Trigger only on explicit "ultra review" or "ultra-review". An agent skill from platformplatform/PlatformPlatform.

MITAuto-check passed

Install Ultra Review

skills CLI
$ npx skills add platformplatform/PlatformPlatform --skill ultra-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install platformplatform/PlatformPlatform ultra-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-src

Use ~/.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/

Facts

Skill name
ultra-review
GitHub stars
441
Token cost
~5.9k tokens
SKILL.md length
1,569 words
Files
1
Skills in repo
18
Repo updated
First seen
Licence
MIT

At a glance

Trigger only on explicit "ultra review" or "ultra-review". An agent skill from platformplatform/PlatformPlatform.

  • Works in 10 steps: Interview → Pre-fetch shared context → Co-design the agent roster → …
  • Ly on explicit ultra review
  • SKILL.md covers Invocation modes, Core principles, Output structure and Workflow, plus 1 more section
  • Calls git

What it does

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.

When your agent uses it

  • Ly on explicit ultra review

Example prompts

  • “ultra review”
  • “ultra-review”
  • “/ultra-review”

Requirements

  • Pre-approved tools (allowed-tools): *

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. Interview
  2. Pre-fetch shared context
  3. Co-design the agent roster
  4. Co-design review affinities
  5. Round 1 — Discovery (all agents in parallel)
  6. Triage Round 1 results and build the Round 2 assignment
  7. Round 2 — Cross-review (all reviewers in parallel)
  8. Round 3 — Final findings + implementations (all agents in parallel)
  9. Round 4 — Orchestrator digest
  10. Present + write findings sink

What it can do on your machine

Read from SKILL.md and the folder at commit 269de1c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • *

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    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.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

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.

Always · name and description, kept in context so the agent knows when to use it
~18
When it runs · the whole SKILL.md, loaded when a task matches
~5.9k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from platformplatform/PlatformPlatform at commit 269de1c, republished under its MIT licence (© platformplatform). 1,569 words, ~5,859 tokens.

Download SKILL.mdSave it as .claude/skills/ultra-review/SKILL.md (or your agent's skills folder).
name
ultra-review
description
Trigger only on explicit "ultra review" or "ultra-review".
allowed-tools
*

Ultra Review Workflow

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.

Invocation modes

  • Interactive (user typed "ultra review"): run STEP 1 interview. STEP 10 offers either [PRODUCT_MANAGEMENT_TOOL] tasks or TASKS.md.
  • Autonomous (team-lead invocation at feature completion): the caller supplies scope, risk hotspots, size, and confidence policy directly. Skip STEP 1's user questions. Skip STEP 10's chat presentation. Always write TASKS.md; never write to [PRODUCT_MANAGEMENT_TOOL]. Return the TASKS.md path to the caller.

Core principles

  • Depth, not token-saving. Spend whatever it takes. Use Explore subagents for code search, MCP tools (Stripe, [PRODUCT_MANAGEMENT_TOOL], Aspire, DB, etc.), and any other tooling. Prefer Perplexity over WebSearch for online research.
  • Generic by design. Roster, clusters, and focus areas are co-designed with the user every time. No fixed catalog.
  • 80/20. Agents spend ~80% on the few highest-risk areas (deep dive), ~20% sanity-scanning the rest.
  • No cap on agent count. Match the change: a 20K-line PR touching money, integrations, and migrations may warrant 25+ agents; a small UI fix may warrant 4.
  • Multiple agents on hot areas. Assign 3-4 agents to each high-risk area from different angles. Overlap is expected and strengthens signal.
  • Agents run independent and parallel. No coordinating, no splitting work mid-flight.
  • False-positive hunt. Round 2 disproves findings, not validates them. Survivors must be defensible.
  • Confidence is categorical, not percentages:
    • Certain — verified; can reproduce, cite exact code paths, quote evidence.
    • Likely — strong evidence, one step short of full verification. Must state what would close the gap.
    • Possible — plausible from patterns or partial evidence; not verified. Must state the gap.
  • Confidence policy (set in interview):
    • Default policy: "Certain only" (drop weaker) or "Allow Likely and Possible with explanation" (keep weaker findings, each with a "Why not Certain" note).
    • High-impact exception: agents flagged high-impact (security, data loss, privacy, regulatory) may always keep Likely and Possible findings with an uncertainty note, regardless of default. Better to surface a possible breach than drop it.

