frank-review

Review Model

Note: The current year is 2026. Use this when dating review notes and searching for recent documentation.

frank-review closes the loop. frank-plan decided WHAT to model and listed the verification assertions; frank-build wrote and ran the generator. This skill captures the built geometry from a recorded camera, dispatches two adversarial reviewers to compare it against the reference, merges their findings, presents a side-by-side to the human (the sole acceptance authority), and iterates until accepted or the iteration cap is reached. It judges the model against a target — it does not author or run the generator. If a finding requires changing a parameter, frank-review proposes the tweak and hands it back to frank-build; the build owns the runnable code.

The capture is the load-bearing artifact. A review is only as good as a comparable capture, so the single non-negotiable discipline here is a pinned camera — recorded once, reused every iteration — and the human, not the agent, signs off on fidelity (an agent judging its own viewport capture is circular).

Interaction Method

When asking the user a question, use the platform's blocking question tool: AskUserQuestion in Claude Code (call ToolSearch with select:AskUserQuestion first if its schema isn't loaded), request_user_input in Codex, ask_user in Gemini, ask_user in Pi (requires the pi-ask-user extension). Fall back to numbered options in chat only when no blocking tool exists in the harness or the call errors (e.g., Codex edit modes) — not because a schema load is required. Never silently skip the question.

Ask one question at a time. Prefer a concise single-select choice when natural options exist.

Core Principles

1. Pin the camera once, reuse it every iteration. A capture is only comparable across iterations if the camera is identical each time. Record the camera's position, target, and lens/focal-length (or a named view) in the review's working notes on iteration 1, and reuse those exact values for every subsequent capture. A moved camera makes the silhouette comparison meaningless and silently invalidates the whole loop.
2. The human is the acceptance authority. The reviewer agents surface mismatches; they do not decide "done." Fidelity sign-off is a human decision — the agent presents the side-by-side and asks. Self-certifying the fidelity target is circular and forbidden.
3. Reviewers judge files, not geometry. frank-review holds the MCP and does the capturing; it passes the reviewers file paths (the capture and the reference), never MCP tools. The reviewers are structurally incapable of mutating geometry — that is the safety model. Do not hand them a viewport or an MCP grant.
4. Findings drive parameter tweaks, not direct edits. A merged finding is a proposal. frank-review translates it into a concrete parameter change and routes it back to frank-build to apply, re-run, and re-capture. The review never edits the generator itself.
5. Cap the loop; do not auto-accept at the cap. Iterating forever is worse than stopping and asking. Cap at ~6 iterations. At the cap without acceptance, present the latest state and unresolved findings to the human for an accept/override decision — never silently accept because the budget ran out.
6. Honor user-named resources. When the user names a reference image, a target view, or prior working notes, treat them as authoritative. Read/discover them before assuming they're unavailable; if a named resource fails to load, say so explicitly rather than substituting silently.

When to Use

Use frank-review when:

• frank-build has produced geometry and you need to know whether it matches the reference.
• You want an adversarial second opinion on a render before declaring a model done.
• You're iterating a parametric build toward a faithful silhouette and need a repeatable capture-compare-adjust loop with a fixed camera.

Do not use frank-review to write or run the generator (that's frank-build), to produce the modeling plan or its assertions (that's frank-plan), or to record a solved gotcha after the fact (that's frank-compound).

Review Quality Bar

A review is complete when:

• The capture was taken from a recorded camera (position/target/lens noted) that matches the reference framing, and every iteration reused it.
• Both reviewers ran (or an absent reviewer was noted) and their JSON findings were merged and deduplicated into one finding set.
• The merged findings were presented side by side with the reference to the human, who made the accept/iterate/override call — not the agent.
• Either: the human accepted, or the iteration cap was reached, the unresolved findings were listed by reviewer lane, and the human was asked to accept or override.
• On Houdini, the capture used render_single_view, read the render from disk, cleaned the scaffolding nodes, and never called render_quad_views.

Reviewers

Two adversarial reviewers, each in its own lane. They share one pinned JSON findings contract (see Stage 4) so the merge step works.

