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qa-quarto

Adversarial Quarto-vs-Beamer parity QA. A critic agent compares the Quarto HTML render to the Beamer PDF benchmark for content/visual parity; a fixer agent applies fixes; loops until APPROVED or two consecutive rounds turn up nothing new (fallback cap 5 rounds). Use when user says "qa the quarto", "check parity", "does the html match the pdf?", "quarto matches beamer?", or after a translate-to-quarto run. Requires both the `.qmd` rendered and a `.pdf` benchmark.

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Adversarial Quarto vs Beamer QA Workflow

Compare Quarto HTML slides against their Beamer PDF benchmark using an iterative critic/fixer loop.

Philosophy: The Beamer PDF is the gold standard. The Quarto translation must be at least as good in every dimension.


Workflow

Phase 0: Pre-flight → Phase 1: Critic audit → Phase 2: Fixer → Phase 3: Re-audit → Loop until APPROVED or dry (2 consecutive dry rounds; fallback cap 5)

Hard Gates (Non-Negotiable)

GateCondition
OverflowNO content cut off
Plot QualityInteractive charts >= static plots
Content ParityNo missing slides/equations/text
Visual RegressionQuarto >= Beamer in all dimensions
Slide CenteringContent centered, no jumping
Notation FidelityAll math verbatim from Beamer

Phase 0: Pre-flight

  1. Locate Beamer (.tex/.pdf) and Quarto (.qmd/.html) files
  2. Check freshness (re-render if QMD newer than HTML)
  3. Verify TikZ SVGs if applicable
  4. Measure the render: "${SLIDE_QA_PYTHON:-python3}" scripts/slide-qa.py Quarto/[Lecture].html. It loads the deck in headless Chrome and writes quality_reports/audits/slide-qa/[Lecture]/report.md with per-slide overflow in pixels, plus one screenshot per slide. Exit 1 means it found something — overflow, clipped content, a broken image, or a missing or wrong-case file; exit 2 means it could not run (usually Playwright is missing — the script prints the one-time venv install and the SLIDE_QA_PYTHON line to set) — say so, and the critic falls back to reading the source.

Phase 1: Initial Audit

Launch the quarto-critic agent to compare Beamer vs Quarto comprehensively, passing the slide-qa report path from Phase 0 for the Overflow gate. Report saved to quality_reports/[Lecture]_qa_critic_round1.md.

Phase 2: Fix Cycle

If not APPROVED, launch quarto-fixer agent to apply fixes (Critical → Major → Minor), re-render, and verify.

Phase 3: Re-Audit

Re-run scripts/slide-qa.py on the fixer's re-render, then re-launch the critic with the fresh report to verify fixes. Loop back to Phase 2 if needed.

Iteration Limits — loop-until-dry

This is the loop-until-dry primitive from orchestrator-protocol.md: the critic returns FINDINGs (the hard-gate table is the CRITICAL roll-up, per orchestration-schemas.md); the loop converges after 2 consecutive dry rounds — rounds that add 0 new CRITICAL/MAJOR findings (deduped on id = sha1(file:line:locus)) — not at a fixed round count.

  • Fallback cap: 5 rounds bounds a non-converging loop, then escalate to the user with remaining issues.
  • Two-strikes: the same gate failing in rounds N and N+2 is flagged for the user, not patched again (summary-parity.md).
  • APPROVED iff every hard gate passes and no CRITICAL or MAJOR finding remains (minor ones are listed for the user).

Final Report

Save to quality_reports/[Lecture]_qa_final.md with hard gate status, iteration summary, and remaining issues.

Findings are validated, not just written (v2.5)

This skill's reviewers emit findings under the machine-checked contract in finding-schema.json. Reports are JSON arrays.

