Content
88%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 exemplary knowledge-skill body: dense with non-derivable, codebase-specific expertise, explicit validation gates, and precise file/test targets for every workflow. The only meaningful gap is a small code-shape example for the most common edit task (adding a taint model entry), and the absence of any reference-file split at ~180 lines.
Suggestions
Add a minimal example of one model entry (source, sink, and a sanitizer with its `neutralizes` kind list) so the "Adding a source / sink / sanitizer" workflow is copy-paste-ready without opening `taint/typescript-model.ts` first.
If the skill keeps growing, move the deferred false-negative classes and prior-art sections into a `references/` file (e.g. `references/prior-art.md`) and keep one-line pointers in the body — this would earn the top progressive-disclosure score.
Consider a brief note on where the milestone/issue identifiers (M1–M4, #2081–#2084, bits M3/U9) resolve, since they are used as rationale anchors but a new reader cannot look them up from the skill alone.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Every section carries non-public, codebase-specific knowledge (phase gating rationale, the 1-based/0-based line join at `startLine - 1`, kind-set sanitizer semantics, `Kuzu has no secondary index on relationship properties`), with zero padding or explanation of concepts Claude already knows. Dense fragments like "a kind-blind kill would be a suppressed live injection (the forbidden FN direction)" earn their tokens by encoding failure directions. The one-line prior-art citations are compact pointers, not essays. | 5 / 5 |
Actionability | The validation checklist is fully executable (tsc, targeted vitest directories, `bench/cfg/measure.mjs --check`, `detect_changes()`, `impact({direction:'upstream'})`) and the model-editing workflow names exact files (`taint/typescript-model.ts` etc.), the registration seam, test harnesses, and the integration proof directory. Falls short of 5 only on the primary edit task: it says the model spec is "hashable data (no functions)" and describes `neutralizes` semantics but never shows a one-line example of a source/sink/sanitizer entry, so a first edit still requires opening the model file to learn the shape. | 4 / 5 |
Workflow Clarity | The "Validation checklist for any `--pdg` change" is an explicitly numbered sequence with validation at every step — type-clean gate, isolation-verified tests, the byte-identical golden parity gate, fingerprint/budget check, and pre-commit `detect_changes()`/upstream `impact()` guards — which is precisely the checklist-with-checkpoints pattern. The layer table (L1–L4) sequences build order, and the add-a-model section sequences edit → fixture → assert with the exact harness per language. Error-recovery guidance is present ("verify by ISOLATION, not full-suite exit (known load-flakes)", `node scripts/build.js` before worker/integration runs). | 5 / 5 |
Progressive Disclosure | No bundle files exist (references/, scripts/, assets/ are absent) and none are referenced, so the 5-point 'one-level-deep well-signaled references' pattern cannot apply; the body is instead a single well-sectioned ~180-line overview with clear headers. This is good structure with most content appropriately placed, but material that could live in a reference file (the prior-art section, the deferred false-negative classes) is inlined, and at this density a `references/` split (e.g. models and prior art) would improve navigation. | 4 / 5 |
Total | 18 / 20 Passed |