qiskit
IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.
87
81%
Does it follow best practices?
Impact
99%
1.39xAverage score across 3 eval scenarios
Passed
No known issues
Quality
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 strong skill description that excels at disambiguation and trigger coverage within the quantum computing domain. It clearly states when to use this skill versus alternatives, which is critical in a multi-skill environment. The main weakness is that the 'what' portion could be more specific about concrete actions (e.g., circuit construction, job submission, transpilation) rather than high-level categories.
Suggestions
Add specific concrete actions like 'build and transpile quantum circuits, submit jobs to IBM backends, apply error mitigation techniques, optimize qubit routing' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (IBM quantum computing) and some actions like 'quantum error mitigation' and 'IBM hardware execution,' but doesn't list multiple concrete actions (e.g., 'build circuits,' 'run jobs,' 'transpile circuits'). It's more about categorizing use cases than listing specific capabilities. | 2 / 3 |
Completeness | Clearly answers both 'what' (IBM quantum computing framework for hardware execution, error mitigation, optimization tools) and 'when' (explicit 'Use when targeting IBM Quantum hardware, working with Qiskit Runtime...' clause with clear triggers and disambiguation guidance). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms: 'IBM Quantum,' 'Qiskit Runtime,' 'IBM hardware,' 'quantum error mitigation,' 'enterprise quantum computing,' plus cross-references to competing tools (cirq, pennylane, qutip) which help with negative matching. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with explicit disambiguation from competing quantum frameworks (cirq for Google, pennylane for gradient-based ML, qutip for open quantum systems). This makes it very unlikely to conflict with other quantum computing skills. | 3 / 3 |
Total | 11 / 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 well-structured skill with excellent progressive disclosure and actionable code examples. Its main weaknesses are some unnecessary marketing content in the overview and missing validation/error-handling steps in the workflows. The decision guide and reference organization are particularly strong for navigability.
Suggestions
Remove marketing statistics ('13M+ downloads', '83x faster', '29% fewer') from the overview—these don't help Claude execute tasks and waste tokens.
Add validation checkpoints to the Development Workflow and Common Patterns, such as checking transpilation output gate count, verifying job status before retrieving results, and handling convergence failures in VQE.
Trim the bulleted 'Topics covered' lists under each reference section—the reference file names and one-line descriptions are sufficient for navigation.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The overview includes some marketing-style content ('world's most popular', '13M+ downloads', '83x faster transpilation') that doesn't help Claude execute tasks. The topic lists under each reference section are somewhat redundant given the reference files exist. However, the code examples themselves are lean and the workflow decision guide is efficient. | 2 / 3 |
Actionability | The skill provides fully executable, copy-paste ready code examples for the most common patterns (simple execution, hardware execution, VQE). The Quick Start section is immediately runnable, and the Common Patterns section covers the three main use cases with complete code. | 3 / 3 |
Workflow Clarity | The Development Workflow section provides a clear sequence (simulators → transpile → choose primitives → choose execution mode) and the Common Patterns show progression from simple to complex. However, there are no explicit validation checkpoints—no 'verify transpilation succeeded' step, no error handling for failed hardware jobs, and no feedback loops for the variational algorithm convergence check. | 2 / 3 |
Progressive Disclosure | Excellent progressive disclosure structure: a concise overview with working Quick Start code, followed by clearly signaled one-level-deep references to 8 topic-specific files. The Workflow Decision Guide provides an effective navigation aid mapping user intent to the correct reference file. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 10 / 11 Passed | |
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
25e1c0f
- Reviewed
Table of Contents
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