Content
92%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is highly actionable and token-efficient, with executable code, concrete tables, a routing decision tree, and a sequenced remote-compute workflow backed by a troubleshooting feedback loop. Its main weakness is structure rather than content: everything lives in one file with no progressive disclosure of the detailed remote-compute and troubleshooting material into separate references.
Suggestions
Move the remote-compute orchestration (submit/wait/attach flow and HF_HOME/HF_HUB_OFFLINE notes) into a references/ file such as REMOTE_COMPUTE.md, leaving a one-line pointer in SKILL.md so the main file reads as an overview.
Extract the troubleshooting and typical-performance tables into a references/ file (e.g. TROUBLESHOOTING.md) referenced from the body, reducing inline tokens while keeping error-recovery guidance one level deep.
If keeping the skill single-file, add an explicit validation checkpoint for batch scoring (e.g. 'confirm ll values are finite and in expected range before computing Δll') to strengthen the workflow feedback loop.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean — prerequisites/install/scoring/generation are given as compact tables and executable snippets, and prose only adds non-obvious domain guidance (e.g. 'More negative ⇒ less likely', HF_HOME/HF_HUB_OFFLINE rationale). It assumes Claude's competence and avoids explaining what DNA or libraries are, matching the 'every token earns its place' anchor. | 3 / 3 |
Actionability | Provides copy-paste-ready executable code ('from evo2 import Evo2', 'model.score_sequences(seqs)', 'model.generate(...)') plus concrete install, model-selection, and remote-compute submission snippets with specific VRAM/timing numbers. Fully instructs rather than describes; matches the level-3 anchor. | 3 / 3 |
Workflow Clarity | Sequences are explicit: a decision tree routes to the right model, and the remote-compute flow is step-sequenced (submit_job → wait_for_notification → save_artifacts → attach_job). The troubleshooting table supplies a symptom→cause→fix error-recovery feedback loop. Not level 2 because checkpoints (notification, error recovery) are present rather than implicit; operations are non-destructive inference so strict validate-before-proceed is less critical. | 3 / 3 |
Progressive Disclosure | Well-organized into clear headered sections, but it is a monolithic ~125-line file with no bundle references (references/scripts/assets absent); detailed material such as the remote-compute orchestration and the troubleshooting/performance tables is inline where separate reference files would let SKILL.md act as an overview. Fits the level-2 anchor ('content that should be separate is inline') rather than level 3, which expects well-signaled one-level-deep references and appropriately split content. | 2 / 3 |
Total | 11 / 12 Passed |