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AC update_models

Audit AI model providers in sources.js against live provider APIs. Detects deprecated, new, and config-changed models. Uses lightweight audit state, JSON-based researcher output, and streamlined diff application. Trigger with /skill:update_models or when user says "update models", "audit models", "check models", "verify providers", "bump models".

The skillemall take

Audits your sources.js against live provider APIs to find deprecated, new, and reconfigured models. Single file, 2467 tokens, no scripts. Quality score 87, no critical findings. No sandbox or model runs yet. Triggers on "/skill:update_models" or phrases like "update models", "verify providers". Supports nine platforms including Claude and Cursor.

Does exactly what it claims: lightweight diff without bloat. Install if you maintain a model list and need it to stay current.

vava-nessa/free-coding-models Agent Skills author: vava-nessa 1 file body ≈ 2 467 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Audit AI model providers in sources.js against live provider APIs. Detects deprecated, new, and config-changed models. Uses lightweight audit state…

As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerGitHubCloudflareSoftware developmentData and analyticsAI 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
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
50/100
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 · 0

    ✓ No critical or high findings

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

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note edit-residue the text marks something as outdated (lines 135, 211, 264, 266, 268): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 50/100

    • 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. 14 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (git) that frontmatter does not declare
    • 100Steps. 16 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2467 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 348: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 16 items
    • +3Output format is stated explicitly
    • +4Has examples (11 code blocks)

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