Nemotron Ultra
NVIDIA-NeMo/Nemotron
Reference desk for NVIDIA Nemotron 3 Ultra (550B-A55B) — architecture, NVFP4 pretraining, SFT, MOPD (multi-teacher on-policy distillation), MTP boosting, quantization, inference.
Trigger only on explicit "ultra review" or "ultra-review". An agent skill from platformplatform/PlatformPlatform.
$ npx skills add platformplatform/PlatformPlatform --skill ultra-review -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install platformplatform/PlatformPlatform ultra-review --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/platformplatform/PlatformPlatform.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/ultra-review .claude/skills/ultra-review && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "ultra-review" agent skill from https://github.com/platformplatform/PlatformPlatform/tree/main/.claude/skills/ultra-review into .claude/skills/ultra-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ultra-review", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/platformplatform/PlatformPlatform/tree/main/.claude/skills/ultra-reviewType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add platformplatform/PlatformPlatform --skill ultra-review -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install platformplatform/PlatformPlatform ultra-review --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/platformplatform/PlatformPlatform.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/ultra-review .agents/skills/ultra-review && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ultra-review" agent skill from https://github.com/platformplatform/PlatformPlatform/tree/main/.claude/skills/ultra-review into .agents/skills/ultra-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ultra-review", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add platformplatform/PlatformPlatform --skill ultra-review -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install platformplatform/PlatformPlatform ultra-review --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/platformplatform/PlatformPlatform.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/ultra-review .cursor/skills/ultra-review && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ultra-review" agent skill from https://github.com/platformplatform/PlatformPlatform/tree/main/.claude/skills/ultra-review into .cursor/skills/ultra-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ultra-review", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/platformplatform/PlatformPlatform.git --path .claude/skills/ultra-review--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add platformplatform/PlatformPlatform --skill ultra-review -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install platformplatform/PlatformPlatform ultra-review --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/platformplatform/PlatformPlatform.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/ultra-review .gemini/skills/ultra-review && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ultra-review" agent skill from https://github.com/platformplatform/PlatformPlatform/tree/main/.claude/skills/ultra-review into .gemini/skills/ultra-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ultra-review", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install platformplatform/PlatformPlatform ultra-reviewInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add platformplatform/PlatformPlatform --skill ultra-review -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/platformplatform/PlatformPlatform.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/ultra-review .github/skills/ultra-review && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ultra-review" agent skill from https://github.com/platformplatform/PlatformPlatform/tree/main/.claude/skills/ultra-review into .github/skills/ultra-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ultra-review", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add platformplatform/PlatformPlatform --skill ultra-review -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install platformplatform/PlatformPlatform ultra-review --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/platformplatform/PlatformPlatform.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/ultra-review .opencode/skills/ultra-review && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ultra-review" agent skill from https://github.com/platformplatform/PlatformPlatform/tree/main/.claude/skills/ultra-review into .opencode/skills/ultra-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ultra-review", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
ultra-reviewTrigger only on explicit "ultra review" or "ultra-review". An agent skill from platformplatform/PlatformPlatform.
Ultra Review is an agent skill from platformplatform/PlatformPlatform. Trigger only on explicit "ultra review" or "ultra-review".
Its SKILL.md is about 5.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: A platform designed for building enterprise-grade, multi-tenant products using Azure, .NET, React, TypeScript, Infrastructure as Code, etc. The licence is MIT.
10 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 269de1c. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
*From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
gitFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Ultra Review loads about 5.9k tokens when it runs. Until then it costs about 18 tokens; SKILL.md has 1,569 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from platformplatform/PlatformPlatform at commit 269de1c, republished under its MIT licence (© platformplatform). 1,569 words, ~5,859 tokens.
.claude/skills/ultra-review/SKILL.md (or your agent's skills folder).Orchestrate a multi-round, multi-agent deep review. Goal: depth over speed. Maximize real problems found, minimize false positives via adversarial cross-critique.
The orchestrator never writes findings. Design the review, spawn agents, digest results.
