Sentry Development Guide for AI Agents

IMPORTANT: AGENTS.md files are the source of truth for AI agent instructions. Always update the relevant AGENTS.md file when adding or modifying agent guidance. Do not add to CLAUDE.md or Cursor rules.

Command Execution Guide

This section contains critical command execution instructions that apply across all Sentry development.

Python Command Execution Requirements

CRITICAL: When running Python commands (pytest, mypy, prek, etc.), you MUST use the virtual environment.

For AI Agents (automated commands)

Use the full relative path to virtualenv executables:

cd /path/to/sentry && .venv/bin/pytest tests/...
cd /path/to/sentry && .venv/bin/python -m mypy ...

Or source the activate script in your command:

cd /path/to/sentry && source .venv/bin/activate && pytest tests/...

Important for AI agents:

• Always use required_permissions: ['all'] when running Python commands to avoid sandbox permission issues
• The .venv/bin/ prefix ensures you're using the correct Python interpreter and dependencies

Backend Development Commands

Setup

# Refreshes dependencies.
# SENTRY_DEVENV_FRONTEND_ONLY=1 skips over migrations which is not needed for pytest. HIGHLY RECOMMENDED.
SENTRY_DEVENV_FRONTEND_ONLY=1 devenv sync

# refresh dependencies, apply migrations
# Only relevant if you want a working development server.
devenv sync

direnv allow    # activate the environment
devservices up  # bring up services

That is all that is required to run pytest.

devservices serve starts the development server.

When the devserver is running, its full console output (all honcho-managed processes — server, taskworker, kafka consumers, webpack/watchers, etc.) is teed to .artifacts/dev.log, ANSI-stripped and gitignored. Agents can't see the devserver terminal, so tail/grep this file to inspect what's happening (startup, reloads, request logs, tracebacks). The file is truncated on each devserver process start (in-process granian reloads keep appending); override its path with SENTRY_DEV_LOG_FILE. Dev-only — this teeing lives in sentry devserver and is not used in production.

Linting

prek is the single entrypoint for all lint, format, and type-checking tools.

Before considering a task complete, run:

cd /path/to/sentry && .venv/bin/prek run -q

prek detects changed files automatically. To run a specific hook:

SENTRY_MYPY_PRE_PUSH=1 .venv/bin/prek run -q mypy --files src/sentry/foo/bar.py --stage pre-push
.venv/bin/prek run -q ruff --files src/sentry/foo/bar.py

If a hook fails, fix the issues, stage changes, then re-run until it passes.

Testing

For backend-scoped changes, always try make test-selective first. It detects which tests are affected by your local diff and runs only those, making the feedback loop much faster. Fall back to pytest when you need to run a specific file or test-selective doesn't cover your case.

# Run a specific test file.
# Do not run pytest by itself; it'll take forever!
.venv/bin/pytest -n3 -svv --reuse-db tests/sentry/api/test_base.py

Database Operations

# Run migrations
sentry django migrate

# Create new migration
sentry django makemigrations

# Update migration after rebase conflict (handles renaming, dependencies, lockfile)
./bin/update-migration <migration_name_or_number> <app_label>
# Example: ./bin/update-migration 0101_workflow_when_condition_group_unique workflow_engine

# Reset database
make reset-db

Frontend Development Commands

Development Setup

# Start the full development server (requires devservices up)
pnpm run dev

# Start only the UI development server with hot reload
# Proxies API requests to production sentry.io
pnpm run dev-ui

Dev server URLs:

• Full devserver: http://dev.getsentry.net:8000
• Frontend-only (dev-ui): https://sentry.dev.getsentry.net:7999/

Typechecking

To typecheck frontend code, run pnpm typecheck script. It checks the whole project and does not accept file paths. DO NOT use tsc directly.

pnpm run typecheck

Linting

# JavaScript/TypeScript linting
pnpm run lint:js

# Linting for specific file(s)
pnpm run lint:js components/avatar.tsx [...other files]

# Fix linting issues
pnpm run fix

Testing

# Run JavaScript tests
pnpm test-ci <file_path>

# Run specific test file(s)
pnpm test-ci components/avatar.spec.tsx

Git worktrees

Each worktree has its own .venv. When you create a new worktree with git worktree add, a post-checkout hook runs devenv sync in the new worktree to setup the dev environment. Otherwise run devenv sync once in the new worktree, then direnv allow to validate and activate the dev environment.

