AC doubleword-batches
Create and manage batch inference jobs using the Doubleword API (api.doubleword.ai). Use when users want to: (1) Process multiple AI requests in batch mode, (2) Submit JSONL batch files for async inference, (3) Monitor batch job progress and retrieve results, (4) Work with OpenAI-compatible batch endpoints, (5) Handle large-scale inference workloads that don't require immediate responses, (6) Use tool calling or structured outputs in batches, (7) Automatically batch API calls with autobatcher.
As a process C 57/100 · Has gaps — weak spots: when it triggers, consistency, running it twice
How to improve
- 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 · 4
✓ No critical or high findings
Medium and low: 4
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low Secrets in code
secret-high-entropy-tokenreferences/pricing.md:16High-entropy token-like string (may be an id, hash or a credential)### Qwen…FP8 (Mid-Size Model)
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low Secrets in code
secret-high-entropy-tokenreferences/pricing.md:32High-entropy token-like string (may be an id, hash or a credential)### Qwen…FP8 (Flagship Model)
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low Secrets in code
secret-high-entropy-tokenSKILL.md:30High-entropy token-like string (may be an id, hash or a credential)**Qwen…FP8** (mid-size):
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low Secrets in code
secret-high-entropy-tokenSKILL.md:35High-entropy token-like string (may be an id, hash or a credential)**Qwen…FP8** (flagship):
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Create and manage batch inference jobs using the Doubleword API (a… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 57/100
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 8 mutating operations with no state check
- 40Consistency. Frontmatter name (doubleword-batches) differs from the folder (doubleword)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 93 steps, 1 vague phrases
- 100Execution cost. Instruction body is 3331 tokens
- low 12 top-level sections: this looks like several domains in one skill
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)
- +1No license
- +2Single-language instructions
- +3Description length 498: enough signal without eating the budget
- +4Structure: 24 headings
- +3Step-by-step instructions: 93 items
- +3Output format is stated explicitly
- +4Has examples (18 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.