[PRODUCT_MANAGEMENT_TOOL] tasks or TASKS.md.TASKS.md; never write to [PRODUCT_MANAGEMENT_TOOL]. Return the TASKS.md path to the caller.All artifacts live in .workspace/<branch-name>/ultra-review/<YYYYMMDD-HHmm>/:
.workspace/<branch-name>/ultra-review/<timestamp>/
├── CONTEXT.md # Diff summary, [feature]/[task] excerpts, env, scope
├── ROSTER.md # Final agent list with affinity clusters
├── round1/
│ └── <agent-slug>.md # One file per agent
├── round2/
│ ├── ASSIGNMENT.md # Reviewer-to-author table from Round 1 triage
│ └── <reviewer>__on__<author>.md # One file per cross-review pair
├── round3/
│ └── <agent-slug>.md # One file per agent (final findings)
└── SUMMARY.md # Orchestrator digest, deduplicated, prioritizedResolve <branch-name> from git branch --show-current. Use local-time YYYYMMDD-HHmm. Slugify agent names (lowercase, hyphenated).
Infer first:
git branch --show-current) and full diff vs main (or the base the user named).[feature]/[task] references. Fetch any found.Then use AskUserQuestion to confirm and fill gaps. Batch up to 4 questions per call. Cover:
Write CONTEXT.md with the small, shared inputs every agent needs. The diff itself does not go here — each agent pulls its own slice.
Include:
[Feature] and [task] excerpts from [PRODUCT_MANAGEMENT_TOOL] if provided..claude/rules/ files.Propose a tailored roster from the diff (file paths, commit messages, [feature]/[task] context). No fixed catalog.
Keep scopes open-ended. The biggest failure mode is the orchestrator confining agents to narrow checklists and missing what the orchestrator didn't think of. Name AREAS worth a deep dive — do not pre-investigate them. A one-line scope naming an area and an angle is right. Five lines listing specific classes, files, methods, columns, or outcomes is wrong: it tells the agent what to find instead of letting them discover it.
You do not deeply read code in this step. Agents do.
Guidelines:
<Area> — <angle / concern>". Examples:Present the roster (name, scope, high-impact flag, rationale) and use AskUserQuestion to confirm and iterate.
Cluster the roster into 3-5 affinity clusters (e.g. "Domain Logic", "Integrations", "Frontend & UX"). Clusters are orchestrator-side hints for Round 2 reviewer selection — never shown to agents, never hard partitions. Round 2 assignment is dynamic and may cross clusters.
Examples (design fresh per review): a domain-heavy review may cluster Domain Logic / Integrations / Backend Quality / Frontend & UX; a security-heavy review may cluster AuthN/AuthZ / Data Exposure / Input Validation / Reliability.
Present clusters with rationale. Confirm via AskUserQuestion. Write final roster + clusters to ROSTER.md.
Launch every agent in a single message with one Agent tool call per agent.
Pick subagent type per scope:
backendfrontend-reviewerqa-reviewergeneral-purposeEach agent's prompt:
You are an Ultra Review agent. Your scope is narrow and focused.
THIS IS NOT A NORMAL REVIEW
Not a line-by-line review. Not the validation pass you usually run alongside an engineer. You run solo, in parallel with many peers, against a large branch. Find REAL problems — bugs, defects, risks, design flaws, edge cases that break under load, data corruption paths, security gaps, anything that should not ship. Skip nitpicks, style, surface-level checks. Spend effort on the highest-risk areas of your scope. Round 2 reviewers will try to disprove your findings — commit only to claims you can defend with concrete evidence.
YOUR SCOPE
<scope from roster — 1-2 lines>
OTHER AGENTS WORKING IN PARALLEL
<full roster — name and scope per agent — overlap is expected and welcome>
CONTEXT
Read in order before starting:
1. .workspace/<branch-name>/ultra-review/<timestamp>/CONTEXT.md
2. .workspace/<branch-name>/ultra-review/<timestamp>/ROSTER.md
SHARED ENVIRONMENT
Current worktree, running locally. Resolve specifics yourself: db-query (read-only), Aspire MCP, Stripe MCP, [PRODUCT_MANAGEMENT_TOOL] MCP. Non-standard items: see CONTEXT.md "Special environment notes".
YOUR JOB
Find real problems in your scope. Your scope names an area, not a checklist — the orchestrator deliberately did NOT pre-list classes, files, edge cases, or outcomes. You are the domain expert. Drive your own investigation.
