Semantic workflow composer — parse natural language workflow description into a DAG of skill/CLI/agent nodes, auto-inject checkpoint save nodes, confirm with user, persist as reusable JSON template. Triggers on "wf-composer " or "/wf-composer".
56
65%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.claude/skills/wf-composer/SKILL.mdQuality
Discovery
67%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 excels at specificity and distinctiveness, clearly articulating a unique capability around workflow DAG composition. However, it relies on command-based triggers rather than natural language keywords users would use, and lacks an explicit 'Use when...' clause describing the scenarios that should activate this skill beyond the command prefix.
Suggestions
Add a 'Use when...' clause describing natural scenarios, e.g., 'Use when the user wants to create, compose, or automate a multi-step workflow, pipeline, or task chain.'
Include natural language trigger terms users would say, such as 'workflow', 'pipeline', 'automate tasks', 'chain steps', 'multi-step process', in addition to the command triggers.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: parse natural language into a DAG, auto-inject checkpoint save nodes, confirm with user, persist as reusable JSON template. These are detailed, concrete capabilities. | 3 / 3 |
Completeness | The 'what' is well-covered with specific actions. The 'when' is limited to command triggers ('wf-composer' or '/wf-composer') rather than describing natural use cases or scenarios. It doesn't have a 'Use when...' clause describing situations like 'when the user wants to create a multi-step workflow' or 'when chaining multiple skills together'. | 2 / 3 |
Trigger Term Quality | Includes the command triggers 'wf-composer' and '/wf-composer', but lacks natural language keywords users would say like 'workflow', 'pipeline', 'automate', 'chain tasks', 'DAG'. The triggers are command-based rather than natural language terms. | 2 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche — semantic workflow composition into DAGs with checkpoint injection. The specific command triggers and unique domain (workflow DAG composition) make it very unlikely to conflict with other skills. | 3 / 3 |
Total | 10 / 12 Passed |
Implementation
62%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill has strong workflow clarity with well-defined phases, success criteria, and user confirmation loops. However, it heavily delegates to external phase files and spec files that are not provided in the bundle, making the actual actionability dependent on unseen content. The SKILL.md itself serves as a good orchestration overview but lacks concrete executable examples within the body.
Suggestions
Include at least one concrete, executable example of a parsed workflow (e.g., a sample natural language input and the resulting template JSON output) directly in the SKILL.md to improve actionability.
Provide the referenced phase files (phases/01-parse.md, etc.) and spec files in the bundle, or inline the critical execution details for at least the most important phases to ensure the skill is self-sufficient.
Add a minimal working example showing the end-to-end flow from a simple natural language description to the final template JSON, so Claude can pattern-match on real data.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient and doesn't over-explain basic concepts, but includes some structural overhead that could be tightened (e.g., the shared constants table, the entry router table). The phase descriptions are concise but the overall document has moderate verbosity for what amounts to delegating to sub-files. | 2 / 3 |
Actionability | Each phase delegates to external files (phases/01-parse.md, etc.) with brief summaries of objectives and success criteria, but the SKILL.md itself contains no executable code, no concrete commands, and no copy-paste ready examples. The pipeline visualization is a good concrete example, but the actual execution details are deferred entirely to referenced files that aren't provided. | 2 / 3 |
Workflow Clarity | The multi-step workflow is clearly sequenced across 6 phases (0-5) with explicit success criteria for each phase, checkpoint injection rules, a confirmation/validation phase (Phase 4) with user approval before persisting, and resume/edit capabilities. The feedback loop in Phase 4 (edit → re-enrich → re-confirm) is well-defined. | 3 / 3 |
Progressive Disclosure | The skill references phase files (phases/01-parse.md through phases/05-persist.md) and spec files (specs/node-catalog.md, specs/template-schema.md) for deeper content, which is good progressive disclosure structure. However, none of these referenced files exist in the bundle, making it impossible to verify they support the content. The references are one-level deep and clearly signaled, but the missing bundle files prevent a score of 3. | 2 / 3 |
Total | 9 / 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 — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
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Table of Contents
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