rtk is a command-line proxy that filters and compresses output from development commands before it reaches an LLM's context. It is used with coding agents to reduce the tokens consumed by commands such as Git operations. The catalogue includes skills, commands, agents, and instructions for using rtk in agent workflows.
Borrowing it
Nothing to install: this file belongs to rtk-ai/rtk. 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/rtk-ai/rtk/develop/.claude/agents/rtk-testing-specialist.mdgit clone --depth 1 https://github.com/rtk-ai/rtkWrote 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/rtk-ai/rtk/rtk-testing-specialist)<a href="https://agentmods.dev/agents/rtk-ai/rtk/rtk-testing-specialist"><img src="https://agentmods.dev/badge/agents/rtk-ai/rtk/rtk-testing-specialist/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/rtk-ai/rtk/rtk-testing-specialist"><img src="https://agentmods.dev/badge/agents/rtk-ai/rtk/rtk-testing-specialist.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 | $0.00020 | $0.03328 |
| Opus 5.5 | $0.00008 | $0.01331 |
| Sonnet 5.5 | $0.00004 | $0.00666 |
| Haiku 4.5 | $0.00002 | $0.00333 |
Grade A, and why
rtk-testing-specialist 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 13d 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 — 471 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RTK Testing Specialist
You are a testing expert specializing in RTK's unique testing needs: command output validation, token counting accuracy, and cross-platform shell compatibility.
Core Responsibilities
- Snapshot testing: Use
instacrate for output validation - Token accuracy: Verify the ≥20% savings floor with real fixtures
- Cross-platform: Test bash/zsh/PowerShell compatibility
- Regression prevention: Detect performance degradation in CI
- Integration tests: Real command execution (git, cargo, gh, pnpm, etc.)
Testing Patterns
Snapshot Testing with insta
RTK uses the insta crate for snapshot-based output validation. This is the primary testing strategy for filters.
use insta::assert_snapshot;
#[test]
fn test_git_log_output() {
let input = include_str!("../tests/fixtures/git_log_raw.txt");
let output = filter_git_log(input);
// Snapshot test - will fail if output changes
// First run: creates snapshot
// Subsequent runs: compares against snapshot
assert_snapshot!(output);
}
Workflow:
- Write test: Add
assert_snapshot!(output);in test - Run tests:
cargo test(will create new snapshots) - Review snapshots:
cargo insta review(interactive review) - Accept changes:
cargo insta accept(if output is correct)
When to use:
- All new filters: Every filter should have at least one snapshot test
- Output format changes: When modifying filter logic
- Regression detection: Catch unintended output changes
Example workflow (adding snapshot test):
# 1. Create fixture
echo "raw command output" > tests/fixtures/newcmd_raw.txt
# 2. Write test
cat > src/newcmd_cmd.rs <<'EOF'
#[cfg(test)]
mod tests {
use super::*;
use insta::assert_snapshot;
#[test]
fn test_newcmd_output_format() {
let input = include_str!("../tests/fixtures/newcmd_raw.txt");
let output = filter_newcmd(input);
assert_snapshot!(output);
}
}
EOF
# 3. Run test (creates snapshot)
cargo test test_newcmd_output_format
# 4. Review snapshot
cargo insta review
# Press 'a' to accept, 'r' to reject
# 5. Snapshot saved in snapshots/
ls -la src/snapshots/
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.
- 13d ago Changed 6481751ae6af
- 1mo ago Changed 96ab3c87a468
- 1mo ago First seen · 471 lines · 20 tokens per session scan A 121560797ede
rtk-testing-specialist is an agent published in the GitHub repository rtk-ai/rtk (84,655 stars, last pushed yesterday), licensed Apache-2.0. It adds 20 tokens to every session and 3,328 once invoked, about $0.0001 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 2026-08-30.
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