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.
A highly actionable, well-sequenced pipeline skill: every step has executable commands, explicit prerequisite validation, conditional branches per tracker, and a gated drift check with user-feedback loops. The weaknesses are mild duplication — the Error Handling section restates several conditions already handled inline, and some local-backlog error entries overlap the externalized reference file.
Suggestions
Trim the Error Handling section to entries not already covered inline (e.g., drop 'gh CLI not authenticated', 'Label missing from repository', and 'No issues match filters', which restate Steps 1-3), cutting roughly a third of the section without losing coverage.
Deduplicate the identical no-results guidance in Step 3's GitHub and Jira branches by hoisting it to a single line after the branch examples.
Consider moving the greenfield-specific error entries (blocked features, decomposed feature selected, Feature Tracker parse/write failures) into references/greenfield-backlog-resolution.md next to the resolution workflow they describe, keeping the main body focused on the remote flow.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and imperative with no concept-teaching padding, but the 18-entry Error Handling catalog restates roughly six conditions already covered inline (e.g., "gh CLI not authenticated", "Label missing from repository", "No issues match filters") and "If no results match the filters…" appears verbatim in both tracker branches. Efficient overall with trimming available — anchor 4, not 5; the systematic duplication keeps it above 3 only because the rest is lean. | 4 / 5 |
Actionability | Fully executable throughout: copy-paste-ready gh commands with exact flags and JSON fields ("gh issue list --label \"<label1>,<label2>\" --state open --limit 10 --json number,title,labels,createdAt,author"), exact JQL, exact display formats, a verbatim drift-detector agent prompt, and concrete file-detection paths. Matches the top anchor; the generic 'use the Atlassian MCP server' phrasing is inherent to MCP integration and doesn't leave a real gap. | 5 / 5 |
Workflow Clarity | Steps 1–7 are clearly sequenced with explicit validation (git remote check, "gh auth status", MCP availability probe), a gated Step 6.5 with skip conditions, severity-based feedback loops (none/minor → proceed, moderate/major → ask user), and failure fallback ("If the drift detector itself fails … Do not block on a tool failure"). Batch label creation is check-then-act and flagged idempotent, so the destructive/batch cap does not apply — top anchor. | 5 / 5 |
Progressive Disclosure | The greenfield procedure is properly externalized to a real, well-signaled one-level-deep reference ("Read ${CLAUDE_PLUGIN_ROOT}/skills/arn-code-pick-issue/references/greenfield-backlog-resolution.md for the full detection chain, resolution workflow, and sub-feature handling"), and cross-references to platform-labels.md and ensure-config.md are clearly signaled. However, the ~287-line body inlines a large error catalog that partially duplicates both the steps and the reference file — good structure with minor organization gaps, anchor 4 rather than the lean-overview anchor 5. | 4 / 5 |
Total | 18 / 20 Passed |