frontier-scout 1.6.1

A local AI adoption radar for tools, MCP servers, agent frameworks, and model drops.

See new AI first. Prove it fits your repo before you ship.

Frontier Scout watches the whole AI ecosystem — GitHub · MCP registries · Hugging Face · Hacker News · RSS — cross-checks your PyPI & npm dependencies, and turns the firehose into source-backed ADOPT / TRIAL / ASSESS / HOLD verdicts mapped to your actual code.
Local-first. Bring your own LLM. Try before you trust.

Quickstart  ·  How it works  ·  Demo  ·  Cost  ·  Roadmap  ·  Releases

377 releases scanned → 5 worth your time. Newsletters tell you what's popular. Trending tells you what's loud. Neither knows your stack — and neither tells you whether a tool is safe to run. Frontier Scout reads your repo locally, ranks releases against it, and refuses to say "ship it" without evidence.

📑 Table of contents

• How it works
• Three promises
• Quickstart
• Bring your own LLM
• 60-second demo
• The killer workflow
• Safety model
• Cost
• Roadmap
• Contributing
• License & acknowledgments

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🛰 How it works

One pipeline, three jobs: find what's new → figure out what's relevant to your code → refuse to say "ship it" without evidence.

01 · WATCH Scout the whole web A candidate can enter from any source family — and only repeated, independent evidence promotes it. GitHub Trending · GitHub Releases · MCP registry · Hugging Face · HN · RSS · arXiv candidate → watched → core → retired

02 · MATCH Map it to your repo A local tree-sitter pass reads filenames and AST imports — never your source — to learn your real stack. Python 3.12 · Docker · AGENTS.md · MCP config · langchain-core==1.3.5 Output: fit · risk · readiness.

03 · DECIDE Verdict + safe next step Source-backed ADOPT / TRIAL / ASSESS / HOLD, a permission map, and the smallest safe trial to run next. capability surface · explicit concerns · dry-run receipt · CI guardrail

A verdict looks like this — note that guard blocks adoption until a trial receipt exists:

  TRIAL  · modelcontextprotocol/servers            safe to test, not to ship
  ─────────────────────────────────────────────────────────────────────────
  what it is        MCP server          what it wants    read  ok   files ro
  fits your code    yes · high · 0.86                    net   ⚠    write ⚠
  risk level        medium · 0.42                        shell ✗    keys  ✗
  eval check        passed · 1.00/1.00   ⚠ guard         blocked until receipt

The verdict detail panel surfaces explicit concerns — burns tokens, abandoned, vendor lock-in, security surface, marketing-only, unproven — so you always see why we'd push back.

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🎯 Three promises

◈ Try before trust	Every adoption candidate gets a sandbox dry-run receipt, a permission map, and a guard check before it touches your real repo.
◆ Fix vulns you didn't know existed	Dependency intelligence cross-references your manifests against curated feeds (security, hardening, breaking) and emits a trial recipe — not a silent lockfile rewrite.
◐ Bound risky changes	Incident Change Scout turns a ticket into cited context, a bounded remediation plan, and a human approval interrupt before any write.

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⚡ Quickstart

Prerequisite: Python 3.11+

# install (pipx recommended) — or run with no install at all
pipx install frontier-scout
uvx frontier-scout demo        # try it without installing

# configure your LLM backend once (auto-detects what you have)
frontier-scout setup

# open Mission Control inside any repo
cd ~/code/my-app && frontier-scout

Mission Control lands on the Scout tab — the radar that ranks the latest AI releases that fit your repo. From a highlighted verdict row, every capability is one keystroke:

L hermetic lab  ·  e Adoption-Firewall eval  ·  i implement & test  ·  D dossier  ·  o open source  ·  p command palette

Tabs: Scout · Schedule · Receipts · Guard · Packs · Deps · Reports · Settings. Everything reflows down to an 80×24 VS Code panel, with unicode/ASCII and colour/mono fallbacks. Prefer a calmer, one-finding-at-a-time flow? frontier-scout --ui briefing.

Develop locally

git clone https://github.com/ajaysurya1221/frontier-scout
cd frontier-scout
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
frontier-scout --help

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🔌 Bring your own LLM

Frontier Scout needs exactly one backend, and works with whichever you already have. The setup wizard detects what's available and picks the first present:

You have…	Set	Cost / scan
An Anthropic API key	ANTHROPIC_API_KEY	~$0.34
An OpenAI API key	OPENAI_API_KEY	~$0.05
Claude Code installed	nothing — auto-detected	$0 marginal
Codex CLI installed	nothing — auto-detected	$0 marginal

Already paying for a Claude Code or Codex subscription? Scouting runs at zero marginal cost — it shells out to the CLI you already pay for. Force a backend with --provider anthropic|openai|claude-cli|codex-cli.

[!NOTE] No backend at all? frontier-scout --demo runs the whole pipeline offline against bundled fixtures — no key, no network, no Slack, no cloud.

