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Hacker News - Newest: "AI"

AI can't read an investor deck AI as an attorney? Student uses ChatGPT, Gemini to sue UW over alleged racial discrimination Hacking MCP Servers in AI Systems – The Rug Pull: Tool Changes After Approval GitHub - MeepCastana/KubeezCut: Free Web based video editor Can AI judge journalism? A Thiel-backed startup says yes, even if it risks chilling whistleblowers Coming soon: 10 Things That Matter in AI Right Now DARPA built an AI to fact-check enemy weapons claims What explains heterogeneity in AI adoption? When AI Meets Muscle: Context-Aware Electrical Stimulation Promises a New Way to Guide Human Movements - Department of Computer Science AI Changed How We Build. It Did Not Change What Matters. Linux rules on using AI-generated code - Copilot is OK, but humans must take 'full responsibility for the… Meta spins up AI version of Mark Zuckerberg to engage with employees Code Mode: Let Your AI Write Programs, Not Just Call Tools | TanStack Blog GitHub - Delavalom/graft: Go framework for building AI agents. Type-safe tools, multi-provider (OpenAI, Anthropic, Gemini, Bedrock), zero vendor SDKs. India's TCS tops estimates, says new AI models did not dent services demand Gen Z's fading AI hype Strong feeling: we are in a folded AI reality GitHub - machinarii/total-recall-catalog: A reference catalog of latest knowledge retrieval, memory & RAG systems GitHub - mensfeld/code-on-incus: Give each AI agent its own isolated machine with root, Docker, and systemd. Active defense detects and stops threats automatically.. Quantization, LoRA, and the 8% Problem: Benchmarking Local LLMs for Production AI Iran war: We spoke to the man making Lego-style AI videos that experts say are powerful propaganda Powell, Bessent discussed Anthropic's Mythos AI cyber threat with major U.S. banks GitHub - immartian/bellamem: Persistent belief-graph memory for AI agents. Retrieves decisive context by importance — not recency, not RAG, not /compact. recursive-mode: The Repo-Native Operating System for AI Engineering After the attack on Sam Altman's home, will AI CEO's go on the offensive? The biggest advance in AI since the LLM Opus 4.6 vs GPT 5.4 One Prompt Unity World Generation Test “AI polls” are fake polls Client Challenge Can AI be a 'child of God'? Inside Anthropic's meeting with Christian leaders
GitHub - Naveja00/OverReach
Naveja · 2026-06-20 · via Hacker News - Newest: "AI"

npm version license CI

A standalone MCP tool that catches AI-agent scope creep.

You give it the prompt you gave your coding agent, and the diff it produced. Overreach tells you whether the diff stayed inside what the prompt asked for — or whether the agent quietly added an endpoint, a dependency, an env var, or a cron job that you never asked for.

"turns out my ai assistant had been extremely making product decisions without me"

Prerequisites

  • Node.js 18+nodejs.org. Verify with node -v.
  • npm comes with Node.js. Verify with npm -v.
  • Git — required for the pre-commit hook and git diff piping.

Try it (no key needed)

npx -y -p overreach overreach-cli demo

Runs the real pipeline on a sample diff — no API key, no setup, costs nothing. Exits 1 with a HIGH scope-creep finding (the demo prompt asks for a login form; the diff smuggles in Stripe, an env var, an endpoint, and a cron job). That's the whole product in one command.

What it checks

A diff is flagged when it adds something the prompt never authorized:

Finding kind Caught when the diff adds…
scope.dep a package/requirement the prompt didn't name
scope.env an env var (process.env.X, os.environ, .env)
scope.endpoint an HTTP route / handler / route.ts file
scope.cron a cron / scheduler job
scope.file edits to a file the prompt didn't touch on
scope.feature a new top-level symbol/feature beyond the prompt

Severity: env / endpoint / cron = high · dep / file = medium · feature = low. Overall scope_creep_score: HIGH if any high finding, MEDIUM if any medium, else LOW.

