[None][fix] Honor deterministic mode in PyTorch backend

.claude/README.md

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+# Custom Claude Code Skills & Agents for TensorRT-LLM
+
+## Background: Skills & agents in Claude Code
+
+Claude Code supports two extensibility mechanisms — **skills** and **agents** —
+that let teams encode domain expertise into reusable, version-controlled
+components.
+
+**Skills** are markdown playbooks that Claude follows step-by-step when
+triggered. They are invoked via `/slash-commands` (e.g. `/perf-analysis`) or
+matched automatically from natural-language requests. Each skill lives in its
+own directory under `.claude/skills/` and can bundle reference materials that
+Claude reads during execution. See
+[Custom slash commands](https://code.claude.com/docs/en/skills)
+for details.
+
+**Agents** (sub-agents) are specialist workers that Claude spawns in a separate
+context to handle focused tasks. Each agent has its own system prompt, tool
+access, and domain knowledge. Claude delegates to them when it determines a task
+fits a specialist's scope, while you can also invoke agents directly. Agent
+definitions live under `.claude/agents/`. See
+[Custom sub-agents](https://code.claude.com/docs/en/sub-agents)
+for details.
+
+## How skills and agents are loaded
+
+For users who are working with Claude Code under TensorRT-LLM project directory,
+skills and agents are automatically discovered by Claude Code at startup — no
+manual registration needed. Files placed in `.claude/skills/` and
+`.claude/agents/` are picked up by convention.
+
+To verify what's loaded, launch Claude Code under TensorRT-LLM project directory
+and type `/skills` or `/agents` in the Claude Code prompt to see available
+skills and sub-agents.
+
+## How to use skills and agents
+
+There are two ways to trigger skills and agents:
+
+1. **Automatic dispatch** — just describe what you need in plain language
+   (e.g. "profile this workload", "compile TensorRT-LLM"). Claude Code will
+   match your request to the appropriate skill or delegate to the right
+   sub-agent automatically.
+
+2. **Manual invoke** — type `/<skill-name>` (e.g. `/perf-analysis`,
+   `/trtllm-serve-config-guide`) to explicitly run a skill. For sub-agents, type
+   `@"<agent-name>" (agent)` (e.g. `@"exec-compile-specialist (agent)"`) to
+   delegate a task directly. This is useful when you know exactly which workflow you want.
+
+In most cases, automatic dispatch is sufficient — you don't need to memorize
+skill or agent names. Manual invoke is there for when you want precise control.
+
+References:
+* [Extend Claude with skills](https://code.claude.com/docs/en/skills)
+* [Work with subagents](https://code.claude.com/docs/en/sub-agents#work-with-subagents)
+
+## Naming convention
+
+Every skill and agent name uses the format `<prefix>-<descriptive-name>`.
+The prefix identifies the primary work area; the descriptive part should be
+short and not repeat it.
+
+| Prefix | Domain | Definition |
+|---|---|---|
+| `ad-` | AutoDeploy | Model onboarding, pipeline debugging, and execution for the AutoDeploy backend |
+| `exec-` | Execution infra | Environment setup and job execution (compile, run, container) |
+| `kernel-` | Kernel development | Kernel writing, generation, and kernel-specific transforms |
+| `perf-` | Performance work | Profiling, analysis, and tuning above the kernel layer (kernel modifications belong under `kernel-`) |
+| `trtllm-` | TRT-LLM project workflows | Project-specific workflows: codebase exploration, contribution, dependency upgrades, and serving configuration (static subsystem knowledge belongs in repo docs) |
+
+Guidelines:
+
+* If a skill doesn't fit any prefix, propose a new one and agree on its
+  boundary before using it.
+* Use the prefix of the skill's **primary** domain, even if it orchestrates
+  across multiple domains.
+* Agents follow the same convention.
+* Good: `exec-local-compile`, `kernel-cuda-writing`, `perf-host-analysis`
+* Bad: `exec-trtllm-compile`, `kernel-cuda-kernel-writing`,
+  `perf-trtllm-host-analysis`

.claude/agent-tests/perf-test-sync/build_prompt.py

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+"""Promptfoo prompt builder.
+
+Loads perf-test-sync.md, strips Claude Code specific sections, and appends the
+user request.
+
+Stripped sections:
+  - YAML frontmatter between leading `---` markers
+  - `# Persistent Agent Memory` section and everything after it (Claude Code
+    memory infrastructure, not relevant to prompt-quality evaluation)
+"""
+
+import os
+import re
+
+_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+_AGENT_MD = os.path.normpath(os.path.join(_SCRIPT_DIR, "..", "..", "agents", "perf-test-sync.md"))
+
+
+def _load_agent_body() -> str:
+    with open(_AGENT_MD, "r", encoding="utf-8") as f:
+        text = f.read()
+    text = re.sub(r"\A---\n.*?\n---\n", "", text, count=1, flags=re.DOTALL)
+    text = text.split("# Persistent Agent Memory", 1)[0].rstrip()
+    return text
+
+
+def build(context: dict) -> str:
+    user_prompt = context["vars"]["prompt"]
+    agent_body = _load_agent_body()
+    return f"{agent_body}\n\n## User request\n\n{user_prompt}\n"