Context-Aware Loading

Use the right AGENTS.md for the area you're working in:

• Backend (src/**/*.py) → src/AGENTS.md (backend patterns)
• Tests (tests/**/*.py, src/**/tests/**/*.py) → tests/AGENTS.md (testing patterns)
• Frontend (static/**/*.{ts,tsx,js,jsx,css,scss}) → static/AGENTS.md (frontend patterns)
• General → This file (AGENTS.md) for Sentry overview and commands

Workflow steering (commit, pre-commit, hybrid cloud, etc.) lives in skills (.agents/skills/). Attach or read the area AGENTS.md when working in that tree. Add or update guidance in the appropriate AGENTS.md or skill—do not duplicate long guidance in editor-specific rule files.

Viewer/Organization Context

• Viewer identity is wired through the app via the ViewerContext contextvar; use sentry.viewer_context.get_viewer_context() instead of explicitly threading org/user identity when the current viewer is in scope.

Agent Skills

Skills under .agents/skills/ should follow the same current-practice conventions as the rest of the repo:

• Prefer diff-first review workflows. When no explicit file or patch is provided, default to the current branch diff.
• Keep skill descriptions aligned with natural user requests like PR review, branch audit, and Warden follow-up.
• If a downstream review harness controls the final response shape, do not hardcode a competing output format in the skill. Specify required evidence instead.

Backend

For backend development patterns, security guidelines, and architecture, see src/AGENTS.md. For backend testing patterns and best practices, see tests/AGENTS.md.

Frontend

For frontend development patterns, design system guidelines, and React testing best practices, see static/AGENTS.md.

Feature Flags (FlagPole)

New features should be gated behind a feature flag.

1. Register the flag in src/sentry/features/temporary.py:

   manager.add("organizations:my-feature", OrganizationFeature, FeatureHandlerStrategy.FLAGPOLE, api_expose=True)
   

   Use api_expose=True if the frontend needs to check the flag. Use ProjectFeature and a projects: prefix for project-scoped flags.

2. Python check:

   if features.has("organizations:my-feature", organization, actor=user):
   

3. Frontend check (requires api_expose=True):

   organization.features.includes('my-feature');
   

4. Tests:

   with self.feature("organizations:my-feature"):
       ...
   

5. Rollout: FlagPole YAML config lives in the sentry-options-automator repo, not here.

See https://develop.sentry.dev/feature-flags/ for full docs.

Customer Information

Never include customer information in pull requests, commits, or code. This covers organization slugs, user emails, account names, internal IDs tied to specific customers, support ticket details, and any other data that identifies a Sentry customer. Use anonymized or synthetic examples (org-slug, user@example.com) in PR descriptions, commit messages, code comments, tests, and fixtures. If a real identifier is needed for debugging, keep it in internal tooling (Slack, tickets, private notes)—not in the public git history.

Pull Requests

Frontend (static/) and backend (src/, tests/) are not atomically deployed. A CI check enforces this.

• If your changes touch both frontend and backend, split them into separate PRs.
• Land the backend PR first when the frontend depends on new API changes.
• Pure test additions alongside src/ changes are fine in one PR.

Awesome Agent Conventions

A curated field guide to the convention files AI agents read, write, and act on.

22 conventions across 11 categories. From common project instruction files to newer agent-web discovery and trust formats.

Agent tools increasingly rely on plain files in a repository or website root: instructions, memory, rules, tool connections, prompt assets, discovery metadata, and protocol hints. The names are easy to mix up, and the adoption levels vary a lot.

This repo keeps the map practical:

• Know what a file is for. Each entry names the convention, usual filename, primary readers, and spec or source.
• Study real examples. Examples are fetched from public repositories by script, with provenance kept at the top of each file.
• Separate practice from proposal. Maturity labels show what is widely used, what is early, and what is still only proposed.

