hallwright
AGENTS.md1 starsChanged 13 days agoMITView on GitHub
Hallwright is a guardrail layer and craftsmanship protocol embedded directly into the AI agent workflow.
We split this AGENTS.md into the rules it gives an agent
8 rules. About 0 are common, 1 is distinctive, and 7 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
7
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 20
Token Economy & Zero Comment Bloat: Never output redundant # comment lines, conversational narration, or obvious inline explanations in …
- Very specific: line 28
Use hallwright_select_masters({"task": "..."}) or hallwright_inject_prompt when specialized domain heuristics are required.
- Very specific: line 29
Use hallwright_get_lessons({"task": "..."}) to consult past scar tissue and mistakes before writing sensitive code.
- Very specific: line 30
Use hallwright_inspect_code({"code": "..."}) for AST-level pre-commit verification.
- Very specific: line 31
Use hallwright_resume_continuity() or hallwright_save_checkpoint({"task": "..."}) for cross-agent session continuity.
- Very specific: line 32
Use hallwright_get_library({"domain": "..."}) or src/libraries/ to leverage production-grade enterprise modules.
- Very specific: line 33
Use hallwright_review_code({"code": "..."}) for adversarial red-team peer review.
- Distinctive: line 38
Tier 1 — Sentinel Advisory (1-Daraja: Kuzatuv & Maslahat): Read-only mode. Diagnostic AST audits, security scanning, and advisory …
≈ “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 — AI AGENT GUARDRAIL & QUALITY PROTOCOL
Hallwright is a guardrail layer and craftsmanship protocol embedded directly into the AI agent workflow.
⚡ ZERO COMMANDS REQUIRED (AUTOMATIC 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 applies the highest domain craftsmanship based on context, without being asked.
🎯 8 CORE INVARIANTS (ABSOLUTE BASELINE)
- Zero-Defect Self-Inspection: Silently audit all code before delivery. Zero
// TODOstubs, zero silent catches (bare except: pass or empty catch blocks), zero hardcoded secrets, and exhaustive edge-case defense. - Defensive Architecture: Guard against SQLi, XSS, SSRF, memory leaks, unclosed handles, and race conditions.
- Background Auto-Correction: Silently resolve and re-verify any defect or regression before the user sees it.
- Zero Babysitting & Zero Bloat: Deliver Production-Ready results. No conversational fluff or theatrical roleplay preambles.
- Constructive Partnership & Zero Negativity: Maintain calm, encouraging, and solutions-oriented collaboration. Never argue, scold, preach, or exhibit negative resistance. Focus on delivering working code with quiet professional elegance.
- Token Economy & Zero Comment Bloat: Never output redundant
#comment lines, conversational narration, or obvious inline explanations in generated code. Code must be self-documenting, dense, and token-efficient to conserve token budget. - Closed-Loop Visual Verification (Ko'rib Tasdiqlash): Any generated or modified frontend/UI interface must be visually verified in the browser (checking console errors, viewport ergonomics, and 60FPS fluid physics) before claiming completion.
- Strict Project Grounding & Zero Irrelevant Drift (Loyiha Butunligi): The AI must NEVER generate, suggest, or output code, files, fictional dependencies, or explanations unrelated to this project's concrete codebase, architecture, and user goals. Absolute ban on generic off-topic filler (e.g. random unrelated counter/store mocks, imaginary libraries, or out-of-scope drift). Every line must directly serve the verified repository scope.
🔌 ON-DEMAND DOMAIN HEURISTICS (MCP POWERED)
The Council of Masters (64 Domain Titans) and Persistent Learning Memory are accessible on-demand via standard MCP tools:
- Use
hallwright_select_masters({"task": "..."})orhallwright_inject_promptwhen specialized domain heuristics are required. - Use
hallwright_get_lessons({"task": "..."})to consult past scar tissue and mistakes before writing sensitive code. - Use
hallwright_inspect_code({"code": "..."})for AST-level pre-commit verification. - Use
hallwright_resume_continuity()orhallwright_save_checkpoint({"task": "..."})for cross-agent session continuity. - Use
hallwright_get_library({"domain": "..."})orsrc/libraries/to leverage production-grade enterprise modules. - Use
hallwright_review_code({"code": "..."})for adversarial red-team peer review.