Smoke-test the harness before spending review effort — a run that fans out reviewers and then cannot write a valid report has wasted the whole pass:

echo '[]' | python3 scripts/validate-findings.py

Reviewer agents are read-only, so this skill writes the files. For each reviewer's final response: save the prose report to this skill's report path for that reviewer, copy its closing fenced json block to a scratch file, and fill the ids while validating:

python3 scripts/validate-findings.py --fill-ids block.json > <report>.json.tmp \
  && mv <report>.json.tmp <report>.json || rm -f <report>.json.tmp   # exit 0 required; a failed run keeps no file
python3 scripts/validate-findings.py --check-quotes <report>.json   # each quote must be the file's own text (orchestration-schemas.md §1)

A reviewer that returned no json block, or a block that does not validate, has not reviewed: re-dispatch it once with the validator's error text, then report the lens as missing rather than reducing without it.

What the contract forces, and why:

  • rule — the documented rule or standard violated. A finding citing no rule is an opinion, and opinions do not gate a commit.
  • failing_case — a concrete configuration under which the claim breaks, or the exact missing hypothesis. "This could be clearer" does not validate.
  • id = sha1("<file>:<line>:<locus>") — deterministic, so dedup across rounds is exact and the two-strikes rule is checkable rather than eyeballed.
  • mechanical — true only for fixes that cannot change a result (typo, cross-reference, formatting, label). Never for an estimand, assumption, specification, inference procedure, sample definition, or reporting language: those return to the researcher.

Apply the per-lens evidence burdens and the "does NOT count" filters in orchestration-schemas.md §7 before verification, so known false alarms never reach the judge. The verifier pass is refute-biased and sets each finding's verdict (reviewers leave it unset): only verdict: "confirmed" findings ship; anything it cannot ground is dropped, not downgraded to a warning.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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概要と使いどころ

Turn an incoming set of findings — from an AI reviewer, a referee report, a code review, a linter, or a second model — into verified fixes, without letting a confident misread damage correct work. Every finding is a CANDIDATE until checked against the actual source. Use whenever you receive review comments, audit findings, or a critique you did not write yourself, especially when the reviewer is a model or when the volume is too large to check by feel.

日本語の概要は準備中です。原文の説明を表示しています。

pedrohcgs/claude-code-my-workflow1,6592026年9月28日 更新

Enforce the replication-protocol.md rule by cross-checking numeric claims in a manuscript against the actual R / Stata / Python outputs. Report PASS/FAIL per claim against tolerance thresholds. Use before submission and before releasing a replication package.

日本語の概要は準備中です。原文の説明を表示しています。

pedrohcgs/claude-code-my-workflow1,6592026年9月28日 更新

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日本語の概要は準備中です。原文の説明を表示しています。

pedrohcgs/claude-code-my-workflow1,6592026年9月28日 更新

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日本語の概要は準備中です。原文の説明を表示しています。

pedrohcgs/claude-code-my-workflow1,6592026年9月28日 更新

challenge

無料

Stress-test a finding against the choices you did not make. Enumerates the discrete forks a competent analyst could have taken (measure definition, sample filter, control set, clustering level, weighting, functional form), runs the specification grid, and reports the distribution rather than a point estimate — then attacks the identifying assumption with named, computable sensitivity statistics. Use when the user says "is this robust", "challenge this result", "specification curve", "multiverse", "how sensitive is this", "what if I'd used a different measure", "stress-test my estimate", or before a result becomes a headline claim. NOT a reviewer of prose or code — it challenges the CLAIM.

日本語の概要は準備中です。原文の説明を表示しています。

pedrohcgs/claude-code-my-workflow1,6592026年9月28日 更新

Save a structured state snapshot before stopping or handing off. Captures the active plan, recent decisions, file pointers (with line numbers), open questions, and the next 1–3 actions into a checkpoint file under `quality_reports/checkpoints/`. Optionally proposes `[LEARN]` entries to add to MEMORY.md. Use when user says "checkpoint", "save state", "snapshot before I stop", "where am I", "wrap up the session for handoff", or before a long break / model switch / collaborator handoff. Companion to (NOT replacement for) the narrative session-log workflow.

日本語の概要は準備中です。原文の説明を表示しています。

pedrohcgs/claude-code-my-workflow1,6592026年9月28日 更新

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