| name | delegate-task |
| description | Delegate tasks to OpenSpace — a full-stack autonomous worker for coding, DevOps, web research, and desktop automation, backed by an extensive MCP tool and skill library. Skills auto-improve through use, reducing token consumption over time. A cloud community lets agents share and collectively evolve reusable skills. |
Delegate Tasks to OpenSpace
OpenSpace is connected as an MCP server. Whether the host uses stdio, sse, or streamable-http, you have the same tools available: cloud_auth_flow, execute_task, search_skills, cloud_browse_skills, fix_skill, upload_skill.
When to use
- You lack the capability — the task requires tools or capabilities beyond what you can access
- You tried and failed — you produced incorrect results; OpenSpace may have a tested skill for it
- Complex multi-step task — the task involves many steps, tools, or environments that benefit from OpenSpace's skill library and orchestration
- User explicitly asks — user requests delegation to OpenSpace
Tools
cloud_auth_flow
Set up OpenSpace cloud access for cloud skill search or skill upload.
Use it only when the user asks for cloud features, or when a cloud operation reports a missing/invalid key.
Ask for email and agent_name. Ask for password only through secure secret input; it must be 8 to 72 characters. Do not ask for passcode/OTP because this tool does not accept one.
If secure password input is available, call:
cloud_auth_flow(
action="bootstrap_agent_key",
email="user@example.com",
password="<securely-collected-password>",
agent_name="openspace-local-agent"
)
If secure password input is not available, ask the user to run:
openspace-cloud-auth bootstrap-agent-key --email user@example.com --agent-name openspace-local-agent
After setup, report only whether the key was saved and verified. Never print or repeat passwords, bearer tokens, or raw API keys.
execute_task
Delegate a task to OpenSpace. It will search for relevant skills, execute, and auto-evolve skills if needed.
execute_task(task="Monitor Docker containers, find the highest memory one, restart it gracefully", search_scope="all")
| Parameter | Required | Default | Description |
|---|
task | yes | — | Task instruction in natural language |
search_scope | no | "all" | Local + cloud; falls back to local-only if no API key |
max_iterations | no | 20 | Max agent iterations — increase for complex tasks, decrease for simple ones |
Check response for evolved_skills. If present with upload_ready: true, decide whether to upload (see "When to upload" below).
{
"status": "success",
"response": "Task completed successfully",
"evolved_skills": [
{
"skill_dir": "/path/to/skills/new-skill",
"name": "new-skill",
"origin": "captured",
"change_summary": "Captured reusable workflow pattern",
"upload_ready": true
}
]
}
search_skills
Search locally installed skills before deciding whether to handle a task yourself or delegate.
search_skills(query="docker container monitoring")
| Parameter | Required | Default | Description |
|---|
query | yes | — | Search query (natural language or keywords) |
limit | no | 20 | Max results |
Use search_skills for local discovery. If you need cloud results, use cloud_browse_skills so you can inspect packages and choose the skill explicitly.
cloud_browse_skills
Use this single stepwise tool for LLM-guided cloud package/skill selection. Continue calling the same tool with the returned next_actions[].action.
Recommended flow:
cloud_browse_skills(
action="search_skills",
query="browser login automation",
limit=5
)
Inspect results[]: each item has cloud_skill_id, title, summary, package_id, and package_path.
Use package discovery only when you need package outlines before choosing a skill:
cloud_browse_skills(
action="recall",
query="browser login automation",
limit=5
)
cloud_browse_skills(
action="pull_projection",
search_id="<search_id>",
package_ids=["<package_id>"]
)
Inspect pulls[].packages[], pulls[].skills[], projection_hash, and root_package_path. This is JSON projection, not the real zip files.
If you need concrete skill ranking inside one package, use the scoped skill-first search:
cloud_browse_skills(
action="search_skills",
package_id="<package_id>",
query="browser login automation"
)
Before import, optionally inspect exact metadata:
cloud_browse_skills(action="fetch_skill_detail", cloud_skill_id="<cloud_skill_id>")
Choose or create the local taxonomy path:
cloud_browse_skills(
action="local_placement",
query="browser login automation"
)
cloud_browse_skills(
action="local_placement",
local_category_path="technology/computing/browser-automation"
)
Inspect existing_path_candidates, new_child_path_examples, and
local_category_path_policy. You may choose an existing path or create a nearby
new child path. For DERIVED/CAPTURED suggestions, put the selected path in
local_category_path.
