Imported from akillness/jeo-skills (.agent-skills/openspace/SKILL.md). Install upstream with npx skills add akillness/jeo-skills --skill openspace. Copyright stays with the author (MIT).

OpenSpace

Use this skill when the real question is "how does this host agent find, trust, and improve the right SKILL.md out of a large catalog?" — not when the user just wants a single skill executed.

OpenSpace's own README frames it as "The Skill Management Layer for AI Agents": your skills keep growing, and OpenSpace helps you retrieve, evaluate, and evolve with every run. In this repo, that job is specifically: act as the skill-finder / skill-retrieval layer for jeo-skills — a host agent installs OpenSpace once, then asks it to search, rank, and load the right SKILL.md out of the ~150 skills already installed under .agent-skills/ (and copied into $HOME/.agents/skills), instead of an agent guessing from a flat skill list or a human maintaining routing rules by hand.

When to use this skill

• The user wants to install OpenSpace so a host agent can act as a skill-finder over an existing skill catalog
• The user (or an agent) needs to retrieve/rank the best-fit skill for a task out of many installed skills
• The user wants to know which installed skills actually work based on real execution outcomes, not just descriptions
• The user wants controlled skill evolution (FIX / DERIVED / CAPTURED) driven by evidence from real runs
• The user wants to share or import skills through a local-first hub (package browsing, explicit import, trust-gated upload)
• The user is choosing between OpenSpace and a plain catalog grep, a long-lived knowledge wiki, or per-repo agent memory

When not to use this skill

• The user only needs plain keyword/grep lookup across the existing skill catalog → route to codebase-search or semble; do not stand up a full MCP skill-management layer for a one-off lookup
• The user wants long-lived, synthesized markdown knowledge (concepts, indexes, research notes) → route to llm-wiki
• The user wants per-repo agent memory (handoff notes, decision logs, manifests) that is not about skill selection at all → route to opencontext
• The user just wants to run one specific, already-known skill → load that skill's SKILL.md directly; OpenSpace is for finding and judging skills, not a replacement for using them

Instructions

Step 1: Classify the job into one routing mode

Pick exactly one primary mode before doing anything else:

1. install-as-skill-finder — wire OpenSpace into a host agent so it can search this catalog
2. retrieve-a-skill — given a task, find/rank/load the right SKILL.md
3. evaluate-skill-quality — judge whether an installed skill is trustworthy from execution records
4. evolve-skills — apply evidence-driven FIX / DERIVED / CAPTURED updates
5. share-import-hub — publish a trusted skill or pull one from the local-first cloud hub
6. route-out — the job really belongs to codebase-search/semble, llm-wiki, or opencontext

Full mode detail and route-out reasoning: references/skill-discovery-routing.md.

Step 2: Install OpenSpace (Path A quick start)

Grounded in the upstream Quick Start:

git clone --filter=blob:none --sparse https://github.com/HKUDS/OpenSpace.git ~/.openspace/OpenSpace
cd ~/.openspace/OpenSpace
git sparse-checkout set --no-cone '/*' '!/assets/'   # skips the ~50 MB assets/ folder

# A dedicated venv, not the system Python: `pip install -e .` against a Homebrew or
# distro Python fails with PEP 668 "externally-managed-environment", which is how
# openspace-mcp ends up missing on a machine that "installed successfully".
python3 -m venv ~/.agents/venvs/openspace          # or: uv venv --python 3.12 ~/.agents/venvs/openspace
~/.agents/venvs/openspace/bin/python -m pip install -e .
~/.agents/venvs/openspace/bin/openspace-mcp --help  # verify installation
ln -sf ~/.agents/venvs/openspace/bin/openspace-mcp ~/.local/bin/openspace-mcp

Requires Python 3.12+.

Or run the bundled installer, which performs the same steps non-interactively, copies the two host skills (Step 4), and registers the MCP server (Step 3):

bash scripts/install-openspace.sh --help
bash scripts/install-openspace.sh --dry-run
bash scripts/install-openspace.sh

Full per-host wiring detail: references/install-and-mcp-wiring.md.

Step 3: Wire the MCP server, with OPENSPACE_HOST_SKILL_DIRS pointed at this repo's skill root

Register the openspace MCP server in every AI runtime installed on the machine — Claude Code and its Anthropic-compatible forks (kimi, glm/zai, deepseek, grok, qwen), Codex, Gemini CLI, Cursor, OpenCode, and the pi / gjc / jeopi agent runtimes. For jeo-skills, OPENSPACE_HOST_SKILL_DIRS must point at $HOME/.agents/skills — that is where this repo's installers copy skills for host agents to load, so that is the directory OpenSpace should scan and rank against.

