Every time you use an AI coding agent, it reads files on your computer. It opens your source code, scans your configuration files, and potentially encounters secrets — API keys, tokens, SSH credentials — that live in your project directories. The agent processes all of this and sends relevant portions to a remote model.

Most developers just… trust that process. But what if you didn’t have to?

Confessor is a new open-source tool from ninjahawk on GitHub that answers one specific question: What did Claude Code actually do on my computer during that session?

It’s 100% local. Zero dependencies. Zero network calls of its own. And it can tell you every file opened, every secret that entered the context window, and every suspicious “sensitive-read-then-network-call” sequence.

Why This Matters

AI coding agents like Claude Code work by reading your files and feeding relevant context to a large language model. That process is mostly invisible. You see the results — generated code, answers to questions, edits to your codebase — but not the trail of file reads and context inclusions that produced those results.

This opacity raises legitimate questions:

  • Did the agent read my .env file? Many projects have sensitive credentials sitting in environment files that live in or near the project root.
  • Did secrets reach the context window? An agent reading your config files might inadvertently pull in API keys, database passwords, or access tokens.
  • Were any sensitive reads followed immediately by network activity? That pattern — read sensitive file, make network call — is exactly the kind of thing you’d want to audit.

Confessor makes all of this visible, in a self-contained HTML report you can read locally.

How Confessor Works

Claude Code maintains detailed session logs at ~/.claude/projects/**/*.jsonl. These are JSON Lines files that record what happened during each Claude Code session — files opened, context included, operations performed.

Confessor reads those JSONL files locally and generates a comprehensive HTML audit report. According to the official GitHub repository, it:

  • Lists every file that entered the agent’s context window
  • Identifies secrets (API keys, tokens, credentials) that the agent encountered
  • Flags sequences where a sensitive file read was followed by a network call
  • Works entirely offline — no data leaves your machine during the audit

Running Confessor

Based on the GitHub repository (confirmed via the official source at github.com/ninjahawk/Confessor):

npx confessor

This runs Confessor against your local Claude Code session logs and generates the HTML report. The tool requires no installation — npx handles it on demand.

For CI integration, the repository documents a --fail-on flag that allows you to use Confessor as a gate in your pipeline — failing the build if sensitive patterns are detected:

npx confessor --fail-on sensitive-network-sequence

Important: The exact flags and options available may evolve as the project develops. Always check the official Confessor README for current usage, especially before integrating into CI pipelines. Do not rely on flags not documented in the official source.

Reading the Report

The HTML report Confessor generates is self-contained — no external resources, no JavaScript phoning home. You open it in a browser locally.

The report is organized to surface the most important findings clearly:

  • Files accessed: A complete list of every file that entered Claude Code’s context window during the audited session
  • Secrets detected: Any credentials, tokens, or keys that the agent encountered, with context about where they were found
  • Suspicious sequences: Highlighted chains where sensitive file reads preceded network activity
  • Session timeline: A chronological view of agent activity for manual review

What To Do With the Results

If Confessor surfaces something concerning, here’s how to think about it:

Sensitive files in context: If .env files or credential files appeared in the context window, evaluate whether those secrets could have been transmitted. If you’re concerned, rotate any exposed credentials immediately.

Sensitive-read-then-network-call sequences: This pattern doesn’t necessarily mean anything malicious happened — the agent may have read a config file to understand your project and then fetched documentation. But it’s worth reviewing what network calls occurred and what data was involved.

Routine access you didn’t expect: It’s common for agents to read more of your project than you realized — package.json, tsconfig, .gitignore, and similar files are often scanned for project context. This is generally expected behavior but worth knowing.

The Bigger Security Picture

Confessor is part of a growing awareness in the developer community that AI coding agents need the same kind of scrutiny we apply to other tools with privileged file system access. You wouldn’t run an npm package from an unknown author without checking what it does. An AI agent with broad file system access deserves at least the same level of auditability.

The project is MIT-licensed, which means you can review the source yourself, fork it, and adapt it for your organization’s specific audit needs.

For teams running Claude Code in professional or enterprise environments, consider making Confessor a standard part of your security practice — especially for sessions that involve repositories containing credentials, proprietary IP, or sensitive configuration.

The tool is young and the ecosystem is moving fast, but the underlying principle — that AI agent file access should be transparent and auditable — is one that’s going to become more important, not less, as agents take on larger roles in development workflows.

Sources

  1. Confessor GitHub Repository (ninjahawk/Confessor)
  2. Reddit r/ClaudeAI Discussion on Confessor
  3. Hacker News — Show HN: ninjahawk/Confessor

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260711-2000

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