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 skills add TonPC64/mcp-a2a-bridge --skill contributegit clone --depth 1 https://github.com/TonPC64/mcp-a2a-bridgeWrote 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/skills/tonpc64/mcp-a2a-bridge/contribute)<a href="https://agentmods.dev/skills/tonpc64/mcp-a2a-bridge/contribute"><img src="https://agentmods.dev/badge/skills/tonpc64/mcp-a2a-bridge/contribute/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/skills/tonpc64/mcp-a2a-bridge/contribute"><img src="https://agentmods.dev/badge/skills/tonpc64/mcp-a2a-bridge/contribute.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 | $0.00012 | $0.00562 |
| Opus 5.5 | $0.00005 | $0.00225 |
| Sonnet 5.5 | $0.00002 | $0.00112 |
| Haiku 4.5 | $0.00001 | $0.00056 |
Grade A, and why
contribute 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 2mo 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Contribute Skill
Use this workflow for any code, dependency, documentation, or dashboard change. Keep changes focused and evidence-based.
Procedure
- Read
AGENTS.mdand the relevant source/tests before editing. - Check
git status --short --branch, identify the base branch, and preserve unrelated work. - Make the smallest change that satisfies the request. Do not add credentials, personal paths, generated screenshots, or unrelated dependencies.
- Add or update tests for behavior changes. Preserve public APIs, MCP tool contracts, and SSE event shapes.
- Run verification from the repository root:
uv lock --check.venv/bin/pytest -qoruv run pytest -qnpm ciandnpm test -- --runfromdashboard/when frontend files changenpm run buildfromdashboard/when frontend or packaged assets changegit diff --check
- For dashboard changes, verify loopback default, token protection for non-loopback deployment, mobile/desktop behavior, and no horizontal overflow when browser tooling is available.
- Review the final diff and confirm every changed file is intentional.
- Commit with a Conventional Commit message, push to GitHub
origin, and report the commit, branch, tests, and any blockers. Create a PR instead of pushing directly tomainwhen repository policy requires it.
Security checklist
- Dashboard defaults to loopback.
- Tokenless non-loopback dashboard binds are documented as trusted-LAN-only.
- Do not expose bearer tokens in logs, URLs, screenshots, README examples, or commits.
- Use TLS and network access controls for LAN deployments.
- Treat
.a2a-agents.jsonand custom headers as secret-bearing configuration.
Pitfalls
- A passing unit test is not proof that a live browser or deployment works.
- Do not claim a dependency upgrade is safe when tests fail after it.
- Do not commit
dashboard/node_modules,.venv, caches, or screenshots. - If a command fails because the environment lacks a tool, report the exact blocker and try a safe alternative.
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.
- 2mo ago First seen · 52 lines · 12 tokens per session scan A c2f652c2d96c
contribute is a skill published in the GitHub repository TonPC64/mcp-a2a-bridge (0 stars, last pushed 1mo ago), licensed MIT. It adds 12 tokens to every session and 562 once invoked, about $0.0000 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 2026-08-31.
Other skills, from other repositories
merge-batch
Prepare and merge an owner-selected PR batch with contributor credit and exact-candidate admission. Use for a pasted PR list; combined focused validation is diagnostic, and every actual landing still requires its applicable checks and authorization.
git-ai-search
Search and restore AI conversation context from git history.
mcore-split-pr
Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.
remember
Save the decisions, facts, dead ends and architecture this conversation established to Atlas's shared memory, so other agents and future sessions can build on them.
machinist
Use Machinist to create, assign, and monitor software tasks. Use when a coding agent needs to work with Machinist, its GitHub issue workflow, intake labels, direct runs, or managed queue.
pr-explainer
Use when a developer asks to explain a pushed PR, walk through a PR stack, understand one stack member, or explain what an AI agent just pushed so they can connect code changes to end-to-end functionality and update domain knowledge. Not for defect review, merge approval, PR-body authoring, or full-repository…