Use the bundled registrar instead of hand-editing each config:

bash scripts/register-openspace-mcp.sh --dry-run   # preview every runtime it would touch
bash scripts/register-openspace-mcp.sh             # merge in place
bash scripts/register-openspace-mcp.sh --force     # overwrite an existing openspace entry

It writes mcpServers JSON (~/.claude.json, ~/.claude/claude_desktop_config.json, ~/.gemini/settings.json, ~/.qwen/settings.json, ~/.cursor/mcp.json, ~/.kimi/mcp.json, ~/.glm/mcp.json, ~/.zai/mcp.json, ~/.deepseek/mcp.json, ~/.pi/agent/mcp.json, ~/.gjc/agent/mcp.json, ~/.jeopi/agent/mcp.json), TOML [mcp_servers.openspace] (~/.codex/config.toml, ~/.grok/config.toml), and OpenCode's mcp block with type: local (~/.config/opencode/opencode.json). Existing files keep their mode and are replaced atomically; symlinks and non-regular configs are refused; a runtime whose config directory does not exist is skipped rather than invented. The absolute venv binary path is written, so registration does not depend on ~/.local/bin being on the agent's PATH (resolution order: $OPENSPACE_VENV/bin/openspace-mcp, ~/.local/bin/openspace-mcp, PATH, legacy ~/.openspace/venv/bin/openspace-mcp).

The entry it writes is equivalent to:

{
  "mcpServers": {
    "openspace": {
      "command": "$HOME/.agents/venvs/openspace/bin/openspace-mcp",
      "toolTimeout": 600,
      "env": {
        "OPENSPACE_HOST_SKILL_DIRS": "$HOME/.agents/skills",
        "OPENSPACE_WORKSPACE": "$HOME/.openspace/OpenSpace",
        "OPENSPACE_CLOUD_MODE": "local",
        "OPENSPACE_CLOUD_API_KEY": "sk-xxx (optional, for cloud)"
      }
    }
  }
}

OpenSpace supports three launch modes; prefer stdio for local use:

• stdio — keep command: "openspace-mcp" in the host config (simplest option)
• SSE — openspace-mcp --transport sse --host 127.0.0.1 --port 8080 → endpoint http://127.0.0.1:8080/sse
• streamable HTTP — openspace-mcp --transport streamable-http --host 127.0.0.1 --port 8081 → endpoint http://127.0.0.1:8081/mcp

Do not report installation success until openspace-mcp --help works and the MCP client can see OpenSpace's tools; long execute_task calls need a toolTimeout of at least 600 seconds.

Step 4: Copy the two host skills that teach the agent when/how to call OpenSpace

cp -r OpenSpace/openspace/host_skills/delegate-task/ /path/to/your/agent/skills/
cp -r OpenSpace/openspace/host_skills/skill-discovery/ /path/to/your/agent/skills/

skill-discovery/SKILL.md teaches the host agent to search/discover skills; delegate-task/SKILL.md teaches it to execute, fix, and upload. For jeo-skills, copy both into $HOME/.agents/skills/ (this is exactly what scripts/install-openspace.sh automates). No additional prompting is needed after that — the host agent now knows when to reach for OpenSpace instead of guessing.

Step 5: Retrieve/discover the right skill for a task

Once installed, ask the host agent (via the openspace MCP tools, or skill-discovery) to search and rank skills for the task at hand instead of scanning the catalog by hand. OpenSpace discovers skills from OPENSPACE_HOST_SKILL_DIRS, configured skills.skill_dirs, project roots such as .openspace/skills, user roots such as ~/.openspace/skills, and finally its own bundled openspace/skills — in that precedence order. Every discovered skill passes check_skill_safety before it can be loaded; skills with dangerous patterns (prompt injection, credential exfiltration) are blocked and logged.

Both ranking stages are optional at runtime and fail silently. BM25 needs the rank_bm25 package in the OpenSpace venv; the semantic re-rank needs an OpenAI-compatible key (OPENAI_API_KEY / OPENROUTER_API_KEY, or a nanobot/OpenClaw host config). With neither, search_skills still answers, every result carries score: 0.0, and the order is arbitrary — the skill finder is effectively a name lookup. Always verify with an intent query ("scrape a javascript rendered page" should surface scrapling), never a name query, which passes even when ranking is dead.

Ranking detail and route-out reasoning: references/skill-discovery-routing.md.

Step 6: Evaluate skill quality from real execution evidence

Do not trust a skill because its description reads well. OpenSpace's quality layer tracks whether a skill was selected, applied, completed, or fell back, and whether its underlying tools became unreliable, slow, or risky — using actual task behavior as evidence instead of self-reported claims.

