Content
77%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-crafted, expert-level workflow document: the eight-phase sequence is clearly ordered with an explicit pre-GPU validation checkpoint, error-recovery guidance, and parameter-level specificity (grammars, defaults, thresholds, published effect sizes) throughout. The main defects are the absence of any runnable example — the manifest that drives the entire pipeline is never shown — and reference files (`FILTERING.md`, `CAVEATS.md`) that are clearly signaled but missing from the bundle.
Suggestions
Ship the referenced files: create `references/FILTERING.md` and `references/CAVEATS.md` (or place them where the body's paths point) so the six in-body references resolve — currently the bundle contains only SKILL.md and navigation dead-ends at the two most-cited documents.
Add a minimal worked manifest example (even 10-15 lines of JSON for one campaign) plus the exact tool-call names for generation and resequencing, so a reader can go from Phase 1 decisions to a runnable `run_pipeline.py` invocation without guessing the schema.
Trim the duplicated Workflow Overview block and move the licence/prohibited-use paragraph to `CAVEATS.md` with a one-line pointer, keeping the in-body licence note to the non-commercial restriction that gates every campaign.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and expert-level — motif grammar diagram, parameter tables, published hit rates, exact thresholds — with no padding explaining concepts Claude already knows. It is not 5 because the Workflow Overview block partially duplicates the per-phase sections, the licence paragraph is long, and the "LOOK UP, DON'T GUESS" / "COMPUTE, DON'T DESCRIBE" boilerplate adds tokens without skill-specific payoff; it is well above 3 since nearly all content is non-obvious and specific. | 4 / 5 |
Actionability | Concrete, executable-level guidance throughout: script names (`run_generator.py`, `evaluate_design.py`), exact defaults (`num_sampling_steps: 1000`, LigandMPNN "temperature 0.1 with weights `v_32_030_25`"), a fully specified `motif_str` grammar with diagram, and MPD parameters ("re-noise the design by 575 of 1000 steps… require 9/10 to pass"). It is not 5 because no runnable artifact is shown — the manifest JSON that drives `run_pipeline.py` is never exemplified, and exact tool-call syntax for generation/resequencing is absent; it is not 3 because the parameter-level specificity far exceeds "some concrete guidance". | 4 / 5 |
Workflow Clarity | Eight phases are clearly sequenced with an explicit pre-GPU validation checkpoint (Phase 2: "A malformed `motif_str` caught here costs seconds; caught in Phase 6 it costs the campaign"), an explicit error-recovery loop ("If a campaign fails, re-examine the motif before re-tuning filters"), inter-phase dependency notes (Phase 5 must be self-arranged; Phases 6–7 depend on it), and a mandated cumulative funnel report. This matches the 5 anchor — clear sequence, explicit validation, feedback loops for a batch operation — rather than the 4 anchor's "most checkpoints present". | 5 / 5 |
Progressive Disclosure | References are one level deep and clearly signaled ("See `FILTERING.md`", "See `CAVEATS.md`", plus a dedicated Reference files section), but the referenced files do not exist in the bundle — there is no `references/` directory, and the out-of-bundle paths (`skills/setup-apnovo-remote-tool/SKILL.md`, `src/tooluniverse/remote/apnovo/README.md`) are likewise unverifiable. Broken reference targets make navigation fail as shipped, which is worse than the 4 anchor's "minor organization gaps", while the clear signaling and appropriate inline/split judgment keep it above the 3 anchor's "buried references". | 3 / 5 |
Total | 16 / 20 Passed |