How it works (3 stages)

  1. Stage 1 — Scope extraction (LLM). Reads your prompt and produces an authorized scope JSON: which files, features, deps, endpoints, env, and behaviors you actually asked for. Deciphers typos to the nearest real concept but never invents scope. This is the only stage that calls a model.
  2. Stage 2 — Diff parsing (deterministic, no LLM). Regex-parses the diff into the set of things it actually adds — imports, deps, process.env.X references, route handlers, cron jobs, new symbols. Runs in milliseconds.
  3. Stage 3 — Comparison (deterministic). Set arithmetic with fuzzy matching: actual − authorized = findings.

Stages 2 and 3 are pure functions — no inference, no opinion, fully auditable. That's what makes Overreach testable without spending a cent on inference.

Install

Or use directly via npx (no install needed):

npx -y -p overreach overreach-cli demo

API key (optional)

For best results, set one LLM provider key for Stage 1 scope extraction:

Provider Env vars
Anthropic ANTHROPIC_API_KEY
OpenAI / OpenAI-compatible (OpenRouter, Groq, Together, LM Studio, …) OPENAI_API_KEY + OPENAI_BASE_URL (e.g. http://localhost:1234/v1 for LM Studio)
Ollama (Cloud or self-hosted) OLLAMA_API_KEY + OLLAMA_BASE_URL

Pin a provider/model with SCOPE_PROVIDER and OVERREACH_MODEL.

No key? No problem. Without an API key, Overreach falls back to deterministic scope extraction — it regex-parses your prompt for concrete items (file paths, package names, /api/... routes, SCREAMING_SNAKE_CASE env vars, cron keywords) instead of calling an LLM. It won't understand vague instructions as well as an LLM would, but it catches every concrete noun in your prompt. Instant, free, fully offline.

Quick start

1. Set up a project (one command)

npx -y -p overreach overreach-cli init

This creates three things:

  • .overreach/prompt.md — write the prompt you gave your agent here
  • .git/hooks/pre-commit — audits every commit against your prompt
  • CLAUDE.md — instructs AI agents to self-audit before committing

2. Write your prompt

Edit .overreach/prompt.md with the actual instruction you gave your AI agent:

Add a login form to the settings page with email/password fields,
form validation, and a submit button that calls /api/auth/login.

3. Commit — Overreach runs automatically

git add . && git commit -m "add login form"

The pre-commit hook audits staged changes against your prompt:

  • HIGH scope creep → commit blocked (exit 1)
  • MEDIUM / LOW → commit allowed with findings printed
  • Template prompt (not yet edited) → skipped gracefully
  • No API key → deterministic fallback (extracts concrete items from prompt)

Skip with git commit --no-verify when you know what you're doing. Update .overreach/prompt.md whenever you give the agent a new task.

Windows: The pre-commit hook is a shell script. It works out of the box with Git Bash (included with Git for Windows).

CLI (manual check)

npx -y -p overreach overreach-cli --prompt "add a login form to the settings page" --diff my-changes.diff

Or pipe a diff:

git diff | npx -y -p overreach overreach-cli --prompt "add a login form to the settings page"

Exits 0 if clean, 1 if HIGH — usable as a CI gate.

Options:

  • --prompt <text> — the instruction that authorized the work
  • --diff <path> — diff file (default: read from stdin)
  • --scope <path|json> — inject authorized scope; skips the LLM entirely
  • --json — emit raw JSON instead of pretty terminal output
  • --no-cache — bypass the scope cache (force a fresh Stage 1 call)
  • demo — run the canonical demo (zero-key)
  • init — install pre-commit hook + CLAUDE.md

MCP server (Claude Code, Cursor, Codex, Claude Desktop)

Overreach is a stdio MCP server, so any MCP-capable client can connect:

Claude Code:

claude mcp add overreach -- npx -y overreach

Claude Desktop / Cursor — add to your MCP config:

{
  "mcpServers": {
    "overreach": { "command": "npx", "args": ["-y", "overreach"] }
  }
}

Codex CLI — add to ~/.codex/config.toml:

[mcp_servers.overreach]
command = "npx"
args = ["-y", "overreach"]

Or Streamable HTTP: set PORT=8787 and POST to http://localhost:8787/mcp.

