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.
npx agentmods add commands/codeready-toolchain/tarsy/researchgit clone --depth 1 https://github.com/codeready-toolchain/tarsyWrote 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/codeready-toolchain/tarsy/research)<a href="https://agentmods.dev/commands/codeready-toolchain/tarsy/research"><img src="https://agentmods.dev/badge/commands/codeready-toolchain/tarsy/research.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00258 |
| Opus 5 | $0.00000 | $0.00129 |
| Sonnet 5 | $0.00000 | $0.00052 |
| Haiku 4.5 | $0.00000 | $0.00026 |
Grade A, and why
research 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 4d 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
Comprehensive Internet Research
Perform thorough internet research to gather the most current and comprehensive information:
-
Choose Relevant Sources (use what makes sense for the task):
- Official documentation and API references
- Best practices and design patterns
- Recent blog posts and articles (check dates)
- Stack Overflow and community discussions
- GitHub issues and pull requests
- Relevant academic papers or technical reports
-
Cross-Reference When Needed:
- Verify important information across sources
- Look for consensus on best practices
- Identify conflicting opinions and evaluate them
- Check for version-specific information when relevant
-
Stay Current (when version/recency matters):
- Prioritize sources published within the last 1–3 years (adjust timeframe by domain; use longer windows for historical or foundational topics)
- Look for deprecation notices or updates
- Check for new features or alternatives
- Identify emerging patterns and trends
-
Synthesize Results:
- Summarize key findings with source citations
- Compare different approaches and trade-offs
- Provide recommendations based on research
- Highlight any gaps or uncertainties
Present findings clearly with links to sources for further reading.
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.
- 4d ago First seen · 32 lines · 0 tokens per session scan A afe3270658a3
research is a command published in the GitHub repository codeready-toolchain/tarsy (10 stars, last pushed 4d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 258 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 2026-08-31.
Other commands, from other repositories
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clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.