Conciseness

Every line carries information Claude would not already know: the library search path, placeholder semantics, exact code for both JS and Python cells, the key-rotation rules, and save locations. There is no padding or explanation of known concepts (the one-sentence "Python cannot import the ESM library" is necessary justification for the Python variant). This matches anchor 5 ('lean and efficient; every token earns its place'), not anchor 4 which allows trimmable over-explanation.

Actionability

The body provides copy-paste-ready code for both runtimes (the two-line JS eval cell, a complete Python cell with key rotation and placeholder filling), exact environment variables (OMO_DAG_LIBRARY, OMO_DAG_SDK_ROOT), exact file paths, and three concrete start() variants with their resulting keys. This matches anchor 5 ('fully executable; copy-paste ready; covers common cases'), exceeding anchor 4's 'minor gaps'.

Workflow Clarity

Each sub-process is clearly sequenced: the ordered library search path, the load/start/done flow, and the save workflow ending in an explicit validation checkpoint ("then confirm by running it once via lib.start"), plus error-recovery guidance ("Recovering a FAILED node ... is retry/amend on that run id"). It fits anchor 4 ('clear sequence with most checkpoints; minor validation gaps') rather than anchor 5, because some validation is implicit — e.g., no check that a definition is valid JSON before saving, or how to handle a name that isn't found in the library — though the runs are not destructive and the explicit confirm-on-save step is present, keeping it above anchor 3.

Progressive Disclosure

No bundle files exist (references/, scripts/, assets/ are absent) and none are needed: the body is a lean, self-contained ~60 lines with well-labeled sections (shape, running, key rotation, Python cells, saving), each tightly scoped. Per the scoring note for skills with no need for external references, well-organized sections alone warrant a 5; there is no inlined content that belongs in a separate file, so anchor 5's 'appropriately split' is satisfied vacuously and anchor 4's 'minor organization gaps' does not apply.

Specificity

"Stores a DAG definition once and re-runs it by name" names the domain and several concrete actions (store, re-run by name, schedule repeatedly), with only minor coverage gaps (e.g., saving/loading details). This matches anchor 4 ('lists several specific actions; minor gaps') rather than 5, which requires comprehensive multi-action coverage, and exceeds anchor 3's '1-2 concrete actions'.

Completeness

The 'what' is explicit ("Stores a DAG definition once and re-runs it by name, instead of pasting the definition into every run") and the 'when' is explicit with concrete triggers ("Use when the user wants to save a DAG, run a saved one, or schedule the same multi-agent graph repeatedly"). This directly matches anchor 5 ('clearly and explicitly answers both what AND when with concrete trigger phrases') — an explicit 'Use when...' clause is present, so it cannot cap at 3 or fit anchor 4's weaker 'when'.

Trigger Term Quality

"save a DAG", "run a saved one", and "schedule the same multi-agent graph repeatedly" are natural phrases users would say, giving good keyword coverage. A few common variations are missing (e.g., 're-run', 'reusable', 'rerun a workflow'), so it fits anchor 4 ('good coverage; a few natural terms missing') rather than anchor 5's synonym-and-extension completeness, and is clearly above anchor 3.

Distinctiveness Conflict Risk

The description carves a clear niche (persisting and re-running named DAG definitions) and the contrast clause "instead of pasting the definition into every run" separates it from one-off DAG authoring skills. Minor overlap risk remains with generic 'run this workflow' or DAG-authoring requests, matching anchor 4 ('mostly distinct; minor overlap risk') rather than anchor 5's minimal-conflict niche.