Override for Codex users who want Gemini, not a second Codex/Codex-MCP reviewer, to act as the reviewer. Install this package after skills/skills-codex/*.
Paper Poster (HTML): measurement-gated poster generation
Gemini overlay assurance: review_independence: cross-family and acceptance_status: accepted.
One HTML file styled for an exact print canvas (@page { size: W H }), rendered to PDF
via Playwright print emulation. Iterate by measuring, not eyeballing — the screen
preview lies; only print emulation at the correct viewport tells the truth. Core gate
machinery is adapted from posterly (MIT, ©
2026 Ruishuo Chen — see NOTICE.md and LICENSES/posterly-MIT.txt in the mainline
skill directory); ARIS adds style discipline gates, figure-provenance gates, the
cross-model review loop, and the anti-patch-loop fix vocabulary.
This overlay is identical to skills/skills-codex/paper-poster-html/ except that the
two cross-model review calls go to Gemini through the local gemini-review MCP
bridge instead of a spawned GPT reviewer agent. Follow the base mirror for everything
not restated here (phases, gates, fix vocabulary, figure provenance, output contract).
Reviewer constants (overlay)
- REVIEWER_MODEL =
gemini-review — Gemini invoked through the local
gemini-review MCP bridge.
- Fresh review job per call — start each review with
mcp__gemini-review__review_start; never reuse a prior review job across review
boundaries. Save the returned jobId, poll mcp__gemini-review__review_status with
a bounded waitSeconds until done=true, and treat the completed payload's
response as the reviewer output.
- The Gemini bridge cannot read your local files — paste the relevant content into the
prompt, and pass rendered posters via
imagePaths.
- If the
gemini-review bridge is unavailable, stop and tell the user what to
configure. Do not silently degrade the cross-model reviews into self-review.
Phase 1 step 2 — Cross-model content audit (Gemini)
mcp__gemini-review__review_start:
prompt: |
Audit a conference-poster content plan against its source paper.
## Poster content plan
[PASTE poster_html/POSTER_CONTENT_PLAN.md]
## Paper source (relevant sections)
[PASTE the paper sections backing the plan's claims — abstract, headline
results tables, method equations, theorem statements]
For EVERY claim, number, equation, and attribution in the plan, output one row:
| claim on poster | paper location | paper says (verbatim) | match? |
with match ∈ {OK, NUMERIC-MISMATCH, OVERCLAIM, MISSING-PRECONDITION,
NOT-IN-PAPER, SCOPE-NARROWED}. End with a count per category.
Poll review_status until done=true; save the response to
poster_html/CLAIM_EVIDENCE.md. Fix every non-OK row or record it as a
user-acknowledged tradeoff.
Phase 6 — Final review (Gemini, multimodal)
All hard gates PASS + polish warnings zero-or-waived + executor visual score ≥ 9
first. Then:
mcp__gemini-review__review_start:
imagePaths: ["poster_html/poster_preview.png"]
prompt: |
Final print-readiness audit of a conference poster (image attached).
## Final poster text content
[PASTE the text content extracted from poster_html/poster.html]
## Gate report summary
[PASTE the overall/hard_failures/warnings fields of poster_html/GATE_REPORT.json]
## Claim→evidence audit
[PASTE poster_html/CLAIM_EVIDENCE.md]
Check: (1) fidelity & overclaims RE-CHECKED on the final text (polish introduces
new claims), (2) residue (\ref{, TODO, raw < in math, missing images, remote
URLs), (3) visual rhetoric (headline numbers prominent, banner readable from
2 m, two-hue discipline, real paper figures central and inside their cards),
(4) gate-log coherence.
Verdict: PRINT-READY or NEEDS-FIX with a numbered, severity-ordered issue list.
Poll mcp__gemini-review__review_status with a bounded waitSeconds until done=true;
treat the completed payload's response as the reviewer verdict.
The reviewer recommends; it does not edit. Any fix → back through Phase 4/5 gates —
never straight to re-review.
Review tracing
Save both review jobs' raw responses per ../../shared-references/review-tracing.md to
.aris/traces/paper-poster-html/<date>_run<NN>/.