Contents

• Instruction & context · category page
  • 🟢 AGENTS.md
  • 🟢 CLAUDE.md
  • 🟢 Tool-specific instruction files
  • 🟠 OKF (Open Knowledge Format)
• Memory & state · category page
  • 🟢 MEMORY.md
  • 🟢 Memory Bank
• Spec-driven development · category page
  • 🟢 Spec Kit
  • 🟢 Kiro steering files
• Skills & prompt assets · category page
  • 🟢 SKILL.md
  • 🟢 Prompt asset files
  • 🟢 Claude Code commands
  • 🟢 Copilot prompt & instruction files
• Tooling & connections · category page
  • 🟢 MCP server config
• Rules & ignore files · category page
  • 🟢 Rules files
  • 🟢 AI ignore files
• Design · category page
  • 🟢 DESIGN.md
• Web & discoverability · category page
  • 🟢 llms.txt
  • 🟢 pricing.md
• Agent-web trust · category page
  • 🟠 auth.md
  • 🔵 ai.txt
• Identity & protocols · category page
  • 🟠 Agent Cards (A2A)
• Proposed namespace · category page
  • 🔵 The protocols.md namespace

What counts

This list is intentionally narrow. A file belongs here when it is a convention for agent behavior or agent-readable metadata: instructions, memory, skills, rules, tool config, prompt assets, or web-discovery hints.

Any file type can qualify - .md, .txt, .prompty, .json, dotfiles, or a directory pattern. Human-first project docs such as README.md, CONTRIBUTING.md, SECURITY.md, and CHANGELOG.md stay out unless the file has become an agent convention in its own right.

Maturity tiers

The badge is a claim about adoption, not quality. It keeps a proven convention from being presented the same way as a new idea.

Badge	Tier	Meaning
🟢	Adopted	Used in production by multiple tools, projects, or teams.
🟠	Emerging	Published by a real organization, but still early or limited in adoption.
🔵	Proposed	Publicly described, but without clear adoption beyond the proposal.

Instruction & context

Standalone page: categories/instruction-context.md

	Convention	Files	Read by	Spec
🟢	AGENTS.md	AGENTS.md	Most coding agents - OpenAI Codex, Cursor, Jules, Aider, Gemini CLI, Zed, and others	spec ↗
🟢	CLAUDE.md	CLAUDE.md	Claude Code, and tools that read the Claude memory convention	spec ↗
🟢	Tool-specific instruction files	GEMINI.md AGENT.md QWEN.md WARP.md CONVENTIONS.md copilot-instructions.md	Each file is read by its namesake tool - Gemini CLI, Amp, Qwen Code, Warp, Aider, GitHub Copilot - often alongside or as a bridge to AGENTS.md	spec ↗
🟠	OKF (Open Knowledge Format)	.md	Agents over MCP (okfy, openknowledge, superops okf CLIs); Google's knowledge-catalog ingests bundles	spec ↗

• AGENTS.md - A plain-Markdown "README for agents" - build/test commands, conventions, and gotchas an agent needs before touching the code. The most widely adopted cross-tool instruction file.
• CLAUDE.md - Anthropic's memory file for Claude Code - loaded automatically at session start to carry project commands, style rules, and standing instructions across turns.
• Tool-specific instruction files - Per-tool instruction files that predate or coexist with AGENTS.md. Some tools now default to AGENTS.md while keeping legacy filenames alive, so these variants still matter when auditing real repositories.
• OKF (Open Knowledge Format) - A machine-first organizational knowledge base: a version-controlled folder of typed Markdown files (one concept per file) that any agent reads as ground-truth context. Open-sourced by Google Cloud in 2026 as the content layer to MCP's transport.

Memory & state

Standalone page: categories/memory-state.md

	Convention	Files	Read by	Spec
🟢	MEMORY.md	MEMORY.md	Claude Code's auto-memory - the per-project MEMORY.md index it writes and re-reads each session	spec ↗
🟢	Memory Bank	projectbrief.md productContext.md activeContext.md systemPatterns.md techContext.md progress.md	Cline, Roo Code, and Cursor (via the Memory Bank custom-instructions pattern)	spec ↗

• MEMORY.md - A persistent, agent-maintained index of durable facts - written and re-read across sessions so an agent accumulates project memory instead of relearning each time.
• Memory Bank - Cline's structured memory system - a set of Markdown files an agent reads at the start of every task to reconstruct full project context after its session memory resets. The six files shown are Cline's set; tools like Roo Code use an overlapping but different variant.

