book-sft-pipeline
This skill should be used when the user asks to "fine-tune on books", "create SFT dataset", "train style model", "extract ePub text", or mentions style transfer, LoRA training, book segmentation, or author voice replication.
Install with Tessl CLI
npx tessl i github:muratcankoylan/Agent-Skills-for-Context-Engineering --skill book-sft-pipelineOverall
score
71%
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
54%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 description is essentially a list of trigger terms without any explanation of what the skill actually does. While it excels at providing natural keywords users might say and occupies a distinct niche, it completely fails to describe the skill's capabilities, making it impossible for Claude to understand what actions this skill enables.
Suggestions
Add a clear 'what' statement at the beginning describing concrete capabilities, e.g., 'Processes ePub files to extract and segment text, then formats content into supervised fine-tuning datasets for training style-transfer LoRA models.'
Restructure to lead with capabilities before the 'Use when...' trigger clause, following the pattern: '[What it does]. Use when [triggers].'
Specify the outputs or deliverables the skill produces (e.g., 'generates JSONL training files', 'creates conversation pairs for SFT').
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain (SFT dataset creation, ePub processing, style transfer) and mentions several actions like 'fine-tune on books', 'create SFT dataset', 'extract ePub text', but doesn't comprehensively list what concrete actions the skill actually performs. | 2 / 3 |
Completeness | The description only addresses 'when' (trigger conditions) but completely fails to explain 'what' the skill actually does. There's no explanation of the skill's capabilities or outputs - it's entirely composed of trigger phrases without describing functionality. | 1 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms users would say: 'fine-tune on books', 'create SFT dataset', 'train style model', 'extract ePub text', 'style transfer', 'LoRA training', 'book segmentation', 'author voice replication' - these are realistic phrases users would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | The combination of ePub processing, SFT dataset creation, LoRA training, and author voice replication creates a very specific niche that is unlikely to conflict with other skills. The triggers are highly distinctive. | 3 / 3 |
Total | 9 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, actionable skill with executable code for each pipeline phase and good progressive disclosure through external references. The main weaknesses are verbosity in the 'Integration with Context Engineering Skills' section (which explains relationships Claude doesn't need) and missing intermediate validation checkpoints between pipeline phases that could catch errors earlier.
Suggestions
Remove or significantly condense the 'Integration with Context Engineering Skills' section - it explains conceptual relationships rather than providing actionable guidance
Add explicit validation checkpoints between phases, e.g., 'Verify extraction produced >50k words before proceeding to segmentation' and 'Validate chunk count is within expected range (200-400 for 80k word book)'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is comprehensive but includes some unnecessary sections like 'Integration with Context Engineering Skills' which adds ~200 lines explaining how this skill relates to other skills - information Claude doesn't need to execute the pipeline. The core pipeline content is reasonably efficient. | 2 / 3 |
Actionability | Provides fully executable Python code for each phase (extraction, segmentation, instruction generation, dataset building, training). Code examples are copy-paste ready with specific libraries, parameters, and expected outputs clearly defined. | 3 / 3 |
Workflow Clarity | The 6-phase pipeline is clearly sequenced with a visual diagram, but validation checkpoints are only at the end (Phase 6) rather than integrated throughout. Missing explicit validation steps between phases - e.g., no 'verify extraction succeeded before segmenting' or 'validate chunk boundaries before instruction generation'. | 2 / 3 |
Progressive Disclosure | Well-structured with clear sections progressing from concepts to implementation to validation. References to external files (segmentation-strategies.md, tinker-format.md, tinker.txt) are clearly signaled and one level deep. Content is appropriately split between overview and detailed references. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 13 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata' field is not a dictionary | Warning |
license_field | 'license' field is missing | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 13 / 16 Passed | |
- Reviewed
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