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BD oz-child-agent-orchestration

Internal Oz child-agent orchestration playbook for child runs. Invoke only when the Oz harness system prompt tells you to coordinate with a lead run through the Oz CLI in `OZ_CLI`, using `OZ_RUN_ID` and `OZ_PARENT_RUN_ID`.

The skillemall take

A skill for internal child-agent coordination in the Oz system—essentially a playbook for orchestrating parallel runs via CLI. Promises to activate only on command from the Oz system prompt, using OZ_RUN_ID and OZ_PARENT_RUN_ID environment variables.

Checks returned: grade B, quality 84%, safety 100%, no critical findings. One file, links intact, lint clean. Process score sits at 49%, though—a signal that orchestration logic either isn't fully exposed or hasn't been tested on models. No sandbox or model_run data, so no live execution happened.

If you're already in Oz: the skill passes basic checks, but without real tests it stays opaque. Install only if you know what it does and trust the Oz harness to invoke it correctly—otherwise you're handing off subprocess control blind.

warpdotdev/warp Agent Skills author: warpdotdev AGPL-3.0 1 file body ≈ 1 274 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Internal Oz child-agent orchestration playbook for child runs.

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 49/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 18 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 60Failures and branches. 2 branches
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1274 tokens
    • low The response is described with custom markup (5 tags): a typed call is more reliable

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 222: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 22 items
    • +4Has examples (0 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.