Phase 1 — Discovery (first): read the code and build a mental model (entry points, boundaries, invariants, dependencies). Identify risk — where math is hard, state mutated, failures cascade, assumptions about external systems live, what is new vs. the existing pattern. Commit to deep-diving the few highest-risk subareas.
Phase 2 — Deep dive (80%): investigate chosen subareas thoroughly. Read every relevant path. Run DB queries. Reproduce edge cases. Verify external assumptions with MCP tools or Perplexity.
Phase 3 — Sanity scan (20%): sweep the rest of your area lightly. Note anything off that does not merit a deep dive.
Tools: read code, grep/find, Explore subagents (subagent_type=Explore) for broad search, Perplexity (preferred over WebSearch), db-query skill (read-only), Stripe MCP, Aspire MCP, [PRODUCT_MANAGEMENT_TOOL] MCP. Do not save tokens.
Be specific. Cite file:line. Quote code, query results, and external sources.
OUTPUT
Write findings to: .workspace/<branch-name>/ultra-review/<timestamp>/round1/<your-slug>.md using the template below.
Return ONLY this one-line triage (nothing else):
`DONE: <path> | findings=<N> | critical=<N> | high=<N> | medium=<N> | low=<N> | uncertain=<N> | hottest=<short title or "—">`
(`uncertain` = Likely + Possible. `hottest` = your most concerning finding or `—`. Findings live in the file, never returned as prose.)
TEMPLATE
# Round 1 — <Agent name>
## Scope
<one paragraph — your framing, derived from the code you read>
## Discovery
<area map: entry points, invariants, risky subareas and why. Then the subareas you deep-dived vs. sanity-scanned, one-line reason each.>
## Method
<files read, queries run, sources consulted>
## Findings
### F1: <short title>
**Severity (preliminary):** Critical | High | Medium | Low
**Confidence:** Certain | Likely | Possible
**Location:** <file:line, or "system-wide">
**Description:** <what's wrong>
**Evidence:**
<code snippet, query result, or quoted source>
**Why it matters:** <impact>
**Rough fix idea:** <optional, hand-wavy OK at this stage>
**Open questions:** <what you couldn't verify>
### F2: ... (repeat per finding)
## What you looked at and found clean
<short list — helps Round 2 reviewers calibrate>
## What you couldn't reach
<gaps — areas you couldn't fully assess and why>After launching, wait for all agents. Do not read findings into context. Verify each agent returned a DONE: triage line and the file exists. On failure, decide: retry, reassign, or proceed without.
Use the triage summaries (NOT the full findings) to design the assignment.
Goals:
Heuristic (adjust freely):
Write the explicit reviewer-to-author table (with round1 file paths) to .workspace/<branch-name>/ultra-review/<timestamp>/round2/ASSIGNMENT.md before launching Round 2.
Launch all reviewers in a single message with one Agent tool call per reviewer. Reuse each reviewer's Round 1 subagent type.
Each reviewer's prompt:
You are <Agent X>. In Round 1 you produced findings on your own scope (<scope>). In Round 2 you read a list of peers' findings and try to PROVE THEM WRONG.
THIS IS NOT A NORMAL REVIEW
Adversarial false-positive hunt, not a friendly walkthrough. Skip nitpicks and style. Focus on whether each claim is real, reachable, and at the right severity. Default to skepticism — adversarial confirmation is the strongest signal a finding can get.
YOUR ASSIGNMENT
Review these peers. Use the exact write paths below — do not invent filenames.
- <Author A> (scope: <scope>)
Read: .workspace/<branch-name>/ultra-review/<timestamp>/round1/<author-a>.md
Write: .workspace/<branch-name>/ultra-review/<timestamp>/round2/<your-slug>__on__<author-a>.md
- <Author B>, <Author C>, ... — same pattern, substitute each author's slug.
CONTEXT
CONTEXT.md and ROSTER.md were loaded in Round 1. Re-read only to refresh a specific detail.
YOUR JOB
For each peer:
1. Read their Round 1 file.
2. For each finding, try to invalidate it independently. Look at the code yourself. Run queries. Spawn Explore subagents. Use Perplexity over WebSearch. Quote counter-evidence.
