Atlassian Skills — AI skill for Claude Code
Token-efficient CLI for Atlassian Server/DC (Jira, Confluence, Bitbucket) — drop-in alternative to mcp-atlassian for LLM agents.
How to install Atlassian Skills
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open eunsanMountain/atlassian-skills and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
What Atlassian Skills does
Token-efficient CLI for Atlassian Server/DC (Jira, Confluence, Bitbucket) — drop-in alternative to mcp-atlassian for LLM agents. Compact output, lossless XHTML<->Markdown round-trip, Claude Code + Codex + GitHub Copilot integration.
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README
atlassian-skills
[](https://pypi.org/project/atlassian-skills/) [](https://pypi.org/project/atlassian-skills/) [](https://pypi.org/project/atlassian-skills/) [](https://opensource.org/licenses/MIT) [](https://github.com/eunsanMountain/atlassian-skills/actions/workflows/ci.yml) [](https://github.com/eunsanMountain/atlassian-skills/stargazers)
A token-efficient CLI that brings [mcp-atlassian](https://github.com/sooperset/mcp-atlassian) functionality to the command line — optimized for LLM agent workflows on Atlassian Server/DC.
mcp-atlassian is great for Cloud setups, but on Server/DC its MCP protocol overhead and verbose JSON responses consume tokens fast. It also lacks lossless Confluence markup round-tripping — edits via MCP can silently alter page content.
**atlassian-skills** re-implements the same Jira and Confluence operations as a lightweight CLI with compact output, achieving **≥50% token reduction**. Its Confluence workflow uses portable managed Markdown plus fresh, source-bound cfxmark proofs; no machine-local database is publication authority.
First-class integration with **Claude Code**, **Codex**, and **GitHub Copilot**. A single `atls setup` wizard configures URLs, tokens, and the auto-loaded Skill for all three agents in one pass.
Why atlassian-skills?
| mcp-atlassian (MCP) | atlassian-skills (CLI) | |
|---|---|---|
| Interface | MCP protocol (JSON-RPC) | Shell CLI (atls) |
| Schema overhead per session | ~15,000 tokens | <400 tokens |
| Response payload size | Full |
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