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⏱ 60-second demo

frontier-scout demo

╭── ◉ FRONTIER · SCOUT  demo ready ──────────────────────────────╮
│  Serving at  http://localhost:54321  ·  Ctrl+C to stop          │
│                                                                 │
│  ✓  briefing.html   adoption receipts                           │
│  ✓  verdicts.json   raw verdict data                            │
│  ✓  judge-trace.md  quality trace                               │
│                                                                 │
│  Next:  frontier-scout setup            ← Mission Control TUI   │
│         frontier-scout scan --dry-run   ← verdicts for this repo│
╰─────────────────────────────────────────────────────────────────╯

Writes demo/briefing.html, demo/briefing.md, demo/verdicts.json, demo/cost-breakdown.md, and demo/judge-trace.md. Use --no-serve for CI / offline.

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🔭 The killer workflow

Someone drops a repo, MCP server, model, or agent framework in a newsletter or team chat. Turn that link into a local adoption decision instead of a vibes-based "looks safe":

frontier-scout init --repo .          # local stack profile (+ tree-sitter import evidence)
frontier-scout evaluate <tool-url>    # source-backed evidence + permission map
frontier-scout trial <tool> --dry-run # adoption receipt, installs nothing
frontier-scout guard --repo .         # CI gate: risky tools need a stored receipt
frontier-scout report                 # static HTML executive radar

Inspect living packs and repo-relevant dependency upgrades:

frontier-scout packs list             # candidate → watched → core → retired
frontier-scout deps scan --repo .     # repo-relevant security & breaking upgrades
frontier-scout dossier <tool>         # local adoption dossier with explicit unknowns

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🔒 Safety model

Frontier Scout handles untrusted public content and can optionally run untrusted packages in the lab — so the rails are load-bearing:

Rail	What it guarantees
Source text is data, not instructions	Incident & breach headlines can never become tool recommendations.
No hallucinated tools	Tool names are checked against the source pool; source URLs must pass a domain allowlist.
ADOPT must earn it	Not enough readiness evidence → demoted. The Adoption Firewall fails closed on unknown capability surfaces.
The lab is hermetic	Stripped environment, wall-clock timeout, size caps, and generated-script secret scanning.
The scanner is offline	Deterministic local tree-sitter AST parse — never sends source content to an LLM, never hits the network.
guard never writes	It only reads local evidence and policy; CI-friendly exit codes.

See SECURITY.md for the full threat model.

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💸 Cost

frontier-scout --demo is free — it never calls the network. The numbers below are measured from real scans of ~220 live items: a fast score pass, a fast verdict pass, and an optional Opus-class judge pass.

Provider (fast / deep)	Score + verdict	+ judge	Weekly scan
Anthropic Sonnet / Opus	~$0.22	+$0.12	~$0.34
OpenAI gpt-4o-mini / gpt-4o	~$0.01	+$0.04	~$0.05
Claude CLI subscription	$0	$0	$0 marginal
Codex CLI subscription	$0	$0	$0 marginal

Set JUDGE_ENABLED=false to skip the judge for the cheapest run on any provider. Every call is written to a local ~/.frontier-scout/costs.jsonl ledger — and the Receipts tab in Mission Control shows exactly what you spent.

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🗺 Roadmap

Shipped & next

• v0.2 — Living Scout Packs, dependency intelligence, Adoption Firewall, Incident Change Scout.
• v0.4.0 — Monorepo profile walker + tree-sitter import-evidence scanner (Python & JS/TS).
• v1.0.0 — Mission Control: every CLI capability gets a TUI surface, scout-first landing.
• v1.1.0 — Global setup wizard, automation mode with cron scheduling, notifications, Go/Rust/Ruby coverage.
• v1.4.0 — Universal LLM provider (Anthropic / OpenAI / Claude CLI / Codex CLI), RLAIF fit-grounding loop, honest per-provider costs.
• v1.5.0 — Mission Control complete: 8-tab keyboard command center + command palette.
• v1.6.0 — Mission Control v2: full mouse↔keyboard parity, Permission map, repo switcher.
• v1.7 — Streaming subprocess output in Trials, multi-repo workspace, launchd / Windows Task Scheduler.

See ROADMAP.md for the longer view.

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🤝 Contributing

The fastest useful PRs improve the CLI/report path, validator coverage, source quality, or lab isolation. Read CONTRIBUTING.md, browse good first issues, and respect the Code of Conduct.

make setup && make demo && make test && make eval && make audit

CI runs compile checks, non-live tests, and a tracked-file secret scan.

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📄 License

Distributed under the MIT License.

Built with — Textual (TUI) · tree-sitter-language-pack (grammars) · Pydantic (typed models) · SQLite (local store). Structure inspired by othneildrew/Best-README-Template; deterministic import evidence pushed forward by Lum1104/Understand-Anything.

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