3. Write your critique to the exact path above, using the template.
You are an adversary, not a cheerleader. Severity inflation is a false positive. Common false positives: missing context the author overlooked, framework guarantees the author missed, issue exists but is unreachable in practice, two different code paths conflated. When you can confirm a finding, say so plainly with reproduced evidence.
OUTPUT
One file per peer. When all done, return: "DONE: reviewed <N> peers".
TEMPLATE (one per peer)
# Round 2 — <Your name> reviewing <Author name>
Author findings file: <path>
## Verdicts
### On <Author>'s F1: <title>
**Verdict:** Confirmed | Confirmed but severity wrong | Partial — <what holds> | False positive | Cannot verify
**Counter-evidence / corroboration:**
<what you found in the code, DB, or online>
**Suggested severity:** Critical | High | Medium | Low
**Notes:** <anything the author missed or got wrong>
### On <Author>'s F2: ... (repeat per finding)
## Findings the author missed (in their scope)
<optional — additional issues your research surfaced>Wait for all reviewers. Verify all critique files exist.
Each Round 1 agent produces their final document, taking critiques into account. Launch in parallel, reusing each agent's original subagent type.
Each agent's prompt:
You are <Agent X>. You wrote round1/<your-slug>.md and peers critiqued each finding in round2/*__on__<your-slug>.md. In Round 3 you finalize.
THIS IS NOT A NORMAL REVIEW
Finalization pass. Decide which Round 1 findings survive adversarial critique and are real enough to ship. Drop weak claims. Strengthen survivors with evidence and concrete implementation. Severity reflects impact, not how hard the finding was to find.
CONTEXT
CONTEXT.md and ROSTER.md were loaded earlier; re-read only to refresh. Read:
- .workspace/<branch-name>/ultra-review/<timestamp>/round1/<your-slug>.md
- All critiques at .workspace/<branch-name>/ultra-review/<timestamp>/round2/*__on__<your-slug>.md
CONFIDENCE POLICY
- Default policy: <"Certain only" or "Allow Likely and Possible with explanation" — from STEP 1>
- High-impact agent: <yes / no — from roster>
Confidence is categorical: Certain (verified, reproducible), Likely (strong evidence, one step short), Possible (plausible, not verified).
- "Certain only" and NOT high-impact: drop anything weaker than Certain.
- "Allow Likely and Possible" OR high-impact: keep Likely/Possible findings, each with a "Why not Certain" note stating the gap. Better to surface a possible issue than drop it.
YOUR JOB
For each Round 1 finding:
1. Read the critiques.
2. Do more research as needed: read more code, run more queries, Explore subagents, Perplexity, MCP tools.
3. Decide: KEEP at Critical/High/Medium with a confidence level, DOWNGRADE, or DROP — per policy.
4. Severity reflects impact, not confidence. A Certain Medium is fine. A Likely/Possible finding with a "Why not Certain" note is fine when policy permits.
5. For each kept finding, write a CONCRETE implementation: code, queries, migrations, config — whatever applies. Make a future engineer's job nearly mechanical.
6. Add new findings if your additional research surfaced any, under the same policy.
OUTPUT
Write to .workspace/<branch-name>/ultra-review/<timestamp>/round3/<your-slug>.md using the template.
Return only: "DONE: <path>".
TEMPLATE
# Round 3 — <Agent name> — Final
## Scope (recap)
<one line>
## Critical (must fix before merge)
### C1: <title>
**Location:** <file:line>
**Confidence:** Certain | Likely | Possible
**Description:** <what's wrong>
**Evidence:**
<code, query result, source>
**Why critical:** <user impact, data corruption, security exposure, regulatory, etc.>
**Round 2 critiques addressed:**
- <Reviewer X>: <what they said, how you resolved it>
**Why not Certain** (only when Likely or Possible): <what's missing, what would close the gap>
**Implementation:**
<concrete change — code, migration, config>
**Test:** <how to verify the fix>
### C2: ...
## High (should fix before merge) / ## Medium (worth considering)
Same template. Medium: only findings the policy permits.