The HTTP endpoint has no auth. It binds to 127.0.0.1 (loopback) by default — safe for local use. Do not expose it publicly (OVERREACH_HOST=0.0.0.0) without an authed reverse proxy in front: anyone who can reach it can call check_overreach and spend your LLM budget.

Tools exposed: check_overreach(prompt, diff, options?) and health.

First-time setup (Claude Code)

# 1. Register the server with Claude Code (one time)
claude mcp add overreach -- npx -y overreach

# 2. Restart your Claude Code session
#    (a session already open won't see the new server until you quit and reopen it)

# 3. Optionally set an API key (works without one via deterministic fallback)
export ANTHROPIC_API_KEY=sk-...     # or OPENAI_API_KEY / OLLAMA_API_KEY

After the restart, every new session has check_overreach available — no per-task setup. The agent calls it when it decides it's relevant.

The key isn't passed through automatically. The MCP server is a separate process; your agent does not hand it its own credentials. If you log in to Claude Code with claude login (OAuth / subscription), there's no ANTHROPIC_API_KEY in the environment — so export one (any provider works; local Ollama needs no key), or for Claude Desktop / Cursor add it to the server's env:

{ "mcpServers": { "overreach": { "command": "npx", "args": ["-y", "overreach"], "env": { "ANTHROPIC_API_KEY": "sk-..." } } } }

The agent self-audit pattern

overreach init adds a scope-audit instruction to your project's CLAUDE.md so AI agents self-audit their staged changes before committing — no user intervention needed. The agent reads the instruction and runs Overreach on its own diff.

You can also have the agent call check_overreach directly via the MCP server with its own task string + the diff it's about to commit:

git diff --staged | overreach-cli --prompt "<the task you just gave me>"

This is best-effort — an agent can skip the call or ignore the findings (fox guarding the henhouse). The hard backstop is the CI gate below.

CI gate (GitHub Action)

The hard backstop. A workflow runs Overreach on every pull request and fails the PR when scope_creep_score=HIGH — the diff adds a dep / env var / endpoint / cron / out-of-scope file the prompt didn't authorize.

Copy .github/workflows/overreach.yml into your repo and add ANTHROPIC_API_KEY (or OPENAI_API_KEY / OLLAMA_API_KEY) as a repository secret. The prompt comes from .overreach/prompt.md in the repo, or the PR title + body if that file is absent. The job posts its findings as a PR comment and fails the check on HIGH. Full setup + customization in docs/ci-gate.md.

# .github/workflows/overreach.yml  (excerpt)
- name: Run Overreach
  run: |
    npx -y -p overreach@latest overreach-cli \
      --prompt "$(cat "$RUNNER_TEMP/prompt.txt")" --diff "$RUNNER_TEMP/pr.diff"
  env:
    ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
- name: Gate — fail the PR on HIGH
  if: steps.overreach.outputs.exit == '1'
  run: exit 1

This open-source Action is free to run (you bring your own LLM key).

Tested models

Model Result
Claude Sonnet 4.6 82/82
Claude Opus 4.6 65/65
GLM 5.2 82/82
Kimi K2.7-Code 82/82
MiniMax M3 81/82

The deterministic fallback (no key) works with any prompt that contains concrete items — no model needed.

Verify it works (zero API key)

Runs 56 assertions through the real pipeline with the scope injected via scopeOverride, so Stage 1 (the LLM) is never called. Covers overreach detection, clean passes, Python/Express/Next.js parsers, deletion handling, determinism, chunking, and the trust contract invariant.

Standalone

Overreach is fully self-contained. It does not import or depend on any other project. It reads only its own process environment. No telemetry, no call-home — it runs entirely on your machine.

Bugs & feedback

If Overreach misses something it should flag, or flags something the prompt authorized, open an issue with the prompt + the smallest repro diff:

https://github.com/Naveja00/OverReach/issues

There's a bug-report template that asks for exactly that.

License

MIT