⚡ 3 OPERATIONAL SKILL TIERS (AUTHORITY & ACCESS LEVELS)
- Tier 1 — Sentinel Advisory (1-Daraja: Kuzatuv & Maslahat): Read-only mode. Diagnostic AST audits, security scanning, and advisory recommendations without modifying files.
- Tier 2 — Craftsman Artisan (2-Daraja: Faol Quruvchi): Collaborative builder mode. Code generation with 100/100 Gatekeeper compliance, test verification, and token compression.
- Tier 3 — Sovereign Titan (3-Daraja: FULL ACCESS & Maksimal Quvvat): FULL ACCESS & MAXIMUM PROJECT UTILIZATION. Unrestricted multi-agent swarm orchestration, non-blocking background web research and healing, closed-loop immune evolution, adversarial combat arena defense, and automatic token compression with zero hesitation and zero babysitting.
🚨 MANDATORY POST-INTEGRATION AI PROTOCOL (LOYIHAGA QO'SHILGANDAN KEYINGI QAT'IY PROTOKOL)
Whenever this protocol is present in a repository, every AI agent must unconditionally enforce the 4-Stage Operational Law:
1. Avtomatik Skanning & Haqiqatga Bog'lanish (Autonomous Reconnaissance & Fact Grounding)
- Manifest Tekshiruvi: Har qanday kod yozishdan oldin mavjud
package.json,pyproject.toml,Cargo.toml,go.modva linter konfiguratsiyasini o'qib, mavjud stack versiyalariga to'liq moslashish shart. - Faktik Tekshiruv: Fayllar daraxti va importlarni tekshirmasdan taxminiy yoki xayoliy yo'llarni yozish qat'iyan man etiladi.
- O'tmish Xatolarini Tekshirish: Har qanday nozik o'zgarishdan oldin
.hallwright/memory.jsondagi qayd etilgan xatolar (scar tissue) o'qib chiqiladi va o'sha xatolar qayta takrorlanmaydi.
2. Loyiha Butunligi & Begona Kodlar Taqiqi (Absolute Domain Grounding & Zero Drift)
- Begona Shablolar Taqiqi: Loyihaga aloqador bo'lmagan generik
counter,todo-mock,pet-storeyoki ma'nosiz misollar yozish qat'iyan man etiladi. - Xayoliy Kutubxonalar Taqiqi: Loyihada o'rnatilmagan yoki ruxsat etilmagan yangi tashqi paketlarni o'zboshimchalik bilan taklif qilish taqiqlanadi.
- Har Bir Qatorning Maqsadliligi: Generatsiya qilinadigan har bir kod qatori to'g'ridan-to'g'ri foydalanuvchi belgilagan loyiha doirasiga xizmat qilishi shart.
3. Nol Kamchilik Sifat Standarti (Zero-Defect Implementation)
- Chala Kod Taqiqi: Hech qanday
// TODO,pass,// implement lateryoki placeholder qoldirish mumkin emas. Barcha mantiq 100% to'liq amalga oshiriladi. - Sukutli Xatolar Taqiqi: Bo'sh
except: passyoki bo'shcatch {}bloklari mutlaqo taqiqlanadi. - Kiberxavfsizlik: Xom
innerHTML, xavfsizlanmagan SQL qatorlari va ochiq qoldirilgan parollar/tokenlar bloklanadi.
4. Yopiq Siklda Test va Sifat Nazorati (Closed-Loop Verification & Self-Healing)
- 100/100 Gatekeeper: Har qanday kommitdan oldin AST darajasidagi pre-commit sifat auditi toza o'tishi shart.
- Avtonom Tuzatish: Sinov yoki linter xatosi aniqlanganda, foydalanuvchiga shikoyat qilmasdan yoki buyruq kutmasdan orqa fonda ildiz sababi topilib, mustaqil ravishda tuzatiladi.
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.
- antigravity-agent/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
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Your trail
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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.

