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Agent-native analytics, error logging, and deploy attribution for AI coding agents.

Install

mkdir -p .claude/skills/agentry && curl -L -o skill.zip "https://mcp.directory/api/skills/download/9558" && unzip -o skill.zip -d .claude/skills/agentry && rm skill.zip

Installs to .claude/skills/agentry

About this skill

Agentry - agent-native analytics and logging

Agentry is agent-native analytics and logging for coding agents. It covers product analytics, error logging, and deploy attribution through one HTTP API so agents can answer what users did, what broke, and what changed.

Use this skill for Agentry install/setup, product analytics, logging, error monitoring, deploy tracking, production debugging, and day-to-day Agentry reads. Do not treat this file as an API reference; it is a compact entry point to the live docs.

What to fetch next

Use a custom User-Agent header for non-browser direct HTTP calls; default clients such as Python-urllib can be blocked. Browser fetch uses the browser's own User-Agent and cannot set this header manually.

Install workflow

When installing Agentry, fetch https://agentry.sh/install.md and follow that flow. Start from the repo's important business question and funnel/business logic flow. Let POST /v1/install/plan derive the events, properties, error surfaces, and deploy proof from the actual codebase. Do not hand-write a generic event catalog.

Before claiming success, save the signal map and implementation report, then verify that the approved telemetry is flowing.

Mental model

Agentry has three write paths and three read concepts.

Write app runtime data with AGENTRY_DSN:

  • POST /v1/logs/ for errors, exceptions, and operational failures.
  • POST /v1/analytics/ for product, user, funnel, and business events.

Write deploy attribution with the same AGENTRY_DSN only from CI/provider post-deploy automation after a successful release:

  • POST /v1/deploys/ for release attribution.

Read with AGENTRY_API_KEY:

  • Cases: what broke. Start with GET /v1/projects/:project_id/cases, then GET /v1/cases/:case_id.
  • Analytics: what users did. Start from the saved signal map, latest verify report, answer contracts, event names, and property keys; then use query blueprints for common reads or POST /v1/projects/:project_id/analytics/query for custom HogQL. Table-like Agentry query reads return object-shaped rows; do not read PostHog-style results from agent-facing endpoints.
  • Deploys: what changed. Use GET /v1/projects/:project_id/deploys.

Query blueprints, event names, public queries, health, and next-steps are helpers around those three read concepts. Public-query URLs and dashboards are optional output surfaces, not proof that the underlying product question is answerable.

For day-to-day questions, read the saved signal map, latest verify report, answer contracts, event names, and property keys before choosing a query blueprint or custom HogQL. If the needed event/property is missing, say what is missing and wire or trigger that product flow instead of inventing a metric.

Auth

Read AGENTRY_API_KEY from env or ~/.agentry/credentials.json; read project_id from AGENTRY_PROJECT_ID or committed .agentry/config.json. With no API key, start the device flow with POST /v1/auth/device. Runtime ingest uses AGENTRY_DSN.

Source of truth

Do not rely on this installed file for endpoint details. Fetch the live docs above; they are authoritative and can update independently of adapters.

Public distribution

Adapter coverage

The same skill and adapter manifest support Codex, Claude Code, Cursor, VS Code and Visual Studio with GitHub Copilot, GitHub Copilot coding agent, Devin Desktop/Windsurf Cascade, Cline, Roo Code, Continue, Zed, Gemini CLI, Aider, OpenCode, ChatGPT custom GPT Actions, generic MCP clients, and AGENTS.md-aware agents. The AGENTS.md adapter also covers compatible agents such as Jules, Factory, Amp, goose, Warp, JetBrains Junie, Augment Code, Kilo Code, Phoenix, Semgrep, Ona, and UiPath coded agents.

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