ruflo-ai-team
claudeTenant-isolated AI teams that plan work, coordinate tasks, recall approved context, and return evidence-backed results
6 skills · 6 commands · 4 agents · 1 MCP server
Multi-agent product validation pipeline. Orchestrates 8 specialized subagents to evaluate new product concepts across market demand, competitive landscape, brand fit, commercial viability, and consumer trends — with adversarial challenge and consensus deliberation.
Not in a marketplace yet, so it loads for one session with --plugin-dir.
Plugin npd-validator in autosolutionsai-didac/npd-validator-plugin. Commands read from the repo's manifest — 1 month ago.
npd-methodologyReference documentation for the NPD validation methodology including scoring rubrics, dimension definitions, verdict thresholds, and report templates. Loaded by evaluator subagents and the orchestrator. Do not invoke directly — use /npd-validator:validate-product instead.
validate-productRun a full NPD validation pipeline on a product concept. Orchestrates 8 specialized subagents (Research Coordinator, 5 Independent Evaluators, Devil's Advocate, Consensus Director) to produce an evidence-based GO / CONDITIONAL GO / REVISIT / NO-GO recommendation. Use when someone asks to validate a
npd-brand-fitIndependent brand fit evaluator for NPD validation. Assesses brand alignment, portfolio coherence, audience fit, and cannibalization risk. Adapts for existing brands (extension analysis) or new brands (credibility assessment). Operates independently.
npd-commercial-viabilityIndependent commercial viability evaluator for NPD validation. Pressure-tests economics: pricing power, unit economics, and channel fit. Produces pricing benchmark table. Thinks like a CFO — everything comes back to the numbers. Operates independently.
npd-competitive-intelIndependent competitive intelligence evaluator for NPD validation. Maps competitive terrain, identifies white space, assesses competitor weaknesses and barriers to entry. Produces a competitor positioning map. Operates independently — never sees other evaluators' output.
npd-consensus-directorFinal synthesizer for the NPD validation pipeline. Reads all evaluations and the devil's advocate case, identifies conflicts, issues challenges back to evaluators, and produces the final validation report with verdict. Acts as panel chair — weighs evidence, doesn't generate it.
npd-consumer-trendsIndependent consumer and trend evaluator for NPD validation. Reads social media like a stock ticker and Reddit like a focus group. Assesses social sentiment, trend momentum, and cultural fit. Distinguishes durable shifts from hype cycles. Operates independently.
npd-devils-advocateAdversarial stress-tester for NPD validation pipeline. Reads all 5 evaluator reports and builds the strongest possible case for NOT launching the product. Identifies evaluator overconfidence, unasked questions, and hidden risks. Assigns a Kill Shot rating (1-10) indicating strength of the no-go case
npd-market-demandIndependent market demand evaluator for NPD validation pipeline. Assesses category size and growth, demand signals, and market timing. Conducts deep research and scores 3 dimensions with extensive reasoning. Operates independently — never sees other evaluators' output.
npd-research-coordinatorConducts broad market research to build a shared factual base for NPD product validation. Gathers category data, competitor profiles, market sizing, regulatory landscape, and trend headlines. Produces a neutral, fact-only context brief that all evaluator subagents will use as their starting point. D
Tenant-isolated AI teams that plan work, coordinate tasks, recall approved context, and return evidence-backed results
6 skills · 6 commands · 4 agents · 1 MCP server
Write accurate Pydantic AI code from the start. Up-to-date patterns, decision trees, and common gotchas for agents, tools, structured output, streaming, and multi-agent apps.
Orchestrate multi-agent teams for parallel code review, hypothesis-driven debugging, and coordinated feature development using Claude Code's Agent Teams
6 skills · 7 commands · 4 agents
Claude Code native multi-agent orchestration with intelligent model routing across agents and skills. Zero learning curve. Maximum power.
85 skills · 20 commands · 19 agents · 1 MCP server · hooks
Enterprise / cross-functional Research Operations domain — the managed counterpart to the academic research/ domain. v2.9.0 ships 5 skills: orchestrator (context: fork), clinical-research (protocol synopsis, endpoint selection, sample-size/power for means/proportions/survival, phase-gate feasibility), research-finance (R&D budgeting with F&A split, burn/runway, capitalize-vs-expense routing, portfolio ROI), market-research (TAM/SAM/SOM top-down and bottoms-up, survey sampling with FPC, Kotler segmentation), and product-research (goal-matched study design, method-based saturation, insight synthesis flagging single-source anecdotes). Hard rules: outputs are estimates routed to a named clinical or finance owner, market sizes show method + assumptions, product insights require recurrence across independent participants. 24 stdlib Python tools, 12 reference docs. Distinct from ra-qm-team, finance, research/grants, product-team, and marketing-skill.
6 skills · 7 commands · 2 agents
Market research skills for PMs: user personas, market segmentation, sentiment analysis, competitive analysis, and survey design.
8 skills · 3 commands
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