Import the exact chosen cloud skill with the selected local path:
cloud_browse_skills(
action="import_skill",
cloud_skill_id="<cloud_skill_id>",
local_category_path="technology/computing/browser-automation/login"
)
Choose local_category_path as a local package taxonomy path. It uses the same
classification style as cloud package paths, but is stored independently. It can
start from a cloud-like path and diverge with finer local child paths.
If the package outline or bundled artifacts are needed, import the package bundle explicitly:
cloud_browse_skills(action="import_package_bundle", package_id="<package_id>")
Do not use package bundle import as the default search step. Use it only after a package has been selected and you need package outline files or bundled artifacts.
fix_skill
Run a manual FIX job for a broken skill through OpenSpace evolution. The tool first creates a TriggerJob, then asks OpenSpace to drain that exact job through the evolution engine. It never directly edits the skill.
fix_skill(
skill_dir="/path/to/skills/weather-api",
direction="The upstream endpoint path changed; update all URLs and add the new 'units' parameter"
)
| Parameter | Required | Description |
|---|
skill_dir | yes | Path to skill directory (must contain SKILL.md) |
direction | yes | What's broken and how to fix — be specific |
Only treat the skill as repaired when status is fixed. If the result is accepted_audit_only, rejected, or failed, do not call upload_skill automatically; report the job/action IDs and reason to the user.
upload_skill
Upload a trusted skill to the cloud community. Public and private uploads both require a matching trusted record in the local SkillStore; provisional and unknown skills remain local. For committed evolved skills, lineage metadata is pre-saved; provide skill_dir and visibility. For non-fix uploads without pre-saved placement, use upload_skill as a step-by-step cloud package picker before uploading. The cloud path is separate from the local local_category_path.
Interactive cloud placement flow:
- Call
upload_skill(skill_dir=...) without cloud_package_path; inspect domain_index.sub_domain_nodes and interaction_flow.
- Call
upload_skill(skill_dir=..., cloud_sub_domain_package_id=...); inspect one bounded subtree.
- Choose either
subtree.selectable_regular_packages[].package_path, or create one new child path by appending one segment under subtree.creatable_parent_packages[].package_path.
- Call
upload_skill(skill_dir=..., visibility=..., cloud_package_path=...); the tool resolves the path to confirmed UUID placement, saves .upload_meta.json, revalidates, then uploads.
New cloud package paths are allowed, but only as one new regular package segment under an eligible parent. Do not upload directly to domain/sub-domain paths, and do not try to create multiple missing segments in one upload.
upload_skill(
skill_dir="/path/to/skills/weather-api",
visibility="private",
cloud_package_path="Technology/Computing/API clients"
)
| Parameter | Required | Default | Description |
|---|
skill_dir | yes | — | Path to skill directory (must contain SKILL.md) |
visibility | no | "private" | "public" or "private" |
cloud_package_path | for non-fix uploads without saved placement | auto | Agent-selected existing regular package path, or one new child regular package segment under an eligible parent |
cloud_sub_domain_package_id | no | — | Browse one upload subtree before selecting/creating cloud_package_path |
cloud_package_query | no | — | Filter cloud package picker results |
cloud_package_path_prefix | no | — | Expand/filter one cloud path prefix |
cloud_package_limit | no | 12 | Maximum picker rows returned |
origin | no | auto | How the skill was created |
parent_local_skill_ids | no | auto | Local parent skill IDs; OpenSpace resolves cloud parent IDs before upload |
When to upload
| Situation | Action |
|---|
| Skill is provisional or missing from SkillStore | Keep it local; use it successfully until it becomes trusted |
| Skill was originally from the cloud | Upload as "private" unless the user explicitly asks to share the improvement |
| Trusted fix/evolution is generally useful | Upload as "private" during broader testing; use "public" only with explicit sharing intent |
| Fix/evolution is project-specific | Upload as "private", or skip |
| User says to share | Upload with the visibility the user wants |
Notes
execute_task may take minutes — this is expected for multi-step tasks.- If
execute_task times out, first check the host's MCP timeout settings. Changing from stdio to HTTP (sse or streamable-http) does not remove host-side per-call time limits.
upload_skill requires a cloud API key; if it fails, the evolved skill is still saved locally.SKILL_NOT_TRUSTED, SKILL_TRUST_UNKNOWN, and SKILL_RECORD_PATH_MISMATCH stop locally before cloud package browsing or upload.- After every OpenSpace call, tell the user what happened: task result, any evolved skills, and your upload decision.
