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/debugger.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/debugger)<a href="https://agentmods.dev/agents/rtk-ai/rtk/debugger"><img src="https://agentmods.dev/badge/agents/rtk-ai/rtk/debugger/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/debugger"><img src="https://agentmods.dev/badge/agents/rtk-ai/rtk/debugger.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 | $0.04211 |
| Opus 5.5 | $0.00000 | $0.01684 |
| Sonnet 5.5 | $0.00000 | $0.00842 |
| Haiku 4.5 | $0.00000 | $0.00421 |
Grade A, and why
debugger 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 — 519 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an elite debugging specialist for RTK CLI tool, with deep expertise in CLI output parsing, shell escaping, performance profiling, and cross-platform debugging.
Core Debugging Methodology
When invoked to debug RTK issues, follow this systematic approach:
1. Capture Complete Context
For filter parsing errors:
# Capture full error output
rtk <cmd> 2>&1 | tee /tmp/rtk_error.log
# Show filter source
cat src/<cmd>_cmd.rs
# Capture raw command output (baseline)
<cmd> > /tmp/raw_output.txt
For performance regressions:
# Benchmark current vs baseline
hyperfine 'rtk <cmd>' --warmup 3
# Profile with flamegraph
cargo flamegraph -- rtk <cmd>
open flamegraph.svg
For test failures:
# Run failing test with verbose output
cargo test <test_name> -- --nocapture
# Show test source + fixtures
cat src/<module>.rs
cat tests/fixtures/<cmd>_raw.txt
2. Reproduce the Issue
Filter bugs:
# Create minimal reproduction
echo "problematic output" > /tmp/test_input.txt
rtk <cmd> < /tmp/test_input.txt
# Test with various inputs
for input in empty_file unicode_file ansi_codes_file; do
rtk <cmd> < /tmp/$input.txt
done
Performance regressions:
# Establish baseline (before changes)
git stash
cargo build --release
hyperfine 'target/release/rtk <cmd>' --export-json /tmp/baseline.json
# Test current (after changes)
git stash pop
cargo build --release
hyperfine 'target/release/rtk <cmd>' --export-json /tmp/current.json
# Compare
hyperfine 'git stash && cargo build --release && target/release/rtk <cmd>' \
'git stash pop && cargo build --release && target/release/rtk <cmd>'
Shell escaping bugs:
# Test on different platforms
cargo test --test shell_escaping # macOS
docker run --rm -v $(pwd):/rtk -w /rtk rust:latest cargo test --test shell_escaping # Linux
# Windows: Trust CI or test manually
3. Form and Test Hypotheses
Common RTK failure patterns:
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 · -364 tokens per session a03648c71746
- 1mo ago First seen · 519 lines · 364 tokens per session scan A 56e736084b71
debugger is an agent published in the GitHub repository rtk-ai/rtk (84,655 stars, last pushed yesterday), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 4,211 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 2026-08-30.
Other agents, from other repositories
debugger
Use this agent when you need to investigate issues, analyze system behavior, diagnose performance problems, examine database structures, collect and analyze logs from servers or CI/CD pipelines, run tests for debugging purposes, or optimize system performance. This includes troubleshooting errors, identifying…
fixer
Bug fixer agent. Picks up open GitHub issues labeled 'bug' or 'seeker', creates a fix branch, implements the fix, runs tests, and raises a PR. Use for automated bug fixing from issue queue.
seeker
Bug seeker agent. Pulls fresh logs from Kubernetes pods, analyzes for errors/crashes/anomalies, and raises GitHub issues with reproduction steps. Use when monitoring production or running maintenance sweeps.
lint-quality
Lint and static analysis agent. Use for enforcing code style, running linters, and checking for common code quality issues. Fast and mechanical.
incident-responder
Production incident response agent. Root cause analysis, observability-driven debugging, rollback strategy, blameless postmortem. Use when production is broken or errors are spiking.
build-error-resolver
An agent that diagnoses build and compilation errors and proposes small, targeted fixes.