batch-review

# Batch Review: $ARGUMENTS ## Parse Arguments The user provides: `{track} {start}-{end}` or `{track} {num}` (single module). Examples: - `a1 5-10` — review modules 5 through 10 in A1 - `a1 8` — review just module 8 - `hist 1-20` — review HIST modules 1-20 ## Resolve Module Slugs For each module number in the range, resolve the slug. Use the curriculum index: ```bash .venv/bin/python -c " import sys; sys.path.insert(0, 'scripts') from batch_gemini_config import get_module_index idx = get_module_index('{track}') for n in range({start}, {end}+1): slug = idx['num_to_slug'].get(n) if slug: print(f'{n} {slug}') " ``` ## Filter: Only Review Built Modules Skip modules that don't have content yet. Check: - `curriculum/l2-uk-en/{track}/{slug}.md` exists - `curriculum/l2-uk-en/{track}/orchestration/{slug}/state-v5.json` (or `state.json`) exists List the modules to review and any skipped modules. ## Dispatch Subagents (Max 4 Parallel) > **Reviewer-seat economics guard** (`rules/model-assignment.md`): each `Agent`-tool subagent reloads the full > project (~2–3M tokens) for its verdict, so it is ~50–150× the cost of an inline review. For a **single > module or a small batch (≤3), review INLINE** in this session (or defer to next session if context is heavy) — > do NOT spawn subagents. Subagents here are justified ONLY for a **genuinely large batch** where wall-clock + > context-overflow outweigh the per-subagent reload. When in doubt, prefer non-Claude dispatched reviewers > (DeepSeek/Codex) for the bulk and prefer the Claude seat in-session for cost — dispatching Claude is > permitted when needed (user 2026-06-22), but it costs the multiple above, so route by need, not by ban. Split the reviewable modules into chunks of 2-3 modules each. Spawn up to 4 subagents using the Agent tool, each processing its chunk. **Each subagent receives this task:** For each module in your chunk: ### Part 1: Prompt Review Read ALL files in `curriculum/l2-uk-en/{track}/orchestration/{slug}/`: - `phase-2-prompt.md`, `phase-2-friction-1.md` (content prompt + friction) - `phase-C-prompt.md`, `phase-C-friction.md` (activities prompt + friction) - `phase-A-prompt.md`, `phase-A-output.md` (research) - `placeholders.yaml` (injected context) - `state-v5.json` or `state.json` (pipeline state, attempt counts) - `completion.md` (final verdict) - `validate-fix*-prompt.md` (validation fix attempts) - `screen-result.json` (VESUM screening) Follow the analysis framework from the prompt-review skill prompt (in `agents_extensions/shared/skills/prompt-review/prompt-review-prompt.md`). Produce the full prompt-review report. Write to TWO locations: 1. `curriculum/l2-uk-en/{track}/audit/{slug}-prompt-review.md` 2. `curriculum/l2-uk-en/{track}/orchestration/{slug}/prompt-review.md` ### Part 2: Content Review Read the module files: - `curriculum/l2-uk-en/{track}/{slug}.md` (content) - `curriculum/l2-uk-en/{track}/activities/{slug}.yaml` (activities) - `curriculum/l2-uk-en/{track}/vocabulary/{slug}.yaml` (vocabulary, if exists) - `curriculum/l2-uk-en/{track}/meta/{slug}.yaml` (meta) - `curriculum/l2-uk-en/plans/{track}/{slug}.yaml` (plan) Follow the content review prompt (in `agents_extensions/shared/skills/content-review/content-review-prompt.md`). Use RAG tools for linguistic verification: - `mcp__sources__verify_word` for suspicious Ukrainian words - `mcp__sources__search_text` for grammar verification against textbooks - `mcp__sources__query_r2u` for Russicism checking Produce the full content-review report with grade (A/B/C/F). Write to TWO locations: 1. `curriculum/l2-uk-en/{track}/audit/{slug}-content-review.md` 2. `curriculum/l2-uk-en/{track}/orchestration/{slug}/content-review.md` ### Return Format Return a JSON summary for the main agent: ```json { "modules": [ { "num": N, "slug": "...", "prompt_review": {"template_health": "GOOD/NEEDS_WORK/BROKEN", "fix_count": N, "top_fix": "..."}, "content_review": {"grade": "A/B/C/F", "critical_count": N, "high_count": N, "issues_summary": "..."} } ] } ``` ## Aggregate Results After all subagents return, the main agent: 1. **Collect all findings** — read all generated reports 2. **Cross-module pattern analysis** — identify recurring issues: - Same friction type across 3+ modules = template-level bug - Same content issue across 3+ modules = systematic prompt problem - Same VESUM failures = pipeline limitation 3. **Write cross-module summary** to `curriculum/l2-uk-en/{track}/audit/batch-review-summary-M{start}-M{end}.md` 4. **Propose auto-fixes** — for template-level patterns, list specific file + diff changes ### Auto-Fix Categories | Category | Auto-fixable? | Target File | |----------|--------------|-------------| | Missing constraint/ban | YES | `scripts/pipeline_lib.py` (PEDAGOGICAL_CONSTRAINTS) | | Missing context injection | YES | `agents_extensions/shared/phases/gemini/*.md` templates | | Immersion variety issues | YES | `agents_extensions/shared/phases/gemini/beginner-content.md` | | VESUM false positives | YES | `scripts/rag_batch_verify.py` | | Content quality (per-module) | NO — needs rebuild | Flag for `--rebuild` | | Pedagogical structure | NO — needs rebuild | Flag for `--rebuild` | For auto-fixable issues: present the diff and ask user for confirmation before applying. For rebuild-needed issues: list modules that need rebuilding and why. ## Output Print a summary table: ``` Batch Review: {track} M{start}-M{end} Reviewed: N modules | Skipped: M modules | # | Slug | Prompt | Content | Grade | Issues | |---|------|--------|---------|-------|--------| | 5 | syllables-and-transfer | GOOD | B | 1H 2M | | 6 | stress-and-intonation | GOOD | A | 0 | ... Template fixes proposed: N (see batch-review-summary) Modules needing rebuild: M ```