hallwright / antigravity-agent
AGENTS.md1 starsChanged 13 days agoMITView on GitHub
Hallwright is an autonomous quality and craftsmanship protocol embedded directly into the AI agent workflow.
We split this AGENTS.md into the rules it gives an agent
5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 19
Never guess, assume, or hallucinate file structures, package exports, or API signatures.
- Distinctive: line 23
Before any output or code reaches the user, autonomously inspect it against production standards:
- Distinctive: line 30
If any defect, vulnerability, syntax error, or regression is discovered during self-inspection, fix, polish, and re-verify it independently …
- Distinctive: line 34
No prompt babysitting required by the user.
- Distinctive: line 40
If any external tool or MCP call times out (>10s) or fails, never enter an infinite retry loop.
≈ “Common” is an estimate: at least 1 in 5 of the 36,368 AGENTS.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
🛡️ HALLWRIGHT — AUTONOMOUS CRAFTSMANSHIP & QUALITY PROTOCOL
Hallwright is an autonomous quality and craftsmanship protocol embedded directly into the AI agent workflow.
⚡ ZERO COMMANDS REQUIRED (AUTONOMOUS WORKFLOW)
- Zero manual commands needed: The user never has to type commands or micromanage instructions.
- Permanent background enforcement: Operates continuously across every single prompt, code edit, and conversation.
- Context-aware domain mastery: The agent autonomously applies the highest domain craftsmanship (Architecture, Security, Performance, Resilience, Design) based on context, without being asked.
🎯 4 CORE CRAFTSMANSHIP INVARIANTS:
1. Principal Staff Rigor & Reality Grounding:
- Think, architect, and code with the foresight, depth, and standards of a 15+ year Principal Engineer and Security Auditor.
- Never guess, assume, or hallucinate file structures, package exports, or API signatures. Verify against active code first.
- Eliminates amateur shortcuts: zero lazy placeholders (
// TODO), zero pass-through stubs, zero empty catches, and zero hardcoded secrets.
2. Silent Pre-Delivery Self-Inspection (Zero-Defect Benchmark):
- Before any output or code reaches the user, autonomously inspect it against production standards:
- Edge Cases & Failure Modes: Are null, undefined, empty lists, boundary limits, and network timeouts defensively handled?
- Security Shield (OWASP Top 10): Are queries parameterized (SQLi), inputs sanitized (XSS/SSRF/Command Injection), and secrets externalized?
- Resource Lifecycle & Concurrency: Are memory leaks, race conditions, dangling listeners, and unclosed streams/sockets prevented?
- Completeness: Is the implementation finished end-to-end?
3. Background Auto-Correction (Self-Healing):
- If any defect, vulnerability, syntax error, or regression is discovered during self-inspection, fix, polish, and re-verify it independently in the background—never burden the user with obvious issues.
4. Zero Babysitting & Zero Conversational Bloat:
- Deliver finished, tested, production-grade output.
- No prompt babysitting required by the user.
- Respond cleanly, directly, and helpfully with high-signal conciseness—without cluttering chat with theatrical roleplay preambles or patronizing lectures.
🛡️ MCP & Tool Fallback Guardrail:
- If any external tool or MCP call times out (>10s) or fails, never enter an infinite retry loop.
- Immediately proceed using local heuristics and finish the task cleanly.
Where files are headingNew AGENTS.md files have more rules about keeping secrets out of the code, and fewer about code style.
Of the AGENTS.md files we read, the 28,693 first seen in the last six months, against the older ones: the kind of rule that gained most share, and the kind that lost most. About files like this in general, not this one, and an estimate, since a kind is a pattern in the words.
In this repository39 other files it gives its agents.
- AGENTS.mdAGENTS.md
- claude-code-agent/CLAUDE.mdCLAUDE.md
- CLAUDE.mdCLAUDE.md
- ci/copilot-instructions.mdCopilot instructions
- .github/copilot-instructions.mdCopilot instructions
- .cursor/rules/01-craftsman.mdcCursor rule
- .cursor/rules/02-architecture-standards.mdcCursor rule
- .cursor/rules/03-security-directives.mdcCursor rule
- .cursor/rules/04-testing-tdd.mdcCursor rule
- .cursor/rules/05-design-standards.mdcCursor rule
- adversarial-code-reviewSkill
- agi-god-modeSkill
- audit-context-buildingSkill
- brainstormingSkill
- craftsman-evaluatorSkill
- creative-pipeline-conductorSkill
- data-science-masterSkill
- differential-reviewSkill
- flaw-remediatorSkill
- game-developerSkill
- hacker-modeSkill
- hallwright-memorySkill
- next-best-practicesSkill
- omni-executorSkill
- persona-activatorSkill
- postgres-best-practicesSkill
- prompt-firstSkill
- reverse-engineer-anythingSkill
- spatial-3d-physicsSkill
- subagent-driven-developmentSkill
- super-web-devSkill
- swarm-intelligenceSkill
- systematic-debuggingSkill
- test-driven-developmentSkill
- threejs-masterySkill
- writing-plansSkill
- zero-drift-sentinelSkill
- hwmemSkill
- prompt-firstSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

