Clawlens - OpenClaw Usage Insights

Generate a comprehensive usage insights report by analyzing conversation history.

When to Use

| User Says | Action | |-----------|--------| | "show me my usage report" | Run full report | | "analyze my conversations" | Run full report | | "how am I using Claw" | Run full report | | "clawlens" / "claw lens" | Run full report | | "usage insights" / "usage analysis" | Run full report |

How to Run

Execute the analysis script:

python3 scripts/clawlens.py [OPTIONS]

Options

| Flag | Default | Description | |------|---------|-------------| | --agent-id | main | Agent ID to analyze | | --days | 180 | Analysis time window in days | | --model | auto-detect | LLM model in litellm format (e.g. deepseek/deepseek-chat). If omitted, auto-detected from OpenClaw config. | | --lang | zh | Report language: zh or en | | --format | md | Output format: md (Markdown) or html (self-contained dark-themed HTML) | | --no-cache | false | Ignore cached facet extraction results | | --max-sessions | 2000 | Maximum sessions to process | | --concurrency | 10 | Max parallel LLM calls | | --verbose | false | Print progress to stderr | | -o / --output | stdout | Output file path |

Model Selection (Agent Interaction)

When the user requests a clawlens report without specifying a model, you must ask the user before running:

是否使用 OpenClaw 当前配置的模型来生成报告？如果不使用，请告诉我你想用的模型（litellm 格式，如 deepseek/deepseek-chat）。

User agrees to use OpenClaw model: Run without --model (the script auto-detects from ~/.openclaw/openclaw.json).
User specifies a different model: Run with --model <user-choice>. The user must also set the corresponding API key env var (e.g. DEEPSEEK_API_KEY).

Note: Each user's OpenClaw model configuration may differ — some use API-key-based providers (e.g. openai-completions), others use OAuth-based providers (e.g. anthropic-messages). The script handles both transparently.

Examples

# Auto-detect model from OpenClaw config (simplest)
python3 scripts/clawlens.py --verbose

# Auto-detect, English, last 7 days
python3 scripts/clawlens.py --lang en --days 7

# Manually specify model (DeepSeek)
DEEPSEEK_API_KEY=sk-xxx python3 scripts/clawlens.py --model deepseek/deepseek-chat

# OpenAI, English, last 7 days
OPENAI_API_KEY=sk-xxx python3 scripts/clawlens.py --model openai/gpt-4o --lang en --days 7

# Verbose, save to file
ANTHROPIC_API_KEY=sk-xxx python3 scripts/clawlens.py --model anthropic/claude-sonnet-4-20250514 --verbose -o /tmp/clawlens-report.md

# HTML report (dark-themed, self-contained)
DEEPSEEK_API_KEY=sk-xxx python3 scripts/clawlens.py --model deepseek/deepseek-chat --format html -o /tmp/clawlens-report.html

Output

The script outputs a report to stdout (or to the file specified by -o). Progress messages go to stderr when --verbose is set.

Markdown (--format md, default): Plain Markdown report. Present it directly to the user.
HTML (--format html): Self-contained dark-themed HTML file with glassmorphism styling, animated stat cards, CSS bar charts, and interactive navigation. Opens directly in any browser — no external dependencies. Requires the markdown Python package for Markdown-to-HTML conversion.

The report includes all dimensions: usage overview, task classification, friction analysis, skills ecosystem, autonomous behavior audit, and multi-channel analysis.

Present the output directly to the user. Do not summarize or truncate it.

Model Configuration

--model is optional. If omitted, the model is automatically resolved from OpenClaw configuration:

Reads primary model from ~/.openclaw/openclaw.json (agents.defaults.model.primary, e.g. kimi-code/kimi-for-coding)
Looks up the provider's baseUrl and api type (e.g. openai-completions, anthropic-messages)
Retrieves API key/token from ~/.openclaw/agents/{agentId}/agent/auth-profiles.json
Maps to litellm format automatically (e.g. openai/kimi-for-coding with custom api_base)

If you prefer to specify a model manually, use --model with litellm's provider format:

| Provider | --model value | Required env var | |----------|----------------|------------------| | DeepSeek | deepseek/deepseek-chat | DEEPSEEK_API_KEY | | OpenAI | openai/gpt-4o | OPENAI_API_KEY | | Anthropic | anthropic/claude-sonnet-4-20250514 | ANTHROPIC_API_KEY | | OpenAI-compatible | openai/<model-id> + set OPENAI_API_BASE | OPENAI_API_KEY |

The format is always <provider>/<model-id>. Refer to litellm docs for the full list of supported providers and their env var naming conventions.

Data Source

The script reads conversation data from:

~/.openclaw/agents/{agentId}/sessions/sessions.json (session index)
~/.openclaw/agents/{agentId}/sessions/*.jsonl (per-session logs, including unindexed historical files)
~/.openclaw/skills/ (installed skills directory for ecosystem analysis)

Cache is written to ~/.openclaw/agents/{agentId}/sessions/.clawlens-cache/facets/ to avoid re-analyzing the same sessions.

Privacy Notice

This skill sends conversation transcript data to an external LLM provider (specified by --model) for analysis. Specifically:

Stage 2 (Facet Extraction): Each session's conversation transcript (truncated to ~80K chars) is sent to the LLM to extract structured analysis (task categories, friction points, etc.). Results are cached locally so each session is only sent once.
Stage 4 (Report Generation): Aggregated statistics and session summaries (not raw transcripts) are sent to the LLM to generate the report sections.

API key handling: When --model is omitted, this skill reads openclaw.json and auth-profiles.json to auto-detect the model and retrieve the API key. The API key is used only for LLM calls during report generation and is not stored or transmitted elsewhere. When --model is specified explicitly, the user must provide the API key via environment variables — no OpenClaw config files are accessed for credentials.