypeScript, strict types, zero dependencies",
  "banned_patterns": [
    "classes",
    "any",
    "console.log"
  ],
  "style": "Implicit returns, max 20 lines per function"
}

After running taste learn, banned patterns are automatically populated:

{
  "flavor": "Standard idiomatic development",
  "banned_patterns": [
    "--single-process_Chromium_flag_on_macOS (reason: causes crashes, documented failure)",
    "hardcoding_connection_URLs_or_env_specific_values (reason: caused 'Queue service unavailable' failure)",
    "jumping_to_implementation_before_design_alignment (reason: wasted work when pricing model wasn't confirmed)",
    "removing_comments_during_code_rewrites (reason: user explicitly called out and expects preservation)",
    "using_browser_only_Node_APIs_in_subprocess (reason: ErrorEvent caused ReferenceError)"
  ],
  "style": "Prefer clarity over brevity",
  "learned": {}
}

FAQ

Does it need a config file? No. An optional .agent-taste.json or TASTE.md can be created, but nothing is required. taste works with zero configuration.

What if I really need that 120-line cache class? You don't. Insist anyway and taste will learn your pattern. Slowly. Correctly. While looking at you.

Does it scale? The context you never waste scales infinitely. Zero tokens wasted, zero generic code, 100% style matching since forever.

Why "taste"? You know exactly why.

Requirements

  • bash 4.0+
  • git
  • opencode or claude (for taste learn)

Future Features

  • Multi-agent support (claude, codex, commandcode)
  • Session auto-capture (daemon mode)
  • Global taste config (~/.config/taste/)
  • JSON output for agents
  • Integration with more agent harnesses

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

License

MIT. The shortest license that works.