audio-track-production

End-to-end audio production workflow with stems, effects, archiving, and verification

Quality

68%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./benchmarks/gdpval/skills/audio-track-production/SKILL.md

Quality

Content

77%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is highly actionable and clearly sequenced with strong validation checkpoints, making it a strong execution guide. Its main weakness is token efficiency and progressive disclosure: a duplicated full example and an all-inline structure that a multi-step skill this size could offload to reference files.

Suggestions

Remove or condense the 'Complete Workflow Example' since it duplicates Steps 1-7, or move it to a separate reference file and link to it from the body.

Fix the inline NameError in Step 5 (stem_audio = np.zeros_like(stem_audio) references an undefined variable) so the per-step blocks run as written.

Extract the per-step recipes or the verification spec template into reference files and signal them from a concise SKILL.md overview to improve progressive disclosure.

Dimension	Reasoning	Score
Conciseness	The body is mostly lean executable code without concept-explanation padding, but the 'Complete Workflow Example' largely re-implements the same logic as the per-step blocks, adding redundancy that could be tightened.	2 / 3
Actionability	Each step supplies specific, executable Python with concrete parameters (48000 Hz, FLOAT subtype, 137s, named stems), and the closing 'Complete Workflow Example' is a self-contained, copy-paste-ready script.	3 / 3
Workflow Clarity	The seven steps are clearly sequenced and each risky/batch operation has explicit validation checkpoints — export asserts on sample rate/subtype, a verify_outputs pass/fail report, archive content listing, and a final 'failed == 0' assertion forming a fix-and-retry feedback loop.	3 / 3
Progressive Disclosure	The skill is well-sectioned but monolithic at ~330 lines with no bundle files or external references, and content that could be split out (the full end-to-end example) is kept inline, so it does not earn the simple-skill exception.	2 / 3
	Total	10 / 12 Passed

Description

60%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is concrete about capabilities but reads as a feature list rather than a triggered skill. It omits explicit 'Use when...' guidance, which caps both completeness and trigger quality.

Suggestions

Add an explicit trigger clause, e.g. 'Use when producing a master track and stems, applying effects, or delivering archived, verified audio outputs.'

Broaden natural trigger terms to include common user phrasings like 'mixing', 'mastering', and 'rendering tracks'.

Reframe the generic 'archiving, and verification' tail in audio-specific terms (e.g. 'package stems in a deliverable zip and assert output specs') to sharpen distinctiveness.

Dimension	Reasoning	Score
Specificity	The description enumerates multiple concrete actions/outputs — 'stems, effects, archiving, and verification' — naming the audio-production domain with specific deliverables rather than vague abstractions.	3 / 3
Completeness	It clearly states what the skill does, but there is no 'Use when...' clause or equivalent trigger guidance, so the 'when' half is missing and completeness is capped at 2 per the rubric guidelines.	2 / 3
Trigger Term Quality	Relevant domain keywords like 'audio production', 'stems', 'effects', and 'archiving' are present, but coverage of natural variations a user might say (e.g. 'mixing', 'mastering', 'tracks', 'render') is incomplete.	2 / 3
Distinctiveness Conflict Risk	The audio-production-with-stems core is a recognizable niche, but the generic 'archiving, and verification' tail overlaps with many other skills and the lack of explicit triggers leaves some conflict risk.	2 / 3
	Total	9 / 12 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: HKUDS/OpenSpace
Path: benchmarks/gdpval/skills/audio-track-production/SKILL.md
Commit: c5a9c4b

Reviewed: 2 days ago

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