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// Skill profile

LLM Chain

version: "2.0.0"

by bytesagain3 · published 2026-03-22

数据处理API集成加密货币
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Last updated
2026-03
// Install command
$ claw add gh:bytesagain3/bytesagain3-llm-chain
View on GitHub
// Full documentation

---

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

    # 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/`

    ---

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