Canvas Humanizer Loop

canvas-humanizer-loop

Wrap canvas-humanizer in a bounded repair loop. The purpose is to catch damage introduced by rewriting, not to keep rewriting until an external score changes.

Inputs and boundaries

Accept the same paths and voice inputs as canvas-humanizer, plus:

• course_id, assignment_id, and the stable assignment work_dir;
• optional spec_path, rubric_path, source_paths, and hard_locks;
• max_iter, default 3 and hard-capped at 3.

Require work_dir to end in course-<course_id>__assignment-<assignment_id>. Write loop artifacts below <output_dir>/_loop_iter_<N>/ and loop_log.json beside the final output. Validate the path against src.course_artifacts.stable_work_dir before writing.

This skill performs local file work only. It does not call Canvas, write an assignment result.json, or declare submission readiness. The caller must run its course-level verification after the loop.

Use src.humanizer_segmentation.split_sentences and paragraph_segment_counts; do not invent another splitter.

Iteration layout

_loop_iter_1/
  humanized.docx
  humanizer_log.json
  audit-a.json
  audit-b.json
  audit-c.json
  merged-audit.json
  repair-log.json
_loop_iter_2/
...
loop_log.json

Use atomic JSON/text writes. Keep the original draft and every iteration so the best safe artifact can be recovered.

Stage 1: preflight

1. Read the complete original, spec, rubric, and necessary source excerpts.
2. Confirm hard locks are unique enough to track. If a lock occurs multiple times, index occurrences rather than relying on raw string replacement.
3. Capture original paragraph order, segment counts, citations, headings, numeric constraints, and source anchors.
4. Reject missing required spec/rubric/source inputs when the caller says they are mandatory.

Stage 2: run one humanizer pass

Invoke the Codex canvas-humanizer skill using the current iteration input and write its output/log into _loop_iter_<N>/. On the first iteration the input is the original. On later iterations the input is the repaired prior output with already-safe segments locked.

If the humanizer cannot produce a valid output, stop and return the safest historical artifact (the original if no iteration succeeded).

Stage 3: three independent audits

Spawn three native Codex audit subagents in parallel. Give each only the raw original, current output, spec/rubric anchors, source excerpts, and the schema below. Do not give them another reviewer's verdict.

Each auditor evaluates every canonical segment (doc_paragraph_index, intra_para_index) for:

• meaning/factual drift;
• missing, altered, duplicated, or moved locks/citations;
• structural damage or segment merge/split;
• grammar that obscures meaning;
• voice-register drift;
• rubric or source-grounding damage;
• word-count/format risk.

Require strict JSON:

{
  "segments": [
    {
      "doc_paragraph_index": 3,
      "intra_para_index": 1,
      "verdict": "MUST_FIX",
      "dimensions": ["meaning_drift"],
      "original_anchor": "exact text",
      "current_anchor": "exact text",
      "fix_directive": "one specific repair"
    }
  ]
}

Allowed verdicts are PASS, SHOULD_FIX, and MUST_FIX; these are audit verdicts, not Canvas Pilot result statuses.

Stage 4: majority merge

Merge by canonical segment ID:

• MUST_FIX when at least two auditors say MUST_FIX, or one says MUST_FIX and another independently flags the same damage dimension;
• SHOULD_FIX when at least two say SHOULD_FIX and no majority MUST_FIX;
• otherwise PASS.

Any deterministic failure—missing lock/citation, changed paragraph count, segment split/merge, unresolved placeholder, or missing required heading—is MUST_FIX regardless of model vote.

Write merged-audit.json with all three raw verdicts, merged verdict, dimensions, and exact anchors. Do not hide disagreement.