Spec-driven development

Standalone page: categories/spec-driven-development.md

	Convention	Files	Read by	Spec
🟢	Spec Kit	constitution.md spec.md plan.md tasks.md	GitHub Spec Kit's slash-command agents (Copilot, Claude, Gemini, Cursor, and more)	spec ↗
🟢	Kiro steering files	product.md structure.md tech.md	AWS Kiro (steering files are largely Kiro-specific)	spec ↗

• Spec Kit - GitHub's spec-driven workflow - a constitution plus per-feature spec → plan → tasks files that drive an agent through structured, reviewable implementation.
• Kiro steering files - Kiro's always-on steering docs - product, structure, and tech files that give the agent persistent project context outside of any single spec.

Skills & prompt assets

Standalone page: categories/skills-prompt-assets.md

	Convention	Files	Read by	Spec
🟢	SKILL.md	SKILL.md	Claude Agent Skills, Claude Code, Amp, Agent Skills-compatible tools	spec ↗
🟢	Prompt asset files	.prompty .prompt system_prompt.txt	Prompty tooling, Azure AI / Semantic Kernel, and apps that load externalized prompts	spec ↗
🟢	Claude Code commands	.md	Claude Code - project .claude/commands/ and user ~/.claude/commands/	spec ↗
🟢	Copilot prompt & instruction files	.prompt.md .instructions.md	GitHub Copilot in VS Code / Copilot CLI	spec ↗

• SKILL.md - A self-contained, model-invoked capability file that tells an agent when to load a reusable procedure and how to execute it.
• Prompt asset files - Externalized prompt files - Prompty's YAML-front-mattered .prompty, plain .prompt templates, and system_prompt.txt - that pull the prompt out of source code so it can be versioned and edited on its own. Only .prompty has a formal spec (prompty.ai); .prompt and system_prompt.txt are ad-hoc externalized-prompt filenames.
• Claude Code commands - A Markdown file Claude Code exposes as a /slash-command - a reusable, version-controlled prompt workflow, with optional frontmatter (allowed-tools, model, argument-hint) and $ARGUMENTS and shell placeholders (@file references are a general Claude Code prompt feature, not command-specific). Now converging with Agent Skills, but still widely committed in its own right.
• Copilot prompt & instruction files - Modular, path-scoped Copilot context: *.instructions.md auto-attach to matching files via an applyTo glob, while *.prompt.md are reusable prompts you invoke by name - the granular cousins of a single .github/copilot-instructions.md.

Tooling & connections

Standalone page: categories/tooling-connections.md

	Convention	Files	Read by	Spec
🟢	MCP server config	.mcp.json	Claude Code, Cursor, VS Code / Copilot, and Claude Desktop - every MCP host reads the same mcpServers schema, though the filename and path differ per tool	spec ↗

• MCP server config - A JSON file that tells an agent which Model Context Protocol servers to launch and how (command, args, env) - making a project's tool and data integrations portable, shareable, and version-controlled across every MCP-capable client.

Rules & ignore files

Standalone page: categories/rules-ignore-files.md

	Convention	Files	Read by	Spec
🟢	Rules files	.cursorrules .mdc .clinerules .clinerules/ (pattern) .windsurfrules	Cursor (.cursorrules / .mdc), Cline (.clinerules/ and legacy .clinerules), Windsurf (.windsurfrules)	spec ↗
🟢	AI ignore files	.aiignore .cursorignore .codeiumignore .aiexclude	JetBrains Junie (.aiignore), Cursor (.cursorignore), Codeium/Windsurf (.codeiumignore)	spec ↗

• Rules files - Per-tool rule files that scope agent behavior - older single-file forms (.cursorrules, .clinerules, .windsurfrules) and newer directory-based, glob-scoped forms (.cursor/rules/.mdc, .clinerules/, .windsurf/rules/.md).
• AI ignore files - gitignore-syntax files that fence an AI agent out of paths - secrets, vendored code, generated output - so they're never sent to the model as context.

Design

Standalone page: categories/design.md

	Convention	Files	Read by	Spec
🟢	DESIGN.md	DESIGN.md	Google Stitch natively; and coding agents (e.g. Claude Code) when pointed at it as design context	spec ↗

• DESIGN.md - A structured, machine-readable design specification - tokens, components, and layout intent - that an agent reads to generate or keep UI consistent with an established system. Open-sourced by Google Labs in 2026 as a cross-tool draft spec.

