SKILLEMALL.ai

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Auto-detect task complexity for OpenAI oAuth models (gpt-5.1-codex-mini + gpt-5.3-codex). Route only safe/negligible tasks to gpt-5.1-codex-mini. Use gpt-5.3-codex for anything executed, uncertain, or high-impact. RULES: Classify first. Default model: gpt-5.3-codex. Use gpt-5.1-codex-mini ONLY for safe triage/summarize/extract/reformat/dedupe/prompt-drafts/non-executable Q&A. NEVER use mini for security, auth, secrets, architecture, migrations, brownfield refactors, integration contracts, schema mapping, ordering/idempotency/retries, code changes, tool runs, or decisions that are hard to reverse. Reasoning: start LOW for mini. For 5.3-codex use LOW/MEDIUM by default; escalate to HIGH/EXTRA HIGH when 2+ are true: hard to reverse, affects 2+ domains (infra/data/security/ops/cost), subtle/expensive failure modes, long dependency-chain reasoning. ESCALATE immediately to gpt-5.3-codex if any: output will be executed (tools/code), ambiguity remains after 1 pass, contradictions found, or requirements are multi-constraint/structured output.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 1 054 tokens Open the sourcegithub.com analyzed 2 d ago

Auto-detect task complexity for OpenAI oAuth models (gpt-5.1-codex-mini + gpt-5.3-codex). Route only safe/negligible tasks to gpt-5.1-codex-mini. Use…

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

IntegrationSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
60
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Shorten the description to 1024 characters.
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

  • error description-long description is 1048 chars, limit 1024

Process rating: all ten parameters 55/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 100Steps. 29 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1054 tokens
  • 100Running it twice. No mutating operations
  • 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
  • +3Description length 1048: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 29 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.