Llm Chain

LangChain4j is an open-source Java library that simplifies the integration of LLMs into Java applica llm-chain, java, anthropic, chatgpt, chroma, embeddings.

Security Scan

The skill's files and instructions match its stated purpose (a local bash-based LLM experiment logging tool) and it does not request credentials or perform network operations in the visible code, but the provided script was partially truncated in the listing so there's residual uncertainty.

Purpose & Capability

Name/description describe a local LLM experiment logging toolkit; the included script implements commands to append and read plain-text logs in ~/.local/share/llm-chain and the SKILL.md documents that behavior. No unrelated clouds, credentials, or binaries are requested.

Instruction Scope

SKILL.md instructs only local logging, exporting, searching and status checks. It suggests editing DATA_DIR in the script to change storage. Risk: logs are plain text and the tool will happily store whatever the user supplies (including API keys, request/response data, billing info) — user should avoid logging secrets. The JSON export also prints unescaped values, which could break format or leak structured content.

Install Mechanism

No install spec; this is instruction-only with an included bash script. That is low-risk compared with downloading remote binaries. The script is written to the user's data dir only and does not fetch remote code in the visible portion.

Credentials

The skill requires no environment variables or credentials and only uses $HOME for a data directory. This is proportionate. However, because it logs arbitrary text, it may end up containing sensitive environment values or credentials if the user stores them there.

Persistence & Privilege

always:false, no system-wide changes or modifications to other skills are requested. The script creates/reads files under ~/.local/share/llm-chain which is normal for a local CLI tool and not privileged.

Assessment

This skill appears to be a simple local logging tool and is coherent with its description. Before installing/running: (1) review the full script (the provided listing was truncated) to confirm there are no hidden network calls or uploads; (2) do not log secrets, API keys, or raw API responses into these plain-text logs; (3) consider setting DATA_DIR to a dedicated directory with appropriate filesystem permissions; (4) if you export JSON for sharing, be aware values are not escaped and may contain sensitive content. If you want higher assurance, ask for the complete untruncated script to be shown and inspected for any network or exfiltration behavior.

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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