Content
86%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.
An unusually strong body: runnable commands and code for both the GatingML and FlowJo workspace paths, a sequenced workflow with genuine validation checkpoints, and clean one-level-deep bundle structure. The residual weaknesses are mild repetition of cautionary caveats and an implied (rather than explicit) error-recovery loop after validation checks.
Suggestions
Consolidate the recurring biological-validity caveats ("not biological validation", "not transferable biology", "not independent experimental replicates") into a single short 'Limits' note so each warning appears once and the workflow steps stay lean.
Make the error-recovery loop explicit after the inspection step: state what to do when warnings appear or imported results disagree with FlowJo (e.g., re-check compensation/transform coordinate consistency, then re-run) so the validate -> fix -> retry cycle is spelled out.
Group version-pinned behavior notes ("FlowKit 1.3.2 reports zero for ordinary children and NaN for quadrants") under a clearly labeled version-specific subsection so future releases can be updated in one place rather than leaving stale statements inline.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense with non-obvious domain guidance ("PnS marker names may be empty or repeated", "the default is FCS $FIL, which can differ from the current filename", "a plot's subsample is not the population denominator") and never explains concepts Claude already knows, so it is above the 3 anchor. It is below 5 because a few items could be trimmed: the cautionary asides about biological validity recur across sections ("They are not biological validation", "demonstration thresholds are not transferable biology", "events from one specimen are not independent experimental replicates"), and the licensing sentence adds little for acting. | 4 / 5 |
Actionability | Fully executable, copy-paste-ready guidance covering the common cases: a uv venv install block, two complete uv-run invocations for both entry paths (--gatingml and --workspace with --fcs file lists), and a runnable end-to-end Python example including transform application and GatingML export, plus a concrete output schema ("gate_report.csv", "provenance.json", named columns like sample_event_count and relative_percent_defined). This matches the 5 anchor; nothing is pseudocode or high-level. | 5 / 5 |
Workflow Clarity | The six-step Workflow is clearly sequenced with real validation checkpoints ("Reject ID collisions before loading a batch", "Run on all events, then check gate overlays and sample-level QC", "Review warnings and compare representative imported results to FlowJo"), and the helper hard-rejects invalid batches ("rejects duplicate sample IDs, missing/extra workspace-group samples, zero-event samples"), so the batch-operation cap of 3 does not apply. It falls short of the 5 anchor only because the error-recovery loop is implied rather than explicit — there is no 'if the comparison mismatches, do X and re-run' step. | 4 / 5 |
Progressive Disclosure | SKILL.md is a clear overview with well-signaled, one-level-deep references: both linked files exist and match their advertised scopes ([compensation-and-gating.md](references/compensation-and-gating.md), [workspaces-and-results.md](references/workspaces-and-results.md)), the referenced scripts/analyze_gates.py exists, the reference files contain no further nested .md chains (only external API links), and each link is annotated with what it covers. Content is appropriately split; navigation is easy. | 5 / 5 |
Total | 18 / 20 Passed |