Skill: bytesagain3/llm-chain (version 2.0.0)

--- version: "2.0.0" name: Langchain4J description: "LangChain4j is an open-source Java library that simplifies the integration of LLMs into Java applica llm-chain, java, anthropic, chatgpt, chroma, embeddings." --- # LLM Chain An AI toolkit for configuring, benchmarking, comparing, prompting, evaluating, fine-tuning, analyzing, and optimizing LLM workflows. Each command logs timestamped entries to local files with full export, search, and statistics support. ## Commands ### Core AI Operations | Command | Description | |---------|-------------| | `llm-chain configure <input>` | Record a configuration change (or view recent configs with no args) | | `llm-chain benchmark <input>` | Log a benchmark run and its results | | `llm-chain compare <input>` | Record a model or output comparison | | `llm-chain prompt <input>` | Log a prompt template or prompt engineering note | | `llm-chain evaluate <input>` | Record an evaluation result or metric | | `llm-chain fine-tune <input>` | Log a fine-tuning session or parameters | | `llm-chain analyze <input>` | Record an analysis observation | | `llm-chain cost <input>` | Log cost tracking data (tokens, dollars, etc.) | | `llm-chain usage <input>` | Record API usage metrics | | `llm-chain optimize <input>` | Log an optimization attempt and outcome | | `llm-chain test <input>` | Record a test case or test result | | `llm-chain report <input>` | Log a report entry or summary | ### Utility Commands | Command | Description | |---------|-------------| | `llm-chain stats` | Show summary statistics across all log files | | `llm-chain export <fmt>` | Export all data in `json`, `csv`, or `txt` format | | `llm-chain search <term>` | Search all entries for a keyword (case-insensitive) | | `llm-chain recent` | Show the 20 most recent activity log entries | | `llm-chain status` | Health check: version, entry count, disk usage, last activity | | `llm-chain help` | Display full command reference | | `llm-chain version` | Print current version (v2.0.0) | ## How It Works Every core command accepts free-text input. When called with arguments, LLM Chain: 1. Timestamps the entry (`YYYY-MM-DD HH:MM`) 2. Appends it to the command-specific log file (e.g. `benchmark.log`, `cost.log`) 3. Records the action in a central `history.log` 4. Reports the saved entry and running total When called with **no arguments**, each command displays the 20 most recent entries from its log file. ## Data Storage All data is stored locally in plain-text log files: ``` ~/.local/share/llm-chain/ ├── configure.log # Configuration changes ├── benchmark.log # Benchmark results ├── compare.log # Model comparisons ├── prompt.log # Prompt templates & notes ├── evaluate.log # Evaluation metrics ├── fine-tune.log # Fine-tuning sessions ├── analyze.log # Analysis observations ├── cost.log # Cost tracking ├── usage.log # API usage metrics ├── optimize.log # Optimization attempts ├── test.log # Test cases & results ├── report.log # Report entries ├── history.log # Central activity log └── export.{json,csv,txt} # Exported snapshots ``` Each log uses pipe-delimited format: `timestamp|value`. ## Requirements - **Bash** 4.0+ with `set -euo pipefail` - Standard Unix utilities: `wc`, `du`, `grep`, `tail`, `date`, `sed` - No external dependencies — pure bash ## When to Use 1. **Tracking LLM experiments** — log benchmark results, prompt variations, and evaluation scores as you iterate on model configurations 2. **Cost monitoring** — record token usage, API costs, and billing data to keep spending under control across multiple models 3. **Comparing models side-by-side** — use `compare` and `benchmark` to log performance differences between GPT-4, Claude, Gemini, etc. 4. **Fine-tuning documentation** — capture fine-tuning parameters, dataset info, and results for reproducibility 5. **Generating operational reports** — export all logged data to JSON/CSV for dashboards, audits, or stakeholder reviews ## Examples ```bash # Log a configuration change llm-chain configure "switched to gpt-4o, temperature=0.7, max_tokens=2048" # Record a benchmark result llm-chain benchmark "gpt-4o MMLU=87.2% latency=1.3s cost=$0.012/req" # Track a cost entry llm-chain cost "2024-03-18: 142k tokens, $4.26 total (gpt-4o)" # Compare two models llm-chain compare "claude-3.5 vs gpt-4o: claude wins on reasoning, gpt wins on speed" # Log a prompt engineering note llm-chain prompt "added chain-of-thought prefix: 'Let me think step by step...'" # Search all logs for a keyword llm-chain search "gpt-4o" # Export everything to JSON llm-chain export json # Check health and disk usage llm-chain status ``` ## Configuration Set the `DATA_DIR` variable in the script or modify the default path to change storage location. Default: `~/.local/share/llm-chain/` --- Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]


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