SKILLEMALL.ai
GitHub ↗ RU Sign in

AD codex-auth-fallback

Set up OpenClaw multi-provider auth with OpenAI Codex OAuth fallback profiles and automatic model switching. Use when configuring multiple OpenAI Codex accounts for rate-limit failover, adding new Codex OAuth profiles via device flow, or setting up a cron job to auto-switch models when a provider hits cooldown.

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files · 1 script body ≈ 1 333 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Set up OpenClaw multi-provider auth with OpenAI Codex OAuth fallback profiles and automatic model switching.

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

IntegrationSoftware developmenttype 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Progress reporting w 2
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "requires"
    • note frontmatter-key unknown frontmatter key "files_read"
    • note frontmatter-key unknown frontmatter key "files_write"

    Process rating: all ten parameters 49/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 5 mutating operations with no state check
    • 40Consistency. Frontmatter name (codex-auth-fallback) differs from the folder (codex-multi-subscription-auth-fallbacks)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 34 steps, 2 vague phrases
    • 100Execution cost. Instruction body is 1333 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 312: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 34 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.