Agent	Lane	Judges
frank-silhouette-critic	silhouette	Visual comparison of the captured render vs the reference — silhouette, proportion, framing, feature placement/count. Vision task — dispatched on a vision-capable model (override at the dispatch site).
frank-geometry-reviewer	geometry	The geometry summary / inspection data — topology, scale, units, naked edges, self-intersection, manifoldness. Reasons over the data frank-review passes it.

Both receive only Read, Grep, Glob, Bash (+ Write for their JSON artifact) — no MCP grant. They get the capture as a file path and the reference image as a file path in their Task payload; frank-review does the capturing.

Workflow

Stage 1: Detect the tool and gather the inputs

1.1 Detect the active modeling MCP family

The capture branch is entirely tool-specific, so resolve the connected family before anything else.

• Inspect which MCP tools are present in your available toolset: an mcp__rhino__* family means Rhino; an mcp__houdini__* family means Houdini (deferred tools surface by name and load via ToolSearch).
• Confirm the family is live by issuing its survey call — get_document_summary for Rhino, get_scene_info for Houdini. A successful response proves a live, responsive connection.
• Both families present → ask the user which to target (see Interaction Method).
• Neither present → halt and point the user at setup: docs/houdini-setup.md for Houdini, or the Rhino MCP setup for Rhino. Suggest /frank-setup. There is nothing to capture without a live modeler.

Record the chosen family — it selects the capture branch in Stage 3 and which references/<tool>-mcp.md pack the STOP-gate in Stage 2 loads.

1.2 Gather the reference and the target view

#$ARGUMENTS

If the input above is empty, ask the user: "What should I compare the model against? Share the reference image (path or URL) and, if you have one in mind, the view to capture from." Then wait for their response.

• Reference image — read/fetch it now; it is the source of truth frank-silhouette-critic compares against. If the model is being reviewed against a described intent rather than an image (per the plan), note that and tell the reviewers what "done" means in words.
• Target view / framing — the camera the reference is shot from (front-orthographic is the usual silhouette-comparison view). If the plan or prior working notes recorded a camera, reuse it (see Stage 3). If not, you'll pick and record one in Stage 3.
• Prior working notes — if this is a continuing review, load the recorded camera and the running iteration count so the loop resumes rather than restarts.

Stage 2: Load the capture POLICY

STOP. Before capturing anything, read the Capture POLICY section of references/<tool>-mcp.md (the connected family's pack — rhino-mcp.md for Rhino, houdini-mcp.md for Houdini). Skipping it means you won't know that Rhino's capture_viewport returns the image inline (no scaffolding) while Houdini's render_single_view writes to disk and leaves three scaffolding nodes that must be cleaned — and that render_quad_views is banned because it deadlocks the server. Getting the capture branch wrong produces an uncomparable image, a wedged Houdini session, or a polluted scene that the next frank-build run trips over. Load only the connected family's pack; never load the absent family's.

Also load knowledge/verification.md for the pinned-camera discipline, silhouette-comparison method, and the human-acceptance doctrine the loop enforces.

Stage 3: Capture from the pinned camera

Record the camera once, on the first iteration, and reuse it for every capture in the loop. Note the viewport/camera position, target, and lens/focal-length (or the named view) in the review's working notes. On every later iteration, re-capture from those exact values — never let the camera drift, or the comparison is meaningless.

Pick the camera so the capture frames the subject the way the reference does (front-orthographic is the standard silhouette view). Capture additional canonical views (left, top, perspective) only if a finding needs a second angle; each extra view also gets a pinned camera recorded once.

Then capture per the connected family's branch:

Rhino branch — inline capture

1. Call capture_viewport(...) from the pinned camera / named view. It returns the image inline (no scaffolding nodes, no banned quad tool — Rhino's capture path is the simpler half).
2. Write the inline image to a file path under the run directory (e.g. /tmp/frank/frank-review/$RUN_ID/capture.png) so the reviewers can Read it. The reviewers receive the path, not the inline bytes.
3. That written file path is the capture_path you pass to both reviewers in Stage 4.

