--- name: platformplatform/ultra-review source: https://app.decimal.ai/s/platformplatform-ultra-review@1/SKILL.md source_sha256: a7df518a2ecf --- # 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//ultra-review//`: ``` .workspace//ultra-review// ├── CONTEXT.md # Diff summary, [feature]/[task] excerpts, env, scope ├── ROSTER.md # Final agent list with affinity clusters ├── round1/ │ └── .md # One file per agent ├── round2/ │ ├── ASSIGNMENT.md # Reviewer-to-author table from Round 1 triage │ └── __on__.md # One file per cross-review pair ├── round3/ │ └── .md # One file per agent (final findings) └── SUMMARY.md # Orchestrator digest, deduplicated, prioritized ``` Resolve `` 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: " — ". 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`. ### 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 OTHER AGENTS WORKING IN PARALLEL CONTEXT Read in order before starting: 1. .workspace//ultra-review//CONTEXT.md 2. .workspace//ultra-review//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//ultra-review//round1/.md using the template below. Return ONLY this one-line triage (nothing else): `DONE: | findings= | critical= | high= | medium= | low= | uncertain= | hottest=` (`uncertain` = Likely + Possible. `hottest` = your most concerning finding or `—`. Findings live in the file, never returned as prose.) TEMPLATE # Round 1 — ## Scope ## Discovery ## Method ## Findings ### F1: **Severity (preliminary):** Critical | High | Medium | Low **Confidence:** Certain | Likely | Possible **Location:** **Description:** **Evidence:** **Why it matters:** **Rough fix idea:** **Open questions:** ### F2: ... (repeat per finding) ## What you looked at and found clean ## What you couldn't reach ``` 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//ultra-review//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 . In Round 1 you produced findings on your own 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. - (scope: ) Read: .workspace//ultra-review//round1/.md Write: .workspace//ultra-review//round2/__on__.md - , , ... — 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 peers". TEMPLATE (one per peer) # Round 2 — reviewing Author findings file: ## Verdicts ### On 's F1: **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.