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BC swarm-layer

OpenClaw Swarm Layer: spec-driven workflow orchestration with ACP-first execution, supervised autopilot control plane, manual fallback, persistent sessions, review gates, automatic retry, parallel dispatch, reject-retry workflows, ACP concurrency protection, harness patterns, cross-session continuity, and operator reporting. Covers setup, operation, diagnosis, and reporting.

ClawHub Agent Skills author: Jerry v0.5.12 MIT-0 2 files body ≈ 4 052 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

OpenClaw Swarm Layer: spec-driven workflow orchestration with ACP-first execution, supervised autopilot control plane, manual fallback, persistent sessions…

As a process C 60/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

ProcedureGitHubObsidianData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
60/100
Has gaps
When it triggers w 12
20
Running it twice w 4
30
Result and completion w 14
40
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 192, 350): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 60/100

    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 11 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, git, node) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4052 tokens
    • 85Steps. 32 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Progress reporting. Reports progress
    • low 17 top-level sections: this looks like several domains in one skill

    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 377: enough signal without eating the budget
    • +4Structure: 40 headings
    • +3Step-by-step instructions: 32 items
    • +4Has examples (24 code blocks)

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

    External checks

    ClawHub: suspicious
    The skill’s workflow automation is mostly disclosed and purpose-aligned, but its unpinned external plugin install paths and long-running automation controls should be reviewed carefully before use.
    LLM: suspicious (medium) · VirusTotal: · 14 Sept 2026