Houdini branch — render to disk, read back, clean scaffolding

1. Call render_single_view(orthographic=..., rotation=<pinned>, render_path=<run-dir path>) at the pinned rotation. Never call render_quad_views — it stacks four unbounded main-thread OpenGL ROP renders and deadlocks the server (see the Capture POLICY you loaded in Stage 2).
2. Read the file at render_path — that is the capture. Ignore the inline image_base64 the call also returns; it is large (~56 KB for 512×512) and overflows tool-result buffers. Rely on the file on disk.
3. Delete the three scaffolding nodes render_single_view adds to the scene, for scope isolation so the next frank-build run starts clean:
   • /obj/MCP_CAMERA
   • /obj/MCP_CAM_CENTER
   • /out/MCP_OGL_RENDER
4. The path you Read is the capture_path you pass to both reviewers in Stage 4.

In both branches, also gather the geometry summary / inspection data frank-geometry-reviewer needs (from the survey call in 1.1 plus per-object inspection — get_object_info on Rhino, get_scene_info/introspection on Houdini): object/topology counts, bounding-box extents, units, and any naked-edge / self-intersection signals the tool exposes. The reviewer reasons over this data, not the live document.

Stage 4: Dispatch the reviewers

4.1 Generate the RUN_ID

Generate a unique run identifier before dispatching any reviewer. It scopes both reviewers' JSON artifacts to one directory so the merge step in Stage 5 can find them.

RUN_ID=$(date +%Y%m%d-%H%M%S)-$(head -c4 /dev/urandom | od -An -tx1 | tr -d ' ')
mkdir -p "/tmp/frank/frank-review/$RUN_ID"

Pass RUN_ID as a named field in each reviewer's Task payload (alongside the capture path and the reference path). The reviewers read RUN_ID from the payload — they do not generate their own — and write their findings to /tmp/frank/frank-review/$RUN_ID/<reviewer>.json. A mismatch here silently breaks the merge.

4.2 Dispatch both reviewers in parallel

frank-silhouette-critic is a vision task — it compares two images — so override its dispatch model to a vision-capable tier at the dispatch site (its frontmatter stays model: inherit). frank-geometry-reviewer reasons over the geometry data and inherits the session model.

Run these agents in parallel:

• Task frank-silhouette-critic(Adversarially compare the captured render against the reference and flag silhouette/proportion/framing/feature mismatches. capture_path={path written in Stage 3}; reference_path={the reference image}; RUN_ID={the value generated in 4.1}; framing notes={the pinned view, e.g. "front-orthographic"} and intent={what the model is supposed to be}. Write your JSON to /tmp/frank/frank-review/{RUN_ID}/frank-silhouette-critic.json per your output contract.) — dispatch on a vision-capable model (override at this site).
• Task frank-geometry-reviewer(Reason over the geometry summary/inspection data and flag topology/scale/units/naked-edge/self-intersection/manifoldness issues. capture_path={path written in Stage 3}; reference_path={the reference image}; geometry data={the summary + inspection gathered in Stage 3}; RUN_ID={the value generated in 4.1}; units/tolerance={from the survey}. Write your JSON to /tmp/frank/frank-review/{RUN_ID}/frank-geometry-reviewer.json per your output contract.)

Dispatch both by their bare, frank-prefixed names — Task frank-silhouette-critic(...) and Task frank-geometry-reviewer(...) — never strip the prefix and never add a path namespace. Omit the mode parameter so the user's permission settings apply.

The reviewers hold no MCP tools by design; they Read the capture and reference files you wrote and return JSON. They do not touch the live model.

Stage 5: Merge the findings

After both reviewers complete, read their JSON artifacts from /tmp/frank/frank-review/$RUN_ID/ and merge them into one finding set. Each reviewer wrote the pinned envelope:

{
  "reviewer": "silhouette-critic",
  "findings": [ { "title": "...", "severity": "P1", "lane": "silhouette", "confidence": 75, "...": "..." } ],
  "residual_risks": [],
  "testing_gaps": []
}

reviewer is silhouette-critic / geometry-reviewer; lane is silhouette / geometry; confidence is exactly one of 100, 75, 50, 25. Merge steps:

