Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/gosha70/code-copilot-teamWrote 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/commands/gosha70/code-copilot-team/bench)<a href="https://agentmods.dev/commands/gosha70/code-copilot-team/bench"><img src="https://agentmods.dev/badge/commands/gosha70/code-copilot-team/bench/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/commands/gosha70/code-copilot-team/bench"><img src="https://agentmods.dev/badge/commands/gosha70/code-copilot-team/bench.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 | $0.00000 | $0.00072 |
| Opus 5.5 | $0.00000 | $0.00029 |
| Sonnet 5.5 | $0.00000 | $0.00014 |
| Haiku 4.5 | $0.00000 | $0.00007 |
Grade A, and why
bench 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 1mo 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.
What it actually says
Run the benchmark/evaluation pipeline:
- Load benchmark datasets from the configured directory
- Run each strategy (direct, RAG, etc.) against the test cases
- Collect metrics: accuracy, token usage, cost, latency
- Print summary table with per-strategy results
- Flag any strategy that regresses below baseline thresholds
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.
- 1mo ago First seen · 7 lines · 0 tokens per session scan A 4a23acc10334
bench is a command published in the GitHub repository gosha70/code-copilot-team (7 stars, last pushed 17d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 72 tokens. 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 2026-09-03.
Other commands, from other repositories
flow-nexus-neural
Train and deploy neural networks in distributed sandboxes.
story-continue
Resume an interrupted story from where you left off.
data
Invoke the Data Engineer for ETL/ELT, dbt, warehouses, streaming, orchestration, and data quality.
rosetta-preflight
Produce Rosetta's deterministic preflight JSON for a scoped change: optional RA1 structural report, decision-state resolution, and local Rosetta gates. Use before handing execution to a loop runner.
change-models
Set, change, or audit model preferences for pipeline agents — interactive 6-mode workflow (global/workspace prefs, per-agent overrides, first-run wizard, catalog refresh).
ai-do
You are a routing assistant for DSPy AI skills. Understand the user's problem, pick the best skill, and generate a ready-to-run prompt.