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/gregbaugues/tokenbowl-mcp/issuegit clone --depth 1 https://github.com/GregBaugues/tokenbowl-mcpWrote 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/gregbaugues/tokenbowl-mcp/issue)<a href="https://agentmods.dev/commands/gregbaugues/tokenbowl-mcp/issue"><img src="https://agentmods.dev/badge/commands/gregbaugues/tokenbowl-mcp/issue.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 5.1 | $0.00000 | $0.00292 |
| Opus 5 | $0.00000 | $0.00146 |
| Sonnet 5 | $0.00000 | $0.00058 |
| Haiku 4.5 | $0.00000 | $0.00029 |
Grade A, and why
issue 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 5d 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
Follow these steps to analyze and fix the GitHub issue: $ARGUMENTS.
PLAN
- Use a subagent to plan the issue's impelmentation.
CREATE
- Create a new branch for the issue
- Solve the issue in small, manageable steps, according to your plan.
- Commit your changes after each step.
- When finished, write tests to cover the work you did. Follow the testing guideines in CLAUDE.md
- Run linting and formatting:
uv run ruff check .anduv run ruff format . - Fix any linting or formatting issues before proceeding
STAGE
- Open a PR.
REVIEW
- Request a PR from a subagent. Focus on pythonic best practices.
- If the review passes, move on.
- If the review does not pass, fix the code, stage, and review until it does
LEARN
- pass the context of the conversation to the claude.md updater
- once it has modified claude.md, add it changes to the PR.
DEPLOY
- make any final commits and pushes necessary to deploy
- wait until the CI checks have finished running.
- if all the CI on the PR pass, merge it.
- if not, fix it.
Remember to use the GitHub CLI (gh) for all Github-related tasks.
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.
- 5d ago First seen · 36 lines · 0 tokens per session scan A a0b1dbd62102
issue is a command published in the GitHub repository GregBaugues/tokenbowl-mcp (6 stars, last pushed 8mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 292 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
checklist
Generate a custom checklist for the current feature based on user requirements.
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.
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.