Borrowing it
Nothing to install: this file belongs to DevonPeroutky/agent-profiler. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/DevonPeroutky/agent-profiler/main/CLAUDE.mdgit clone --depth 1 https://github.com/DevonPeroutky/agent-profilerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/devonperoutky/agent-profiler/claude-md)<a href="https://agentmods.dev/instructions/devonperoutky/agent-profiler/claude-md"><img src="https://agentmods.dev/badge/instructions/devonperoutky/agent-profiler/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/devonperoutky/agent-profiler/claude-md"><img src="https://agentmods.dev/badge/instructions/devonperoutky/agent-profiler/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.04101 | $0.04101 |
| Opus 5.5 | $0.01640 | $0.01640 |
| Sonnet 5.5 | $0.00820 | $0.00820 |
| Haiku 4.5 | $0.00410 | $0.00410 |
Grade A, and why
agent-profiler CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agent-profiler
A local trace viewer for Claude Code conversations. Reads session transcripts directly from disk and renders them as a span-tree waterfall. A React + Vite app with a small middleware plugin — no separate server process, no hooks, no OTEL SDK.
Communication style
Speak simply and explain things clearly. Avoid jargon — when a domain term is genuinely necessary (e.g. requestId, tool_use), define it the first time it appears in a response. Prefer plain-English descriptions over acronyms and library-internal naming. If a concept can be explained with a short sentence instead of a technical label, use the sentence.
North Star
Claude Code's own per-session JSONL transcripts (~/.claude/projects/<project-slug>/<sessionId>.jsonl) are the source of truth. The viewer is a pure (transcript → SpanNode tree → JSON) pipeline. If the UI and the raw transcript disagree about what happened, the transcript is right.
Core vocabulary — inference vs. tool call
These two are not the same thing. Confusing them is the largest recurring source of bugs in this transformer.
Inference. A single request to the remote Anthropic API. Something that costs money, takes wall-clock time, and ends with the model returning a response. Has a requestId — one per API round-trip. Whatever content the model returned (thinking, text, tool_use, or any combination) belongs to that one inference and shares its requestId. The state layer mirrors API round-trips 1:1: one inference span per distinct requestId within a slice, regardless of what content kinds the response contained. A response with [thinking, text, tool_use] is one inference, not three. How that inference is rendered (waterfall bar, chat bubble, both, neither) is a UI decision driven by has_* flags and the events list — the state layer is content-kind-agnostic.
The canonical predicate for "an assistant record that counts" is isCanonicalAssistantRecord (type === 'assistant', !isApiErrorMessage, has message.usage, has non-empty requestId). It gates both the inference emitter and token dedupe (dedupeUsagesByRequestId in lib/traces/traces.js) — inferences.length equals agent_trace.turn.request_count by construction. Drift here recreates the count discrepancy on any turn with API errors.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 178 lines · 4,101 tokens per session scan A c63f71f30084
agent-profiler CLAUDE.md is an instructions file published in the GitHub repository DevonPeroutky/agent-profiler (15 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 4,101 tokens to every session, about $0.0164 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
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Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
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AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.