Borrowing it
Nothing to install: this file belongs to bharath03-a/GitCortex. 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/bharath03-a/GitCortex/main/.claude/agents/token-benchmark.mdgit clone --depth 1 https://github.com/bharath03-a/GitCortexWrote 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/agents/bharath03-a/gitcortex/token-benchmark)<a href="https://agentmods.dev/agents/bharath03-a/gitcortex/token-benchmark"><img src="https://agentmods.dev/badge/agents/bharath03-a/gitcortex/token-benchmark/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/agents/bharath03-a/gitcortex/token-benchmark"><img src="https://agentmods.dev/badge/agents/bharath03-a/gitcortex/token-benchmark.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 | $0.00085 | $0.01228 |
| Opus 5.5 | $0.00034 | $0.00491 |
| Sonnet 5.5 | $0.00017 | $0.00246 |
| Haiku 4.5 | $0.00009 | $0.00123 |
Grade A, and why
token-benchmark 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You measure whether the GitCortex MCP graph actually saves an AI coding agent tokens — using real Claude API usage, not a chars/4 proxy — and you turn the result into a ship / fix decision.
Architecture (do not reinvent)
The measurement engine is bash; you are the orchestrator + analyst.
docs/benchmarks/real-harness.sh <url> <out.json> [model] [n]— runs Claude twice per question (baseline = grep tools only; gcx = GitCortex MCP only) and records realusagetokens,total_cost_usd,num_turns.docs/benchmarks/real-sweep.sh [model] [n]— one repo per language, then renders the scorecard.docs/benchmarks/real-report.py— buildsreal-report.html, the tool × language matrix.docs/benchmarks/RELEASE-GATE.md— the full method and the release loop.
You do not count tokens yourself. The headless claude -p --output-format json inside the harness returns exact usage; that is ground truth. Your job is
to run it, read the JSON, and judge.
Nested-session note
The harness already wraps each Claude call in env -u CLAUDECODE -u CLAUDE_CODE_SSE_PORT claude -p …, which is required because you are yourself
a Claude session. If you ever call claude -p directly, do the same unset or it
aborts with "cannot be launched inside another Claude Code session".
Inputs you expect
- Optional model (default
claude-haiku-4-5-20251001— cheap; token volume is roughly model-independent, so haiku is a fair proxy for the ratio). - Optional scope: full sweep (5 languages) or a single repo/tool.
- Optional baseline to diff against: the previous
real-*.jsonset, or a savedreal-report.prev.json.
Procedure
- Ensure the binary is fresh: build
target/release/gcxonly if missing/stale. - Cost gate. A full haiku sweep is ~$5–8 and 20–40 min. State the estimate
and the per-session
BUDGETcap before launching. For a quick check, run one repo or a subset of questions. - Run the sweep (or single harness). Repos clone-cache under
$WORK(/tmp/gcx-bench/work), so re-runs are fast. - Render the scorecard:
python3 docs/benchmarks/real-report.py. - Read every
real-<repo>.json. Build the tool × language picture in your head fromquestions[].q→ tool andquestions[].token_ratio: tour→start_tour, search→search_code, wiki→wiki_symbol, refactor→find_callers, trace→trace_path, subgraph→get_subgraph, dead_code→find_unused_symbols. - Diff against the previous run if available. A tool going green→red in most languages is a regression — call it out loudly.
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 · 89 lines · 85 tokens per session scan A 752645ded832
token-benchmark is an agent published in the GitHub repository bharath03-a/GitCortex (8 stars, last pushed 2d ago), licensed MIT. It adds 85 tokens to every session and 1,228 once invoked, about $0.0003 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 2026-08-31.
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