aligned-stem-workflow

Incremental audio production with duration alignment handling, per-stem verification, and adaptive extension strategies

Quality

63%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./benchmarks/gdpval/skills/audio-track-production-enhanced-enhanced/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 content is a highly actionable, well-sequenced workflow with strong per-step validation, but it is monolithic and somewhat padded, which holds back conciseness and progressive disclosure. Splitting the bulk into reference files and trimming boilerplate would raise the weaker dimensions.

Suggestions

Move the large per-step code blocks and the troubleshooting/strategy guidance into separate reference files (e.g. STEMS.md, ALIGNMENT.md) linked from a concise overview to improve progressive disclosure toward score 3.

Strip decorative print/logging lines and the redundant 'Key Differences from Standard Workflow' section to tighten conciseness without losing clarity.

Keep the excellent verification/fail-fast pattern intact; it is the strongest part of the workflow and should anchor the slimmed-down overview.

Dimension	Reasoning	Score
Conciseness	The body is dominated by concrete, runnable code, but it is padded with pervasive print/logging statements, a 'Key Differences from Standard Workflow' section that restates the Overview, and introductory prose, fitting 'mostly efficient but includes some unnecessary explanation or could be tightened' rather than the lean score-3 anchor.	2 / 3
Actionability	Every step ships complete, executable Python using real imports (numpy, soundfile, scipy) with full function bodies and a working end-to-end pipeline, matching 'fully executable code/commands; specific examples; copy-paste ready'.	3 / 3
Workflow Clarity	Eight steps are presented in strict order, each stem is verified immediately after generation with explicit fail-fast 'raise RuntimeError' checkpoints, and final verification closes the loop, matching 'clear sequence with explicit validation steps; feedback loops for error recovery'.	3 / 3
Progressive Disclosure	Sections are clearly organized, but the skill is a monolithic ~684-line SKILL.md with no bundle files and no one-level-deep references, so large code blocks and the troubleshooting/strategy material that should be separate remain inline; it exceeds the under-50-line simple-skill exception, so it does not reach score 3.	2 / 3
	Total	10 / 12 Passed

Description

50%

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 specific about its audio-production niche and key features, but it is written as a noun phrase with no 'Use when...' trigger guidance and leans on technical jargon over natural user terms. This places it solidly at the midpoint across all four dimensions.

Suggestions

Rewrite as verb-led third-person actions, e.g. 'Generates, verifies, and aligns audio stems into a master track' to lift specificity toward score 3.

Add an explicit 'Use when...' clause with natural trigger terms users would actually say (e.g. stems, mixing, song production, aligning stem durations).

Replace jargon like 'adaptive extension strategies' with concrete user-facing terms (zero-pad, loop, crossfade stems) to improve trigger term quality.

Dimension	Reasoning	Score
Specificity	Names the domain and several concrete features ('duration alignment handling, per-stem verification, and adaptive extension strategies'), but as noun phrases rather than verb-led actions, matching the 'names domain and some actions, but not comprehensive' anchor and falling short of the multi-action verb-led score-3 example.	2 / 3
Completeness	The description clearly states WHAT the skill does but contains no 'Use when...' clause or equivalent trigger guidance, so per the judging guidelines completeness is capped at 2 ('Has what, but when is missing or only implied').	2 / 3
Trigger Term Quality	'audio production' is a natural term, but 'duration alignment handling' and 'adaptive extension strategies' are technical jargon, and common user variations (stems, mixing, music, song) are missing, fitting 'some relevant keywords but missing common variations' rather than the full natural-term coverage of score 3.	2 / 3
Distinctiveness Conflict Risk	The stem/duration-alignment niche is fairly specific, but without explicit triggers it could still overlap a more general audio or music production skill, matching 'somewhat specific but could still overlap with similar skills' rather than the clearly-distinct score-3 anchor.	2 / 3
	Total	8 / 12 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (684 lines); consider splitting into references/ and linking	Warning

	Total	15 / 16 Passed

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

Reviewed: 2 days ago

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