Web & discoverability

Standalone page: categories/web-discoverability.md

	Convention	Files	Read by	Spec
🟢	llms.txt	llms.txt llms-full.txt (pattern)	Docs sites publish it for LLM tools and crawlers - though no major provider has confirmed reading it	spec ↗
🟢	pricing.md	pricing.md	Agents and LLM browsers fetching a clean, parse-able pricing page	spec ↗

• llms.txt - A proposed-turned-widely-published standard: a root-level Markdown file giving LLMs a curated, link-rich map of a site's docs. Published across hundreds of developer-docs sites - though whether the major LLM providers actually read it remains unproven.
• pricing.md - The Markdown twin of a pricing page - same URL with a .md suffix - so an agent gets structured plans and numbers instead of scraping marketing HTML. A concrete, shipping instance of the page.md pattern.

Agent-web trust

Standalone page: categories/agent-web-trust.md

	Convention	Files	Read by	Spec
🟠	auth.md	auth.md	Agents discovering how to authenticate to a service (early adopters)	spec ↗
🔵	ai.txt	ai.txt	AI training/data-mining crawlers that voluntarily honor AI usage preferences; crawler support is not yet reliable	spec ↗

• auth.md - A Markdown file that tells an agent how to authenticate with a service - discovery of auth endpoints and flows. Shipped by WorkOS as a real, working convention, but adoption beyond it is still early.
• ai.txt - A text file declaring machine-readable consent, licensing, or policy preferences for AI training and data-mining. Spawning popularized the deployed root-file pattern, and a 2026 Internet-Draft now proposes a well-known URI; adoption and crawler obedience are still thin, so it stays 🔵.

Identity & protocols

Standalone page: categories/identity-protocols.md

	Convention	Files	Read by	Spec
🟠	Agent Cards (A2A)	agent-card.json agent.json (pattern)	A2A-compatible agents discovering another agent's capabilities	spec ↗

• Agent Cards (A2A) - The Agent2Agent (A2A) capability card - a JSON document at a well-known path advertising an agent's skills, endpoints, and auth so other agents can discover and call it. Now a Linux Foundation project at v1.0; adoption is growing but early.

Proposed namespace

Standalone page: categories/proposed-namespace.md

	Convention	Files	Read by	Spec
🔵	The protocols.md namespace	proof.md	- (no demonstrated readers; aspirational)	spec ↗

• The protocols.md namespace - A single maintainer's pre-registered namespace of ~74 aspirational .md "protocols" (proof.md, signature.md, reputation.md, …) staked as Schelling points for a future agent web. Published concept, no demonstrated adoption - see the page for the audited, honest caveats.

Maintaining examples

Example files are fetched, not invented. The extractor pulls them from public sources, stores them under conventions/<slug>/examples/<source>/<filename>, and adds a line-1 provenance comment. The examples remain under their upstream owners' licenses and terms; see THIRD_PARTY_EXAMPLES.md before reusing them.

To refresh everything:

pip install -r scripts/requirements.txt
python scripts/extract.py          # fetch real files + rebuild each convention's README
python scripts/build_readme.py     # rebuild this README from scripts/targets.json

Re-running is idempotent. A missing target prints a miss and is skipped. Examples are representative samples: any file over 256 KB (for example, a multi-MB llms-full.txt) is truncated with a marker pointing back to the full source. scripts/targets.json remains the source of truth for conventions that have not been migrated yet; the skill-md pilot uses local convention metadata instead. Edit the relevant source and re-run both scripts.

Shortcut targets are available in the Makefile:

make verify          # schema + generated files + example provenance + links
make extract         # refetch public examples and rebuild generated docs
make license-report  # summarize upstream licenses for vendored examples

CI keeps the generated files and links honest. The verify workflow checks that generated docs match catalog metadata and migrated local metadata, and that every spec, example, and instance URL still resolves on each pull request and weekly. Run the same link check locally with python scripts/check_links.py.

Contributing

Read CONTRIBUTING.md. In short: an entry must pass the filter above and carry evidence for its maturity tier. Add sources to scripts/targets.json for non-migrated conventions, run the scripts, and open a PR. The skill-md pilot uses local convention metadata instead. Do not hand-write example files.

Before proposing adjacent standards, check WATCHLIST.md. Project direction lives in ROADMAP.md.

License

The curation, scripts, and original prose in this repository are MIT. Vendored example files remain under their upstream owners' licenses and terms; see THIRD_PARTY_EXAMPLES.md.