A Smart Zone / Dumb Zone context-window meter for Claude Code, OpenCode, Codex, and other AI coding agents.

When you're talking to a long-context model, the first ~30% of the window is the Smart Zone, the model reasons reliably, follows instructions, and doesn't hallucinate. Push past ~40% and you enter the Dumb Zone: attention fragments, instructions get dropped, and "lost-in-the-middle" failures climb. context-bart puts that threshold front-and-center in your terminal so you know when to /compact or start a fresh session before quality degrades.

• Single-line meter: with a 10-segment progress bar, percentage, and zone label.
• Color tiers: green (0–29%), yellow (30–39%), red (40%+). Toggle via the color field in config.
• Multi-host: adapters for Claude Code, OpenCode, Codex, and a generic env-var fallback.
• Zero dependencies: pure Node.js stdlib (fs, child_process). Node 18+.
• Auto window detection: 1M when the model id is tagged [1m], the host sets exceeds_200k_tokens, or observed usage tops 200k; 200k otherwise. Pin a specific window via windowTokens in config.
• Extras: model name, raw tokens, git branch (annotated with worktree name when you're in a linked worktree, e.g. feat-x@worktree_3), session cost.
• Crash-safe: any unexpected input degrades to a fallback line instead of breaking your status bar.

The Claude Code statusline docs hand you the contract, a JSON payload on stdin, your script's stdout becomes the bar, plus a few minimal examples. context-bart is what you'd end up writing if you sat down to ship a good one. The non-obvious things it solves:

• The transcript walk. Sum input_tokens + cache_creation_input_tokens + cache_read_input_tokens from the latest assistant turn, stopping at compact_boundary markers so the bar visibly drops after /compact. The docs examples don't compute used context at all.
• Window detection. Claude Code reports model.id: "claude-opus-4-7" for 1M-tier sessions, the [1m] suffix isn't always there. context-bart reads exceeds_200k_tokens and auto-grows when observed usage tops 200k, so you never see 205% · 410k/200k.
• A defensible threshold. Green / yellow / red at 30 / 40 % comes from the lost-in-the-middle research, not picked from a hat.
• Speed. ~40 ms cold, zero deps. Shell-pipe statuslines that fan out to git, jq, and node land at 200–400 ms, noticeable every refresh.
• Portability. OpenCode and Codex send different payloads; switch tools, the bar follows.

If you want a multi-line bar, embedded gh / weather / etc., or anything that isn't a context meter, the docs page is your starting point. context-bart is purpose-built for the "am I about to enter the Dumb Zone?" question.

One line per host:

# Claude Code
npx context-bart install claude

# OpenCode
npx context-bart install opencode

# Codex (prints the wire-up block — Codex has no central config to patch)
npx context-bart install codex

Each install subcommand resolves an absolute command path, merges a statusLine (or statusline) block into the host's config file, and backs up the previous file to .bak. It's idempotent — re-running it overwrites only the bart-managed key, leaving every other setting intact. Pass --dry-run to preview the change without writing.

Prefer to install the binary first? Either path works — install <host> records whichever invocation it was launched with:

# npm install (puts `context-bart` on your $PATH)
npm install -g context-bart
context-bart install claude

# git clone (no build step; bin/context-bart.js runs as-is)
git clone https://github.com/alejandrok5/context-bart.git ~/context-bart
node ~/context-bart/bin/context-bart.js install claude

For the manual JSON snippets, the env-fallback mode, or troubleshooting, see the per-host docs:

• Claude Code
• OpenCode
• Codex / generic

After running the installer, restart your host so it picks up the new statusline config.

context-bart does a once-a-day, fire-and-forget check against the npm registry. When a newer version is available, a dim ↑0.2.1 segment is appended to the bar, that's your cue to run npm update -g context-bart (or git pull if you cloned). The check runs in a detached background process so it never delays a status-bar refresh; results are cached in ${XDG_CACHE_HOME:-~/.cache}/context-bart/latest.json (or %LOCALAPPDATA%\context-bart\ on Windows). Opt out entirely with "updateCheck": false in your config file.

All user preferences live in a single JSON file. The file is optional — without it you get the defaults.

Drop a config.json at:

• Linux / macOS: $XDG_CONFIG_HOME/context-bart/config.json (default: ~/.config/context-bart/config.json)
• Windows: %APPDATA%\context-bart\config.json

Every field is optional; missing fields fall through to the defaults. Invalid values are silently dropped (the bar still renders).

