skills/slop-judge/SKILL.md
Score generated prose or product output that cleared deterministic checks against a rubric threshold.
npx skillsauth add bigeasyfreeman/adlc slop-judgeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this only after stop-slop regex and pattern checks do not hard-fail.
The judge exists to close the output-side quality loop. It is not a prompt rewriter. It scores the produced output against a concrete rubric and returns a numeric result that a gate can compare to a threshold.
{
"mode": "general | outreach | product_output | agent_output",
"regex_screen": "pass",
"content": "string",
"audience": "internal | external",
"rubric": [
"Concrete criterion such as specificity, actionability, schema validity, policy fit, or non-hallucination"
],
"threshold": 0.7,
"baseline_score": 0.82,
"regression_tolerance": 0.03
}
{
"verdict": "pass | revise",
"score": 0.0,
"threshold": 0.7,
"criterion_scores": [
{
"criterion": "string",
"score": 0.0,
"reason": "string"
}
],
"regression_delta": 0.0,
"rationale": "string",
"signals": ["generic_filler", "passive_evasion", "tautology"],
"new_eval_case_candidate": {
"source": "council_rejection | human_edit | runtime_failure | production_sample | other",
"input": "optional string",
"bad_output": "string",
"expected_quality": "string",
"metric": "rubric_score",
"threshold": 0.7
}
}
pass means score >= threshold and any baseline regression is within regression_tolerance.revise means the output scored below threshold, regressed beyond tolerance, or remains padded, evasive, generic, structurally invalid, or self-referential even though deterministic checks did not catch it.score is the average unless the input names a stricter metric.revise with signals: ["missing_benchmark"].new_eval_case_candidate so feedback-loop can promote the failure into the saved suite after approval.development
Discovers and records repo-local approved build paths so agents reuse proven patterns instead of inventing parallel architectures.
development
Scoped maintenance for docs/solutions entries when stale signals, refactors, or explicit user scope require refresh.
documentation
Conditionally captures verified reusable ADLC learnings into docs/solutions after successful closeout.
development
Uses Graphify as ADLC's graph-backed research layer and Beads as an optional dependency-aware task memory layer. Produces evidence for compatibility, reuse, accuracy, dark-code hotspots, and long-horizon handoff.