## Dropped findings (from Round 1)
- F-X: <title> — Dropped because <reason, citing reviewer or own re-investigation>
## Notes for the orchestrator
<known overlaps with other agents' scopes, etc.>Wait for all agents.
Read every round3/*.md file. This is the only point findings enter your context.
Produce .workspace/<branch-name>/ultra-review/<timestamp>/SUMMARY.md:
# Ultra Review — <timestamp>
**Scope:** <branch / PR / diff range>
**Agents:** <N> (<G> affinity clusters)
**[Feature]/[task] context:** <links if any>
## Critical (must fix before merge)
### C1: <consolidated title>
**Raised by:** <agent-a>, <agent-b> *(independent confirmation)*
**Location:** <file:line>
**Summary:** <consolidated description>
**Why critical:** <impact>
**Implementation:**
<best implementation across agents — pick the most concrete and complete>
**Test:** <how to verify>
**Source files:** round3/<agent-a>.md, round3/<agent-b>.md
### C2: ...
## High / ## Medium
Same structure as Critical. Medium can be terser.
## Dropped during review
(one line each — raised in Round 1, dropped after critique. Audit trail.)
## Coverage map
Table: Area | Agent(s).
## Notable disagreements between agents
<short list — Round 2 critiques that flipped severity or dropped findings, especially the interesting ones>Deduplication rules:
Autonomous mode: skip presentation. Write TASKS.md (format below) and return its path to the caller. Done.
Interactive mode: present SUMMARY.md in chat — Critical first, then High, then a one-line list of Medium titles. Do not paste Medium implementations unless asked. Wait for the user (discuss, won't-fix, reprioritize, deeper investigation).
Once satisfied, AskUserQuestion:
.workspace/<branch-name>/ultra-review/<timestamp>/TASKS.md[PRODUCT_MANAGEMENT_TOOL] — one [task] per Critical and High (or Critical/High/Medium)[PRODUCT_MANAGEMENT_TOOL] tasks (when chosen): follow .claude/reference/product-management/[PRODUCT_MANAGEMENT_TOOL].md. Each [task]: title (imperative), description (consolidated finding from SUMMARY.md), link to parent [feature] if any, [Planned] status, current iteration.
# Ultra Review Tasks — <timestamp>
<one-line summary>. Full per-agent reports in `round3/`. Consolidated digest in `SUMMARY.md`.
**Merge blockers:** <comma-separated IDs of Critical + High>.
## Tally
- Critical: <N> (<IDs>)
- High: <N> (<IDs>)
- Medium: <N>
- Low / Nit: <N>
## Tasks
| ID | Status | Severity | Title | Files | Fix size | Source |
| --- | --- | --- | --- | --- | --- | --- |
| C-1 | ⏳ Open | Critical | <title> | <file:line, ...> | <~lines or n/a> | <agent-slug> |
| H-1 | ⏳ Open | High | ... | ... | ... | ... |
ID format: `<severity>-<index>` where severity is `C` (Critical), `H` (High), `M` (Medium), `L` (Low), `N` (Nit).
Status values (mirror the standard task lifecycle):
- `⏳ Open` — initial, not yet picked up
- `🔧 In progress` — engineer is working on the fix
- `👀 In review` — reviewer has it
- `✅ Done (<commit>)` — committed
- `🚫 Blocked — <reason>` — needs user input or external access
## Details
### C-1 — <title>
**Why**: <root cause>.
**Blast radius**: <impact>.
**Fix**: <concrete change>.
**Rule citation** (when applicable): <path>.
### H-1 — ...DO:
DONE: lines. Never return findings as text.DON'T:
[PRODUCT_MANAGEMENT_TOOL] before presenting findings to the user and getting approval.© platformplatform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .claude/skills/ultra-review of platformplatform/PlatformPlatform.