Output structure

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, prioritized

Resolve <branch-name> from git branch --show-current. Use local-time YYYYMMDD-HHmm. Slugify agent names (lowercase, hyphenated).

Workflow

STEP 1: Interview

Infer first:

  • Scope: current branch (git branch --show-current) and full diff vs main (or the base the user named).
  • [Feature] / [task] context: scan branch commits and conversation for [feature]/[task] references. Fetch any found.
  • Environment: current worktree, running locally as configured.

Then use AskUserQuestion to confirm and fill gaps. Batch up to 4 questions per call. Cover:

  1. Scope — confirm what's being reviewed.
  2. Environment — anything non-standard agents should know.
  3. Risk hotspots — areas the user wants extra eyes on.
  4. Deprioritize — areas to skip or treat lightly.
  5. Size — small (~5 agents), medium (~10-15), or large (20+). Default from diff size.
  6. Confidence policy (single-select): "Certain only" (drop weaker) or "Allow Likely and Possible with explanation" (keep weaker with a "Why not Certain" note — recommended for thorough reviews where missing a real issue is worse than surfacing a maybe).
STEP 2: Pre-fetch shared context

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:

  • Branch and base (so agents run their own diff/log commands).
  • Short category breakdown of changed files (counts per area: backend / frontend / migrations / tests / config).
  • [Feature] and [task] excerpts from [PRODUCT_MANAGEMENT_TOOL] if provided.
  • Special environment notes from the interview.
  • Pointers (not contents) to relevant .claude/rules/ files.
STEP 3: Co-design the agent roster

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:

  • Identify which areas the diff touches and which carry highest risk (domain logic, security, migrations, third-party integrations, concurrency, multi-tenancy, data consistency, API contracts).
  • Propose 3-4 agents per hot area from different angles (e.g. "correctness of the math", "behavior under failure", "what happens during a deploy"), 1-2 on lighter areas.
  • Each scope is one line, two at most: "<Area> — <angle / concern>". Examples:
    • "Authentication and session handling — token lifecycle, replay, timing-sensitive checks"
    • "Schema migrations — backwards compatibility and backfill correctness"
  • Do NOT enumerate specific classes, methods, files, columns, line numbers, or outcomes in the scope.
  • No "general" agents. Every agent has a sharp focus.
  • Flag each agent as high-impact (yes/no) — areas where false negatives are worse than false positives (security, data loss, privacy, regulatory). High-impact agents may always keep Likely and Possible findings with an uncertainty note, regardless of STEP 1 policy.

Present the roster (name, scope, high-impact flag, rationale) and use AskUserQuestion to confirm and iterate.

STEP 4: Co-design review affinities

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.

Show full SKILL.md (652 more words)Show less
STEP 5: Round 1 — Discovery (all agents in parallel)

Launch every agent in a single message with one Agent tool call per agent.

Pick subagent type per scope:

  • Backend / .NET / domain logic / database / migrations → backend
  • Frontend / React / TypeScript / UX / accessibility / i18n → frontend-reviewer
  • E2E / Playwright / test architecture → qa-reviewer
  • Anything else (cross-cutting security, infra, integration analysis) → general-purpose

Each 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.

STEP 6: Triage Round 1 results and build the Round 2 assignment

Use the triage summaries (NOT the full findings) to design the assignment.

Goals:

  • Reviewers spend time where signal lives: an author with 0 findings needs one quick sanity check; the most concerning results (multiple Critical/High, or a single alarming "hottest") deserve 6-8 independent reviewers.
  • Each reviewer carries ~2-3 reviews; every author gets at least one external reviewer. Top reviewers for hot authors come from the matching affinity cluster but may cross clusters when scope demands.

Heuristic (adjust freely):

  • Findings = 0 → 1 reviewer (light sanity check)
  • Findings ≤ 3 and no Critical/High → 2 reviewers
  • Critical/High findings or a serious "hottest" → 4-8 reviewers, scaled by severity
  • +1-2 reviewers if the author is high-impact

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.

STEP 7: Round 2 — Cross-review (all reviewers in parallel)

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.

