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 disciplined orchestration body: it treats the loop as a scripted state machine with concrete commands, explicit validation at every stage, and a genuine progressive-disclosure layout over real bundle files. The only deductions are modest cross-section repetition of the gating rules and two path inconsistencies (a missing skill_info.yaml reference and a specs/ vs references/ baseline_spec path mismatch).
Suggestions
Fix the two broken/inconsistent paths: add the missing references/skill_info.yaml (or correct the path in '## Launch Intake'), and align the 'specs/baseline_spec.yaml' citation in '## Base Model' with the actual references/baseline_spec.yaml location used by the reference files.
De-duplicate the gating material: state the single user gate once (Agent Behavior) and have '## Gating' reference it in one line instead of restating the pre-flight/gate/pre-seed rules a third time.
Move the detailed Train AutoML Policy cases (direct docker run vs SDK dispatch) into references/scripts-and-agents.md next to the existing AutoML-policy pitfall, keeping only the one-line rule and the spec-YAML prohibition in SKILL.md.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and operational with no explanations of concepts Claude already knows, but there is cross-section redundancy: the single user gate is fully specified in the 'Agent Behavior' quote block and restated in '## Gating', and pre-flight / baseline-pre-seed points repeat across 'Execution Contract', 'Workflow', and 'Gating'. This fits the level-4 anchor (efficient, minor trimmable instances) rather than 5, where every token would earn its place. | 4 / 5 |
Actionability | Guidance is copy-paste executable throughout: 'PYTHON=$(bash scripts/deft_python.sh)', '"$PYTHON" scripts/init_deft_state.py', '[ -n "$VAR" ] && echo SET || echo UNSET', 'set -a; source /path/to/.env; set +a', exact mount rules ('$WORKSPACE:/data/workspace'), and even the exact Hydra failure string to recognize. This matches the level-5 anchor of fully executable, specific commands covering the common cases. | 5 / 5 |
Workflow Clarity | The loop is clearly sequenced (Pre-Flight → Baseline → Iterate → Stop → Render) with pervasive validation checkpoints: 'run ""$PYTHON"" scripts/deft_context.py ... before every stage', commit_stage.py verifying required inputs atomically, an enumerated hard-stop list, and an explicit error-recovery feedback loop ('read the final error block ... make one evidence-based correction, and rerun its documented verification'). This matches the level-5 anchor including feedback loops, so the destructive/batch cap does not apply. | 5 / 5 |
Progressive Disclosure | Structure is good: a topic→reference table with contents, a stage→reference→skill mapping, bundled scripts, and just-in-time reading rules; references are one level deep (sibling cross-links only). But '## Launch Intake' directs reading 'references/skill_info.yaml', which does not exist in the bundle, and the body cites 'specs/baseline_spec.yaml' while the file actually lives at 'references/baseline_spec.yaml' — minor organization gaps that fit the level-4 anchor, not 5. | 4 / 5 |
Total | 18 / 20 Passed |