Step 7: Evolve skills only when evidence demands it

OpenSpace evolves skills through three controlled triggers:

• FIX — repair a broken or outdated skill
• DERIVED — create a better or more specialized version from an existing skill
• CAPTURED — save one reusable subworkflow, but only when the source trace shows both its execution and a separate validation of the claimed postcondition

New/evolved skills are provisional by default; independent successful use promotes a skill to trusted, while an attributable failure demotes it. enabled controls reuse independently from that trust lifecycle. Blocked or uncertain proposals stay as audit-only candidates — recurrence never auto-promotes them.

Full detail: references/quality-and-evolution.md.

Step 8: Share or import skills through the local-first hub

Skills run and are evolved locally; the cloud is for discovery and review, not execution:

openspace-download-skill  # download a skill from the cloud
openspace-upload-skill --skill-dir /path/to/skill/dir  # upload a trusted skill

Cloud upload requires the matching local SkillStore record to be trusted — both public and private uploads fail closed for provisional or unknown records. The local trust state itself is never sent to the cloud. Optional cloud bootstrap:

openspace-cloud-auth bootstrap-agent-key --email you@example.com --agent-name openspace-local-agent

Without cloud bootstrap, all local capabilities (task execution, evolution, local skill search) still work normally.

Step 9: Route out honestly when OpenSpace is not the right layer

• Plain grep/keyword catalog lookup (no ranking/quality/evolution needed) → codebase-search or semble
• Long-lived synthesized markdown knowledge (not skill selection) → llm-wiki
• Per-repo agent memory / handoff notes / decision logs (not skill selection) → opencontext

Examples

Example 1: Install OpenSpace as the skill-finder for this repo

Input

Wire up OpenSpace so my coding agent can find the right skill out of our 150 installed skills instead of me pointing it at one by hand.

Output sketch

• Mode: install-as-skill-finder
• Run bash scripts/install-openspace.sh (clone + pip install -e . + verify openspace-mcp --help)
• Register the openspace MCP server in every installed runtime with bash scripts/register-openspace-mcp.sh (OPENSPACE_HOST_SKILL_DIRS=$HOME/.agents/skills, OPENSPACE_WORKSPACE=$HOME/.openspace/OpenSpace)
• Copy skill-discovery/ and delegate-task/ into $HOME/.agents/skills/
• Verify tools are visible and a lightweight local skill search works

Example 2: Find the right skill for a task

Input

I need to scrape a JS-rendered page — which of our installed skills should I use?

Output sketch

• Mode: retrieve-a-skill
• Ask OpenSpace to search/rank skills for "JS-rendered page scraping" across OPENSPACE_HOST_SKILL_DIRS
• Expect it to surface scrapling (or an equivalent) ranked above generic candidates, using BM25 + embedding hybrid ranking, then load that skill's SKILL.md

Example 3: Judge whether a skill is still reliable

Input

We've used the deployment-automation skill a dozen times. Is it actually working?

Output sketch

• Mode: evaluate-skill-quality
• Pull execution records: selected / applied / completed / fell-back counts, tool reliability signals
• Report trust status (provisional vs trusted) instead of trusting the skill's own description

Example 4: Should NOT trigger — plain catalog search

Input

Just grep the repo for which skill mentions "playwright".

Output sketch

• Mode: route-out
• This is a plain keyword lookup, not skill ranking/quality/evolution — route to codebase-search/semble instead of installing/invoking OpenSpace

Best practices

1. Position OpenSpace as the skill-finder layer, not a replacement for the skills themselves — it selects and judges, agents still execute.
2. Point OPENSPACE_HOST_SKILL_DIRS at $HOME/.agents/skills for this repo so ranking runs against the actual installed catalog.
3. Prefer stdio for local MCP wiring; only use SSE/streamable HTTP when the host cannot use stdio or needs a standalone/remote server.
4. Trust skills by evidence (selected/applied/completed/fell-back), not by how good their description reads.
5. Keep evolution controlled — FIX/DERIVED/CAPTURED with provisional-then-trusted promotion, never silent auto-rewrite.
6. Treat the cloud hub as discovery-only; execution and trust decisions stay local, and upload requires trusted status.
7. Route out honestly to codebase-search/semble, llm-wiki, or opencontext when the job is not actually skill retrieval/quality/evolution.
8. Do not report install success until openspace-mcp --help works and the MCP client can see OpenSpace's tools.

References

• Install and MCP Wiring
• Skill Discovery and Routing
• Quality and Evolution
• scripts/install-openspace.sh — non-interactive installer, host-skill copy, and MCP registration
• scripts/register-openspace-mcp.sh — merge the openspace MCP entry into every installed runtime config (claude / codex / gemini / cursor / opencode / kimi / glm / zai / deepseek / grok / qwen / pi / gjc / jeopi)
• OpenSpace GitHub Repository