STEP 8: Round 3 — Final findings + implementations (all agents in parallel)

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.

STEP 9: Round 4 — Orchestrator digest

Read every round3/*.md file. This is the only point findings enter your context.

Produce .workspace/<branch-name>/ultra-review/<timestamp>/SUMMARY.md:

markdown
# 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:

  • Two agents at the same location → merge. Note both under "Raised by". Independent confirmation is strong signal — call it out, and bump confidence one level if both are Likely (two independent Likelys ≈ Certain).
  • Disagreement on severity → take the higher (carry its uncertainty note if below Certain). Disagreement on implementation → prefer the more concrete one, note the alternative.
  • Likely/Possible findings → preserve the "Why not Certain" note in the summary.
STEP 10: Present + write findings sink

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:

  • TASKS.md — one row per Critical, High, and Medium in .workspace/<branch-name>/ultra-review/<timestamp>/TASKS.md
  • [PRODUCT_MANAGEMENT_TOOL] — one [task] per Critical and High (or Critical/High/Medium)
  • Let me select which findings
  • Handle manually

[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.

TASKS.md format
markdown
# 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 — ...

Guidelines

DO:

  • Co-design roster and clusters with the user every time. Tailor to the diff.
  • Launch each round in a single parallel message.
  • Make agents write findings to disk and return only DONE: lines. Never return findings as text.
  • Read agent outputs only at digest time (Round 4).
  • Prefer Perplexity over WebSearch.
  • Be specific in agent prompts — "review for security" is too vague; "review every new endpoint and handler for missing tenant scoping" is right.

DON'T:

  • Use a fixed agent catalog. Every review is custom.
  • Cap agent count arbitrarily. Match the change.
  • Let agents coordinate or split work mid-flight.
  • Treat overlap as wasted work. Overlap is signal.
  • Read agent findings into context before Round 4.
  • Create [tasks] in [PRODUCT_MANAGEMENT_TOOL] before presenting findings to the user and getting approval.
  • Commit, push, or amend anything. Findings are the output. Fixes are a separate workflow.

© platformplatform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/ultra-review of platformplatform/PlatformPlatform.

Open the folder on GitHubat commit 269de1c

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Nemotron 3 Ultra Text2sql LoraNVIDIA-NeMo/Nemotron2.1k—~1.9kAutomated safety check: PassApache-2.0
Team Ultra Analyzecatlog22/Claude-Code-Workflow2.1k—~2.4kAutomated safety check: NotesMIT

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More from platformplatform/PlatformPlatform

All 18 skills in this repo
  • Create Prd

    platformplatform/PlatformPlatform

    Create a product requirement document (PRD) for a new feature.

    441 GitHub stars~3k tokensUpdated 15 days ago
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  • Rebuild Branch

    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.

    441 GitHub stars~2.1k tokensUpdated 15 days ago
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  • Aspire Restart

    platformplatform/PlatformPlatform

    Start or restart the .NET Aspire AppHost via the developer CLI.

    441 GitHub stars~415 tokensUpdated 15 days ago
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  • Commit

    platformplatform/PlatformPlatform

    Commit session changes to git. An agent skill from platformplatform/PlatformPlatform.

    441 GitHub stars~567 tokensUpdated 15 days ago
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  • E2E

    platformplatform/PlatformPlatform

    Run end-to-end Playwright tests via the developer CLI. An agent skill from platformplatform/PlatformPlatform.

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Questions about Ultra Review

What does Ultra Review do?

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".

When should I use Ultra Review?

Ultra Review fits situations like: ly on explicit ultra review.

How do I install Ultra Review in Claude Code?

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.

How do I install Ultra Review in Codex?

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.

Can I use Ultra Review in Cursor, Gemini CLI or GitHub Copilot?

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.

What does Ultra Review need to run?

Going by SKILL.md and its folder, Ultra Review needs the command-line tools its instructions call (git). Its frontmatter pre-approves these tools: *.

Does Ultra Review access the network?

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.

Is Ultra Review safe to install?

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.

What licence does Ultra Review use?

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.

How many tokens does Ultra Review use?

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.

What are the alternatives to Ultra Review?

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.

Who maintains Ultra Review?

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.