Content
77%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, highly actionable skill for creating and optimizing Tessl skills. Its greatest strengths are the clear multi-phase workflow with explicit validation checkpoints, concrete CLI commands, and the comprehensive integrated example. The main weakness is verbosity — the skill could be more concise by reducing redundancy between non-negotiables and anti-patterns, and by leaning more heavily on the referenced companion files rather than summarizing their content inline.
Suggestions
Reduce redundancy by removing anti-patterns that merely restate non-negotiables (e.g., 'Modifying skill files during eval execution' duplicates non-negotiable #6) — or consolidate into a single authoritative list.
Move detailed fallback implementations (Phase 4 Path B LLM-as-Judge, Phase 3 Path M manual scenario authoring) into the referenced companion rule files to reduce SKILL.md length and better leverage progressive disclosure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is comprehensive but verbose at ~400+ lines. Some sections (like the full interview table, detailed CLI pipeline steps, and the integrated example) earn their place, but there's redundancy — non-negotiable #6 is restated multiple times, and some explanations could be tightened. The anti-patterns section partially duplicates non-negotiables. | 2 / 3 |
Actionability | Highly actionable throughout: specific CLI commands (`tessl tile lint`, `tessl scenario generate`), concrete file structures (tile.json schema, criteria.json with exact format), exact interview questions with fallback logic, and a complete integrated example showing input-to-output. Code snippets are copy-paste ready. | 3 / 3 |
Workflow Clarity | Excellent multi-step workflow with clear phase sequencing (Interview → Scaffold → Lint → CLI pipeline → Eval → Optimize → Benchmark). Validation checkpoints are explicit (lint check after scaffold, completeness check after interview, gate check after eval). Feedback loops are well-defined (Phase 5.3 apply → re-eval → log → flag regressions). Non-negotiable #6 establishes a clear read-only boundary during eval execution. | 3 / 3 |
Progressive Disclosure | References to companion rule files (scaffold-rules.md, activation-design.md, benchmark-loop.md, eval-runner.md) are well-signaled and one-level deep, which is good. However, no bundle files were provided, so we cannot verify these references resolve. The main SKILL.md itself is quite long and some content (like the full Phase 3 scenario schema or Phase 4 fallback details) could arguably live in the referenced rule files rather than being duplicated/summarized inline. | 2 / 3 |
Total | 10 / 12 Passed |