Llm Flow

Langflow is a powerful tool for building and deploying AI-powered agents and workflows. llm-flow, python, agents, chatgpt, generative-ai.

Security Scan

The skill's code and runtime instructions match its stated purpose: a simple local bash-based logging/tooling utility for LLM workflows that stores data under the user's home directory; nothing in the package requests unrelated credentials, network access, or elevated privileges.

Purpose & Capability

Name/description (LLM workflow logger) align with included script and SKILL.md. No unexpected binaries, cloud credentials, or unrelated dependencies are requested.

Instruction Scope

SKILL.md and the script confine actions to writing/reading plain-text logs in ~/.local/share/llm-flow and use standard Unix tools. Note: the tool will record whatever text you provide (prompts, costs, API call metadata) in plaintext logs — that can include secrets if you log them.

Install Mechanism

No install spec or network downloads; the package is instruction-only plus a single bash script. Nothing is written outside the user's data dir by default.

Credentials

No environment variables or credentials are required. The script uses HOME and standard utilities only. Caveat: sensitive tokens or API keys placed into entries will be stored locally in plaintext and later exported if you run exports.

Persistence & Privilege

Does not request always:true, does not modify other skills or system configs, and only writes to a per-user data directory. No elevated privileges are requested.

Assessment

This appears coherent and low-risk: it is a local bash tool that logs LLM workflow entries to ~/.local/share/llm-flow. Before installing, review the script (already included) and consider: (1) avoid typing or logging secrets (API keys, passwords, raw transcripts) because entries are stored in plaintext and included in exports; (2) if you prefer a different storage location, set DATA_DIR to a directory with restrictive permissions; (3) validate file permissions (chmod 700 on the data dir) or run in a restricted environment if you have sensitive data; (4) be aware of minor bugs (JSON export formatting may be imperfect) but no network/exfiltration behavior is present. If you need the skill to integrate with external services, expect to supply credentials externally — this package does not request or manage them.

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-flow configure <input>	Record a configuration change (or view recent configs with no args)
llm-flow benchmark <input>	Log a benchmark run and its results
llm-flow compare <input>	Record a model or output comparison
llm-flow prompt <input>	Log a prompt template or prompt engineering note
llm-flow evaluate <input>	Record an evaluation result or metric
llm-flow fine-tune <input>	Log a fine-tuning session or parameters
llm-flow analyze <input>	Record an analysis observation
llm-flow cost <input>	Log cost tracking data (tokens, dollars, etc.)
llm-flow usage <input>	Record API usage metrics
llm-flow optimize <input>	Log an optimization attempt and outcome
llm-flow test <input>	Record a test case or test result
llm-flow report <input>	Log a report entry or summary

Utility Commands

Command	Description
llm-flow stats	Show summary statistics across all log files
llm-flow export <fmt>	Export all data in json, csv, or txt format
llm-flow search <term>	Search all entries for a keyword (case-insensitive)
llm-flow recent	Show the 20 most recent activity log entries
llm-flow status	Health check: version, entry count, disk usage, last activity
llm-flow help	Display full command reference
llm-flow version	Print current version (v2.0.0)

How It Works

Every core command accepts free-text input. When called with arguments, LLM Flow:

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-flow/
├── 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. Building AI agent workflows — log each step of your agent pipeline (configure → prompt → evaluate → optimize) with full traceability
2. Tracking LLM costs and usage — record per-request costs, token counts, and API usage to monitor spending across providers
3. Benchmarking and comparing models — log benchmark metrics side-by-side to make data-driven model selection decisions
4. Fine-tuning experiment tracking — capture hyperparameters, dataset details, and evaluation scores for every fine-tuning run
5. Generating compliance reports — export all logged activity to JSON/CSV for audits, SOC reviews, or stakeholder reporting

Examples

# Configure a new workflow
llm-flow configure "workflow: summarize → classify → respond, model=claude-3.5"

# Benchmark a model
llm-flow benchmark "claude-3.5-sonnet: 94% accuracy, 0.8s p50 latency, $0.003/req"

# Log a prompt template
llm-flow prompt "system: You are a helpful assistant. Always cite sources."

# Track API costs
llm-flow cost "March week 3: 890k tokens in, 210k tokens out, $12.40 total"

# Evaluate output quality
llm-flow evaluate "human eval score: 4.2/5.0 across 50 samples"

# Search across all logs
llm-flow search "claude"

# Export to CSV for analysis
llm-flow export csv

# Quick health check
llm-flow status

Configuration

Set the DATA_DIR variable in the script or modify the default path to change storage location. Default: ~/.local/share/llm-flow/

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