groq-migration-deep-dive
Migrate from OpenAI/Anthropic/other LLM providers to Groq, or migrate between Groq model generations with zero-downtime traffic shifting. Trigger with phrases like "migrate to groq", "switch to groq", "groq migration", "openai to groq", "groq replatform".
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/groq-pack/skills/groq-migration-deep-dive/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 solid description with clear trigger terms and explicit 'when to use' guidance. Its main weakness is that the 'what' portion could be more specific about concrete actions (e.g., updating API calls, converting prompt formats, configuring traffic shifting). The Groq-specific focus makes it highly distinctive.
Suggestions
Add more concrete actions to improve specificity, e.g., 'Updates API endpoints, converts prompt formats, configures gradual traffic shifting between providers.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (LLM provider migration to Groq) and mentions some actions ('migrate', 'zero-downtime traffic shifting'), but doesn't list multiple concrete steps or detailed capabilities beyond the high-level migration concept. | 2 / 3 |
Completeness | Clearly answers both 'what' (migrate from other LLM providers to Groq or between Groq model generations with zero-downtime traffic shifting) and 'when' (explicit trigger phrases provided). The trigger guidance is explicit. | 3 / 3 |
Trigger Term Quality | Includes a strong set of natural trigger phrases users would actually say: 'migrate to groq', 'switch to groq', 'groq migration', 'openai to groq', 'groq replatform'. These cover common variations well. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive — specifically targets Groq migration with named source providers (OpenAI, Anthropic). Unlikely to conflict with generic migration or other LLM-related skills due to the Groq-specific focus. | 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, highly actionable migration guide with excellent concrete code examples and a thoughtful traffic-shifting strategy. Its main weaknesses are verbosity in the abstraction layer code (Step 3 could be halved), lack of explicit validation checkpoints between migration steps, and the monolithic structure that could benefit from splitting detailed code into referenced files.
Suggestions
Add explicit validation checkpoints: after code changes in Step 1, run the benchmark (Step 6) as a gate before proceeding to traffic shifting, and define pass/fail criteria (e.g., 'quality within 5% on your test suite').
Trim Step 3 by showing only the GroqProvider implementation and noting that OpenAIProvider follows the same interface pattern — Claude can infer the rest.
Move the full provider abstraction layer and benchmark code to a referenced file (e.g., MIGRATION_PATTERNS.md) and keep only the key snippets inline.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with good use of tables and code examples, but the provider abstraction layer (Step 3) is quite verbose with two full class implementations that are nearly identical. The OpenAIProvider class could be omitted since Claude can infer the pattern. The overview sentence about '10-50x faster inference' is slightly promotional. | 2 / 3 |
Actionability | Excellent executable code throughout — the OpenAI-to-Groq migration code is copy-paste ready, the model mapping is concrete, the migration scanner bash script is fully executable, and the benchmark code is complete. Every step provides specific, runnable examples. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced and the traffic shifting schedule is well-defined with a rollback plan. However, there are no explicit validation checkpoints between steps — e.g., after Step 1 migration there's no 'verify the response matches expected format' step, and the benchmark (Step 6) should be positioned as a mandatory validation gate before increasing traffic percentage, not a separate step. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear headers and tables, but it's quite long (~200 lines of code) and could benefit from splitting the provider abstraction layer and benchmark code into separate reference files. The reference to 'groq-upgrade-migration' at the end is good, but the main file tries to contain everything inline. | 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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