1. Validate. Check each artifact has the top-level keys reviewer, findings, residual_risks, testing_gaps, and that each finding carries title, severity (P0–P3), lane, and confidence (one of the four anchors). Drop malformed findings and record the drop count.
2. Handle an absent artifact. If a reviewer's file is missing (the reviewer errored or timed out), note the missing reviewer and proceed with what's present — a one-reviewer merge is degraded but valid. Do not block the loop on a missing artifact; record "geometry lane unreviewed this iteration" (or whichever) for the human.
3. Deduplicate across lanes. The two reviewers police separate lanes (silhouette vs geometry) and a finding usually carries the lane of its author, so cross-lane duplicates are rare — but when both flag the same observable defect (e.g. silhouette notes a render gap that geometry confirms as a naked edge), merge them: keep the higher severity, keep the higher confidence anchor, and note both lanes flagged it (stronger signal).
4. Confidence gate. Suppress findings at anchor 25 (the reviewers already self-suppress these). Keep 50-anchor findings as soft signal — surface them with the residual risks rather than dropping them, and never drop a P0/P1 just because it's at 50.
5. Collect coverage. Union the residual_risks and testing_gaps arrays across both reviewers — these tell the human which views couldn't be compared and which concerns one lane handed to the other.

Stage 6: Present to the human, propose tweaks, iterate

Present the result to the human — the acceptance authority — as a side-by-side:

1. Side-by-side. Show the reference and the latest capture together (file paths, and the pinned view they were compared at), so the human sees what the reviewers saw.
2. Merged findings, by lane. List the merged findings grouped by reviewer lane (silhouette / geometry), each with its severity, confidence, the concrete observation, and the proposed fix. Surface the residual risks and testing gaps from Stage 5 (including any unreviewed lane from an absent reviewer).
3. Propose parameter tweaks. For each actionable finding, translate it into a concrete parameter change for frank-build (e.g. "raise P['turns'] from 2.0 to 2.4 to add the missing half-coil"; "close the top profile curve to a point, not a fillet"). frank-review proposes; frank-build applies, re-runs, and produces the next geometry — the review never edits the generator itself.
4. Ask the human (see Interaction Method). Single-select: Accept (fidelity target met — done), Iterate (apply the proposed tweaks via frank-build, then re-capture and re-review), or Override / adjust (the human changes the target, the tweaks, or the camera). Never self-certify; never silently skip this question.

On Iterate: hand the proposed parameter tweaks to frank-build, then return to Stage 3 and re-capture from the same pinned camera recorded on iteration 1 (do not re-record it). Generate a fresh RUN_ID for the new reviewer dispatch (Stage 4). Increment the iteration count.

Cap the loop at ~6 iterations. Track the count in the working notes.

Stage 7: At the iteration cap without acceptance

If the loop reaches the cap and the human has not accepted, do not auto-accept — the budget running out is not a sign-off. Instead:

1. Present the latest capture alongside the reference (the same side-by-side).
2. State the cap was reached — name the iteration count and that the fidelity target was not met within it.
3. List the unresolved findings by reviewer lane — what silhouette still flags, what geometry still flags, and the residual risks/testing gaps — so the human sees exactly what's outstanding.
4. Ask the human to accept or override (see Interaction Method): accept the current state despite the open findings, raise the cap and continue, change the target/tweaks/camera, or stop.
5. If a systematic blocker surfaced — a repeating mismatch the parameter loop can't resolve, a primitive that fights the form, a recurring guard warning — suggest /frank-compound to record the learning so the next build doesn't rediscover it.

Cross-References

• Reference packs (Capture POLICY this STOP-gate-loads): references/rhino-mcp.md (inline capture_viewport), references/houdini-mcp.md (render_single_view to disk, clean scaffolding, render_quad_views banned)
• Canon this loop enforces: knowledge/verification.md (pinned-camera, silhouette comparison, human acceptance), knowledge/parametric-scripting.md (the param tweaks route back to the build)
• Reviewers dispatched (and the shared JSON envelope/path contract): agents/frank-silhouette-critic.agent.md, agents/frank-geometry-reviewer.agent.md
• Upstream / downstream skills: frank-plan (wrote the verification assertions this checks), frank-build (applies the proposed parameter tweaks and re-runs), frank-compound (records a systematic blocker the loop couldn't resolve)