From Flagrare
Spawns five expert subagents (PM, engineer, designer, design engineer, end user) to evaluate product-direction questions with competing constraints, then synthesizes a single recommendation.
How this skill is triggered — by the user, by Claude, or both
Slash command
/flagrare:five-lens-reviewThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Spawn five specialized subagents in parallel — Senior PM, Senior Product Engineer, Senior Product Designer, Senior Design Engineer, and a realistic end user — each examining the same product-direction question through their own discipline's lens. Then synthesize their convergent themes, surface their disagreements, and produce a single actionable recommendation.
Spawn five specialized subagents in parallel — Senior PM, Senior Product Engineer, Senior Product Designer, Senior Design Engineer, and a realistic end user — each examining the same product-direction question through their own discipline's lens. Then synthesize their convergent themes, surface their disagreements, and produce a single actionable recommendation.
The output is a structured memo the user can decide against, not a slide deck of opinions.
Use it when ALL of these are true:
Skip this skill for:
The right test: would the user feel like the decision benefits from being interrogated from five disciplines? If yes, use this. If you're not sure, ask.
Before spawning anything, write a one-paragraph decision context that every persona will receive. The personas have no session memory; the brief is the only thing they see. It must contain:
If the question is ambiguous, ask the user to clarify before spawning. A vague brief produces vague memos that don't reconcile.
In a single message, dispatch five subagent calls using the Agent tool (or Task tool / whatever the runtime exposes) with model: "sonnet". All five receive the same decision context plus a persona-specific lens. They run concurrently and notify on completion.
Each persona's brief should follow this shape:
Adopt the lens of a [PERSONA]. Your job is to [PERSONA-SPECIFIC GOAL].
## Context (you have no session memory)
[The shared decision context from Step 1]
## The questions to think through
[Enumerated questions, 3-7 of them, framed for this persona's discipline]
## Your [PERSONA]-specific lens
[3-6 bullets pointing at what this persona uniquely contributes — see Step 3]
## Deliverable
[Structured memo shape — see Step 4. ~700-900 words.]
End with: "[A one-line takeaway prompt the persona must answer]"
The five lenses, with what each uniquely contributes:
Goal: Surface the right questions, articulate trade-offs, recommend a decision framework from product-strategy lens. Do NOT write code or get into implementation detail. Unique contribution: Which decisions are user-retention critical vs nice-to-have. What's the cheapest v0 answer + the eventual right answer. Which choices ENABLE future features vs LOCK US OUT. GDPR / data-retention concerns. What must decide NOW vs can defer. End with: "If we don't decide X, we'll regret it because Y."
Goal: Identify technical implications, data-model trade-offs, migration paths, edge cases. Get specific about implementation consequences of different decisions. Unique contribution: Which decisions create migration debt later. Where query complexity explodes. What testing gaps each decision creates. Indexing implications. Cheapest implementation that doesn't paint into a corner. Schema additions (add now vs defer). End with: "The one schema change I'd push to land THIS phase to avoid pain later is X."
Goal: Design the user's mental model, surface where current behavior contradicts intuition, recommend microcopy + visual decisions. Unique contribution: Where the user's mental model BREAKS with current behavior. Microcopy needed at each transition. IA decisions — new sections/views needed. The right level of nuance (surface everything vs hide complexity). Modal/drawer patterns for the explanatory moments. Voice-of-product alignment. End with: "The one design decision I'd make NOW to prevent the most confused-user moments later is X."
Goal: Recommend how to IMPLEMENT design decisions in concrete component patterns, state machines, and reactive UI that hold up across the feature's lifecycle. Unique contribution: Component-pattern recommendations for the consequences-confirmation moments. State shape — where filters/derived values live so changes don't ripple. Reactive pitfalls and the patterns that prevent them. Mobile-first considerations. Where to extract shared bases vs duplicate. Honest "where can we get away with less." End with: "The one component pattern I'd extract NOW to keep us honest is X."
Goal: A SPECIFIC person — not a focus-group abstraction. Adopt a real persona matching the product's audience (the framer of the brief should describe them: age, job, situation, what they came to the app for, what they fear losing). Voice: First person. Honest about what they'd actually want, what would confuse them, what would make them uninstall. Unique contribution: What would make them uninstall (worst case per transition). What would make them fall in love (best case). Where they'd be CONFUSED returning after time away. Tolerance for confirmation modals vs silent actions. The "I just want it to work" expectations that need no input. End with: "If you can only get ONE of these right, get X right because Y."
Do not try to start synthesizing until all five are back. Partial syntheses based on the first 2-3 responses miss the cross-discipline convergence that's the whole point.
When the last persona notifies completion, write the synthesis. Structure it like this:
## Convergent themes (where N+ lenses agree)
[A table OR a series of headers. For each convergent decision: the conclusion, which lenses support it, and the killer quote that captures why.]
## Disagreements worth surfacing
[Cases where two lenses propose incompatible answers. Show both with rationale. Recommend a resolution OR escalate the choice to the user.]
## What today's code/design gets wrong
[Concrete current-vs-recommended table for any behavior the personas converged on as broken.]
## The architecture/path that makes it cheap
[If the personas surfaced a unifying implementation pattern (a shared resolver, a single source of truth, an extracted modal base), name it explicitly.]
## What the spec gets wrong (if applicable)
[If the personas surfaced a place where the spec doc no longer matches the right answer.]
## The one-paragraph "if you only do one thing" recommendation
[The single highest-leverage decision distilled into one paragraph the user can act on without reading the rest.]
After synthesizing, deliver the recommendation to the user as flowing written prose (5-10 paragraphs), not as nested bullet points. The user just asked a strategic question; they need a written answer they can sit with, not a slide deck.
Each paragraph should:
Reserve bullet points and tables for places where the structure is genuinely tabular (e.g., "what today does vs what it should do").
End with the one-sentence summary — the single line that, if the user only reads one thing, captures the whole synthesis. Lead with the action, then the reason.
If the runtime exposes no Agent / Task tool (some nested-subagent contexts strip it), don't abort the skill — fall back to writing all five persona memos as five separate fully-in-character passes in a single batched message, then synthesize. The cross-pollination value drops but the multi-discipline coverage is preserved. State the fallback explicitly in your output so the user knows the parallelism was conceptual rather than literal.
Agent / Task, all five MUST run in parallel via a single dispatch message. Sequential burns 5× the wall-clock time and prevents fresh-eye cross-pollination. Use the fallback above only when the tool genuinely isn't accessible.This skill spawns 5 parallel subagents — substantial token + wall-clock cost. The value comes from catching the user-hostile default that one-perspective reasoning silently locks in. Use it for decisions you'd regret getting wrong; don't use it as a default review pattern. A good heuristic: if reversing the decision later would require a data migration or a user-trust apology, this skill earns its cost. If reversing it would just mean changing a button label, it's overkill.
Provides behavioral guidelines to reduce common LLM coding mistakes, focusing on simplicity, surgical changes, assumption surfacing, and verifiable success criteria.
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.
Creates, edits, and optimizes skills for Claude Code, including drafting, evaluating with test prompts, iterating on performance, and improving skill descriptions for better triggering accuracy.
npx claudepluginhub flagrare/agent-skills --plugin flagrare