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AB ganglion

Use for every task involving this project. Covers running Ganglion, its CLI commands, HTTP bridge API, pipeline execution, knowledge queries, configuration, and operational workflows. Trigger phrases: 'run the pipeline', 'start the server', 'check status', 'query knowledge', 'configure', 'call the API', 'scaffold a project', 'check metrics', 'rollback', 'swap policy'.

ClawHub Agent Skills author: tensorlink-dev v0.1.0 MIT-0 9 files · 1 script body ≈ 3 205 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 67/100 · Nearly there — weak spots: when it triggers, running it twice

ProcedureAI and agentstype 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
A
96/100
safety, quality, tests
Safety 60%
99
Quality 40%
91
Run on models
none yet
Process rating
B
67/100
Nearly there
When it triggers w 12
20
Running it twice w 4
30
Failures and branches w 10
50
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Dangerous commands cmd-eval-dynamic references/troubleshooting.md:264
      Dynamic code execution from decoded/untrusted input (quoted — discussed, not commanded)
      python3 -c "exec(open('./my-subnet/tools/problem_file.py').read())"
      quoted

    Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 67/100

    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 24 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3205 tokens
    • 100Progress reporting. Reports progress

    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)
    • -2localhost URLs: will not work for another user
    • +1No license
    • +2Single-language instructions
    • +3Description length 370: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 24 items
    • +3Output format is stated explicitly
    • +4Has examples (15 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: suspicious
    Ganglion is a coherent operator skill, but it gives agents broad control over a local or remote execution bridge that can read project files, upload Python code, mutate pipelines, update prompts, run experiments, and roll back state.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026