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AC kilocode-model-sync

Sync the Kilocode provider model list in openclaw.json with the live Kilo AI API. Use when: running the weekly model sync job, checking for new/removed/updated Kilocode models, applying approved model patches to openclaw.json, or reporting sync results to mission control and Telegram. Requires KILOCODE_API_KEY in ~/.openclaw/.env.

ClawHub Agent Skills author: Guillaume Maka v1.0.0 MIT-0 4 files body ≈ 1 053 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

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

IntegrationTelegramInfrastructureData 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
A
94/100
safety, quality, tests
Safety 60%
99
Quality 40%
87
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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 Exfiltration read-dotenv SKILL.md:26
      Reads a .env file
      source ~/.openclaw/.env && python3 ~/.openclaw/workspace/skills/kilocode-model-sync/scripts/sync_models.py

    Files scanned: 4. 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 60/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 5 mutating operations with no state check
    • 85Steps. 14 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1053 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 332: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 14 items
    • +4Has examples (7 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: suspicious
    This deprecated skill is not malware, but it can change OpenClaw configuration and restart the gateway after loosely scoped approval signals.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026