clay-performance-tuning
Optimize Clay table enrichment throughput, reduce processing time, and improve hit rates. Use when experiencing slow enrichment, poor email find rates, or needing to process large tables efficiently. Trigger with phrases like "clay performance", "optimize clay", "clay slow", "clay throughput", "clay fast enrichment", "clay batch optimization".
80
77%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/saas-packs/clay-pack/skills/clay-performance-tuning/SKILL.mdQuality
Discovery
89%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 well-structured skill description that clearly identifies its niche (Clay platform enrichment optimization), provides explicit 'Use when' triggers, and includes a helpful list of trigger phrases. The main weakness is that the specific capabilities could be more granular—listing concrete optimization techniques rather than high-level goals would strengthen the specificity dimension.
Suggestions
Add more concrete actions beyond high-level goals, e.g., 'Configures batch sizes, parallelizes enrichment columns, sequences waterfall lookups, and tunes API rate limits for Clay tables.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Clay table enrichment) and some actions (optimize throughput, reduce processing time, improve hit rates), but doesn't list multiple concrete specific actions like batch sizing strategies, parallel processing, or specific optimization techniques. | 2 / 3 |
Completeness | Clearly answers both 'what' (optimize Clay table enrichment throughput, reduce processing time, improve hit rates) and 'when' (experiencing slow enrichment, poor email find rates, needing to process large tables) with explicit trigger phrases. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms including 'clay performance', 'optimize clay', 'clay slow', 'clay throughput', 'clay fast enrichment', 'clay batch optimization', plus natural phrases like 'slow enrichment', 'poor email find rates', and 'process large tables efficiently'. | 3 / 3 |
Distinctiveness Conflict Risk | Very distinct niche targeting Clay platform specifically with enrichment optimization. The 'Clay' product name combined with specific concepts like 'enrichment throughput' and 'hit rates' make this highly unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, actionable skill with concrete code examples, specific configurations, and a clear step-by-step structure for optimizing Clay table performance. Its main weaknesses are the lack of validation checkpoints between steps (e.g., testing with a small batch before full import) and some verbosity in the code examples that could be extracted to separate files. The error handling table is a nice practical addition.
Suggestions
Add a validation checkpoint after Step 2 (e.g., 'Test conditional rules on 10-20 rows before running the full table to verify conditions trigger correctly').
Extract the lengthy TypeScript pre-processing function into a separate referenced file (e.g., `clay-preprocess.ts`) and keep only a usage example inline.
Add a 'verify results' step at the end: compare hit rates and processing time before/after optimization to confirm improvements.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient but includes some unnecessary elements — the prerequisites section states obvious things ('Understanding of which providers are in your waterfall'), the Output section restates what was already covered, and some code examples (like the scheduler) are more verbose than needed. The TypeScript pre-processing function is quite long and could be tightened. | 2 / 3 |
Actionability | The skill provides fully concrete, executable guidance: real Clay formula syntax for conditional rules, complete TypeScript functions for pre-processing and scheduling, specific YAML configurations for waterfall optimization, and a detailed column ordering table with actual timing benchmarks. Everything is copy-paste ready or directly applicable in the Clay UI. | 3 / 3 |
Workflow Clarity | The six steps are clearly sequenced and logically ordered (fast columns first → conditional rules → pre-process data → limit waterfall → auto-update controls → scheduling). However, there are no explicit validation checkpoints or feedback loops — for instance, no step to verify that conditional rules are working correctly, no way to validate that the pre-processing actually improved hit rates, and no 'run a small test batch first' checkpoint before processing large imports. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear headers and a logical flow, and it references external resources and a related skill (clay-cost-tuning). However, the inline TypeScript pre-processing function is quite long (~50 lines) and could be split into a separate reference file. The skill is borderline monolithic at its current length. | 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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