Use Agent Pulse to inspect local AI-agent activity across Hermes, Claude Code, Codex, DeepSeek, OpenClaw, Copilot, Aider, Qwen, OpenCode, Goose, Cursor, Antigravity, and Amp logs. Use when the user asks about AI-agent sessions, tokens, tool/search calls, model usage, estimated cost, budgets, forecas

Agent Pulse Purpose Use the installed agent pulse CLI as the source of truth for local AI agent activity. The PyPI package is agentpulse cli , while the command remains agent pulse . Prefer running commands and summarizing their output over reading the Agent Pulse source code. Always enable UTF 8 on Windows before running commands because Agent Pulse output contains emoji and box drawing: If agent pulse is not on PATH, ask before installing dependencies. If the user approves, install the PyPI package or try running from a local project checkout: Source Keys Use P/ platform when the user asks about one agent tool instead of all local data: Choose Commands Use this command selection table first: User wants Run Current status agent pulse status json Full dashboard agent pulse json or agent pulse no banner Demo data agent pulse demo json Setup diagnosis agent pulse doctor json Recent sessions agent pulse json hours 24 limit 20 Top sessions agent pulse top sort tokens json Top expensive sessions agent pulse top sort cost json hours 168 Model cost analysis agent pulse models json Model ranking agent pulse leaderboard json rank by efficiency Cost savings agent pulse optimize json Budget status agent pulse budget json Cost forecast agent pulse forecast json Cost anomaly check agent pulse anomaly json Health/CI check agent pulse health json Composite score agent pulse score json Search sessions agent pulse search "<query " json Compare periods agent pulse compare json Compare projects agent pulse compare projects json Activity calendar agent pulse heatmap json Smart recommendations agent pulse insights json Prometheus metrics agent pulse metrics format prometheus Export report agent pulse export f markdown or agent pulse export html Web dashboard agent pulse web port 8765 REST API agent pulse api port 8766 MCP tools agent pulse mcp list tools If the installed command lacks an option, run agent pulse <command help and adapt. Workflow 1. Start with agent pulse doctor json only when the user asks why data is missing, asks for setup help, or a normal data command returns no sessions. 2. Use JSON output whenever possible. Summarize the fields that matter: sessions, tokens, tools, search calls, model breakdown, source breakdown, estimated cost, warnings. 3. Use time filters for scoped questions. Default to 24 hours for "recent" and 168 hours for "this week": 4. Use platform filters when the user asks about a specific agent system: 5. For cost questions, pair summary, model, and top session views: 6. For trend and risk questions, use forecast/history/compare/anomaly: 7. For setup, use the discovery commands before guessing paths: Interpreting Results Treat total cost usd as an estimate based on Agent Pulse's local model pricing table. Report both cost and token volume; low cost models can still have very high token usage. Distinguish sources such as codex , claude , hermes , deepseek , openclaw , aider , cursor , opencode , and goose . Mention if doctor reports missing optional sources, missing dev root , or optional web dependencies. If no sessions appear, check doctor , then try a wider time window such as hours 168 . Check whether the user asked for a source ( P ) filter, a model filter, or a project comparison before giving overall totals. If a command emits plain text instead of JSON or fails because an installed version is older, run agent pulse <command help and use the closest supported option. Reports For a short human readable answer, run JSON commands and summarize. For artifacts, prefer: Do not invent exact savings or costs. Use the CLI output. Integrations Use the web and API extras only when the user asks for a browser dashboard or programmatic server. Ask before installing missing extras: For monitoring pipelines: MCP Use MCP mode when the user wants other AI clients to query Agent Pulse: When explaining MCP, mention that it exposes tools such as status, forecast, top sessions, model analytics, optimization, health, search, and leaderboard. Local Helper This skill includes scripts/run agent pulse snapshot.py , which runs a compact set of JSON friendly Agent Pulse checks and prints a combined summary:

agent-pulse