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leg-review-spec-from-code-oracle

Review one linked Oracle Forms legacy-evidence package produced by leg-spec-from-code-oracle for semantic relationships, cross-fact ambiguities, apparent conflicts, missing runtime context, and source-supported reconciliations. Use after Oracle code evidence extraction and before target requirement curation when the master specification and its operation, decoded-source, and database children need a human-readable semantic review overlay without modifying extracted evidence or generating target requirements, UI designs, tests, or implementation decisions.

68

Quality

81%

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

70%Scale 1-5

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

This is a well-structured, domain-specific skill for a complex review process with strong workflow clarity and reasonable actionability. Its main weaknesses are moderate verbosity (could be 30% shorter without losing clarity) and reliance on bundle files that aren't provided, making progressive disclosure hard to fully evaluate. The non-negotiable boundaries and conservative resolution criteria are well-articulated and add genuine value.

Suggestions

Provide the referenced bundle files (review-lenses.md, semantic-review-template.md, oracle_semantic_review.py) so the skill's progressive disclosure can be properly evaluated and the actionability of delegated steps is verifiable.

Tighten the relationship checklist in Step 2 by using a more compact format (e.g., a simple bullet list without the verbose 'X versus Y and Z' pattern) or move it to a reference file.

Consider moving the detailed validation failure conditions list from Step 7 into a reference file or the validation script itself, keeping only a summary in the main skill body.

DimensionReasoningScore

Conciseness

The skill is detailed and domain-specific, providing information Claude wouldn't inherently know. However, it's quite lengthy (~200 lines) with some repetitive phrasing and could be tightened—e.g., the review relationship checklist in Step 2 is exhaustive but could be more compact, and some boundary rules restate what's implied by others. It doesn't over-explain basic concepts, but it's not lean either.

3 / 5

Actionability

The skill provides concrete PowerShell commands, specific file paths, exact finding types/statuses/severities, and clear resolution criteria. The validation step has explicit failure conditions. Minor gaps: the referenced scripts and template files are not provided in the bundle, so the actual executable behavior depends on external assets that aren't visible, and some steps like 'Apply every lens in references/review-lenses.md' delegate specifics to an unavailable file.

4 / 5

Workflow Clarity

The 7-step workflow is clearly sequenced with explicit validation checkpoints (Step 7), a preparation/freeze step (Step 1), conservative resolution criteria (Step 4), and a rerun/staleness handling step (Step 6) that acts as a feedback loop. Validation has explicit failure conditions enumerated. The scaffold-vs-reconcile branching is clearly defined. This is a thorough workflow for a complex, non-destructive review process.

5 / 5

Progressive Disclosure

The skill references external files (references/review-lenses.md, references/semantic-review-template.md, scripts/oracle_semantic_review.py) which is good progressive disclosure design, but none of these bundle files are provided, making it impossible to verify they exist or are well-structured. The SKILL.md itself is quite long and some content (like the full relationship checklist or the complete validation failure conditions) could potentially be moved to reference files. The structure within the file is good with clear sections.

3 / 5

Total

15

/

20

Passed

Description

92%Scale 1-5

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

This is a strong, well-crafted description that precisely defines a narrow role in a multi-step Oracle Forms migration pipeline. It excels at completeness by explicitly stating what it does, when to use it, and what it does not do. The only minor weakness is that the trigger terms are heavily jargon-laden, which could reduce discoverability if a user phrases their request in more casual language.

DimensionReasoningScore

Specificity

The description lists multiple specific concrete actions: reviewing semantic relationships, cross-fact ambiguities, apparent conflicts, missing runtime context, and source-supported reconciliations. It also specifies the exact input artifact type (Oracle Forms legacy-evidence package) and the producing skill (leg-spec-from-code-oracle).

5 / 5

Completeness

Clearly answers both 'what' (review semantic relationships, ambiguities, conflicts, missing context, reconciliations) and 'when' (after Oracle code evidence extraction, before target requirement curation, when master specification and its children need semantic review). It also explicitly states what it does NOT do (modify evidence, generate requirements, UI designs, tests, or implementation decisions), which strengthens the 'when' guidance.

5 / 5

Trigger Term Quality

Includes domain-specific terms like 'Oracle Forms', 'legacy-evidence', 'semantic review', 'master specification', 'decoded-source', and 'database children' that would match relevant queries. However, the language is heavily specialized jargon; natural user phrases like 'review extracted code evidence' or 'check for conflicts in Oracle analysis' are not explicitly included.

4 / 5

Distinctiveness Conflict Risk

Extremely distinct niche: it explicitly names the upstream skill (leg-spec-from-code-oracle), specifies its position in a pipeline (after extraction, before curation), and clearly delineates what it excludes. This makes it highly unlikely to conflict with other skills in a large skill library.

5 / 5

Total

19

/

20

Passed

Validation

100%

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

Validation11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository
sibendu/ai_tessl
Reviewed

Table of Contents

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