{
  "zones": { "smart": 30, "dumb": 40 },
  "windowTokens": null,
  "ascii": false,
  "updateCheck": true,
  "color": "auto"
}

The following env vars are not user preferences — they're how the env-fallback and Codex adapters receive per-invocation runtime data from their host. You only set these if you're wiring up a host that drives the bar through env vars rather than a stdin JSON payload.

• Claude Code: walks the JSONL transcript at transcript_path (passed in by Claude Code) from the bottom up, finds the most recent assistant message with a usage block, and sums input_tokens + cache_creation_input_tokens + cache_read_input_tokens. That's the model's actual context size at the last turn. After /compact, the walk stops at the compact_boundary marker and reports 0 until the next assistant turn writes fresh usage — so the bar visibly resets the moment you compact.
• OpenCode: reads usage.total_tokens (or input_tokens + output_tokens) directly from the payload.
• Codex: reads env vars or a nested codex.usage payload.
• env fallback: trusts CONTEXT_BART_USED_TOKENS directly.

The cutoffs aren't picked from a hat — they track converging findings from a stack of long-context benchmarks published between 2023 and 2026. The qualitative cliff is real, and it tends to land earlier than the advertised window suggests:

• "Lost in the Middle" (Liu et al., TACL 2024) — the original result. Recall on multi-document QA drops 30%+ when the relevant document sits in the middle of the context vs. at the edges, even on models marketed as long-context. (ACL Anthology version)
• RULER (Hsieh et al., COLM 2024) — only about half of models advertising a 32K window actually hold up at 32K once you move past needle-in-a-haystack to multi-hop reasoning. "Effective context" is routinely a fraction of the marketing number.
• NoLiMa (Modarressi et al., ICML 2025) — 11 of 12 long-context models fall below 50% of their short-context baseline by 32K tokens. GPT-4o drops from 99.3% → 69.7%. (Adobe Research repo)
• Found in the Middle (Hsieh et al., 2024) — pins the root cause on a U-shaped attention bias from RoPE positional encoding, which is baked into most modern LLMs. The cliff is architectural, not just a training artifact.
• Context Length Alone Hurts LLM Performance Despite Perfect Retrieval (Oct 2025) — even when the relevant passage is handed to the model perfectly, scores degrade 14–85% as surrounding input length grows. Length itself is the variable.
• Intelligence Degradation in Long-Context LLMs (2026) — identifies a critical threshold around 40–50% of max context length where F1 scores collapse catastrophically (e.g. Qwen2.5-7B: 0.55 → 0.30, a 45% drop).
• MRCR v2 8-needle leaderboard (2026) — OpenAI's multi-fact retrieval benchmark is the de-facto 2026 yardstick for the 1M tier. Even the leaders bleed double-digit points moving from 128K to 1M: Claude Opus 4.6 drops 93% → 76%, Gemini 3.1 Pro ~85% → ~70%. 2026 industry analysis puts effective utilization at 50–65% of advertised window for multi-hop work, and notes that the gap between "advertised" and "effective" widens to 30–60 points past 200K tokens (reality check).

Numbers vary by model and task, but the cliff consistently lands in the 30–50% of window band — and the 1M tier hasn't changed that, it's just stretched the absolute token count where degradation starts. context-bart warns yellow at 30% and red at 40% — deliberately conservative, since the alternative is realizing you're past the cliff only after a bad answer. If you're working exclusively on a model with an unusually flat degradation profile, raise the bar via "zones" in your config file, or fork src/render.js (pickZone) — it's ~10 lines.

Adapters live in src/adapters/. The contract is two functions:

module.exports = {
  name: 'my-host',
  detect(stdin, env) {
    // return true if this payload/env looks like yours
  },
  parse(stdin, env) {
    // return a normalized payload:
    // { modelId, modelDisplayName, usedTokens, windowSize, costUsd, cwd, transcriptPath }
  },
};

Register it in src/detect.js (order matters — first detector wins). Add a test in test/adapters.test.js. Submit a PR.

npm test    # runs node --test on the test/ dir
npm run smoke   # pipes an empty payload to verify the fallback path

No dependencies to install. See CONTRIBUTING.md for the release process.

MIT. See LICENSE.

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