Open the folder on GitHubat commit 269de1c
Ultra Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Ultra Review this skillplatformplatform/PlatformPlatform | 441 | — | ~5.9k | Automated safety check: Pass | MIT | |
| Nemotron UltraNVIDIA-NeMo/Nemotron | 2.1k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| 3D Ultra Realistic WaterMengTo/Skills | 6.7k | — | ~2.7k | Automated safety check: Pass | MIT | |
| Ideogram Ultraartokun/comfyui-mcp | 800 | — | ~5.6k | Automated safety check: Pass | MIT | |
| Nemotron 3 Ultra Text2sql LoraNVIDIA-NeMo/Nemotron | 2.1k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Team Ultra Analyzecatlog22/Claude-Code-Workflow | 2.1k | — | ~2.4k | Automated safety check: Notes | MIT |
NVIDIA-NeMo/Nemotron
Reference desk for NVIDIA Nemotron 3 Ultra (550B-A55B) — architecture, NVFP4 pretraining, SFT, MOPD (multi-teacher on-policy distillation), MTP boosting, quantization, inference.
MengTo/Skills
Build an ultra-realistic open ocean in Three.js with a deep-water Gerstner spectrum shaded per pixel from analytic derivatives, each wave faded at its own pixel footprint, plus Fresnel sky-cube and…
artokun/comfyui-mcp
Build Ideogram 4 (Ideogram Ultra) txt2img and img2img workflows with the local open-weights model, dual conditional/unconditional models with DualModelGuider, Qwen3-VL text encoder, and structured…
NVIDIA-NeMo/Nemotron
Run the Nemotron-3 Ultra Text2SQL LoRA fine-tuning tutorial (NeMo Megatron-Bridge) end-to-end for the user on their SLURM cluster: data prep, distributed checkpoint conversion, and packed LoRA…
catlog22/Claude-Code-Workflow
Deep collaborative analysis team skill. An agent skill from catlog22/Claude-Code-Workflow.
catlog22/Claude-Code-Workflow
Deep collaborative analysis team skill. An agent skill from catlog22/Claude-Code-Workflow.
platformplatform/PlatformPlatform
Create a product requirement document (PRD) for a new feature.
platformplatform/PlatformPlatform
Rebuild a stale branch by cherry-picking each commit onto a fresh branch off main, using a ralph-loop to validate each commit (build, test, format, lint, optional e2e) before moving on.
platformplatform/PlatformPlatform
Diagnose and fix Lingui SWC plugin compatibility errors with Next.js, Rspack, or other SWC runtimes.
platformplatform/PlatformPlatform
Start or restart the .NET Aspire AppHost via the developer CLI.
platformplatform/PlatformPlatform
Commit session changes to git. An agent skill from platformplatform/PlatformPlatform.
platformplatform/PlatformPlatform
Run end-to-end Playwright tests via the developer CLI. An agent skill from platformplatform/PlatformPlatform.
Trigger only on explicit "ultra review" or "ultra-review". An agent skill from platformplatform/PlatformPlatform. Ultra Review is an agent skill from platformplatform/PlatformPlatform. Trigger only on explicit "ultra review" or "ultra-review".
Ultra Review fits situations like: ly on explicit ultra review.
Run `npx skills add platformplatform/PlatformPlatform --skill ultra-review -a claude-code`. Or copy the skill folder (.claude/skills/ultra-review in platformplatform/PlatformPlatform) into .claude/skills/ultra-review in your project. Claude Code loads it when a task matches its description.
Run `npx skills add platformplatform/PlatformPlatform --skill ultra-review -a codex`. Or copy the skill folder (.claude/skills/ultra-review in platformplatform/PlatformPlatform) into .agents/skills/ultra-review in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add platformplatform/PlatformPlatform --skill ultra-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ultra-review, .gemini/skills/ultra-review, .github/skills/ultra-review and .opencode/skills/ultra-review in your project.
Going by SKILL.md and its folder, Ultra Review needs the command-line tools its instructions call (git). Its frontmatter pre-approves these tools: *.
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Ultra Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 5.9k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Ultra Review: Nemotron Ultra (NVIDIA-NeMo/Nemotron, 2.1k stars), 3D Ultra Realistic Water (MengTo/Skills, 6.7k stars), Ideogram Ultra (artokun/comfyui-mcp, 800 stars) and Nemotron 3 Ultra Text2sql Lora (NVIDIA-NeMo/Nemotron, 2.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
platformplatform (a GitHub organization) maintains it in platformplatform/PlatformPlatform, which has 441 GitHub stars. The repository holds 18 skills in this directory. The repository was last updated on September 24, 2026.
Source: platformplatform/PlatformPlatform on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.