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Found 340 Skills
Phase 1 of the Issue Workflow - Translate the user's problem into a reproducible, traceable {slug}-report.md through conversation. The AI only asks "what you saw, how to reproduce it, what should happen" here, and does not guess the root cause for the user (that's Phase 2's responsibility). This phase is also the only official decision point for determining whether to take the fast track or the standard path: first read the relevant code based on the user's description, and if the root cause can be identified at a glance and the changes required are minor, directly inform the user to take the fast track. Trigger scenarios: The user says "file an issue", "log this bug", "I found a problem". This is the starting point of the issue workflow with no pre-requisites.
Watch for the 11 known AI-coding-agent failure modes (fabrication, scope_creep, security_vulnerability, etc.) — consult this skill before edits, dependency adds, completion claims, or anything that could trip a known supervision concern. Quote the snake_case failure-mode ids verbatim when flagging risks.
Use when you find additional work needed during task execution that's not in the original task description, to report it to ZŌE for review
Guides senior corporate transaction leadership—deal thesis, valuation and offer strategy, negotiation priorities, structure (cash/stock/earnout/RWI/locked box), IC and board recommendations, adviser and banker management, go/no-go and walk-away, and oversight of execution through close. Use when leading an M&A, divestiture, financing, or JV as deal principal, preparing investment committee or board materials, setting negotiation mandates, or adjudicating price/structure—not for closing matrices and diligence logistics (transaction-manager), contract drafting (corporate-counsel, commercial-counsel), general strategy consulting (business-consultant), or sales quote-to-cash (deal-operations-administrator). Human executives and counsel approve binding terms.
Creates an Architecture Decision Record (ADR) documenting a significant technical decision, its context, alternatives considered, and consequences. Every major technical choice should have an ADR.
Generates professional board meeting presentation content (board-deck.md) with executive summary, financials, product updates, GTM metrics, team/hiring, strategic decisions, and appendix. Supports early-stage, growth-stage, and pre-IPO formats. Use when preparing board meeting materials, quarterly board updates, or investor presentations.
Clarify or discuss a proposed task, plan, design update, or ADR by resolving the highest-value unresolved decisions, decision criteria, trade-offs, and option boundaries until the inputs are ready for task creation, task planning, task/design updates, ADR writing, or safe implementation continuation. Use this as the default path when the user asks to clarify, discuss criteria, compare options, stress-test a design, or otherwise resolve material unresolved questions before proceeding. When clarification ends, resume the invoking workflow. It may also be used for general grilling when explicitly selected or when no other default grilling skill is available.
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.
Build comprehensive AI-native brand asset systems that maintain consistency across all AI-generated content. Train AI tools on brand guidelines, create reusable prompt libraries, and manage visual/voice assets at scale. Use when ", " mentioned.
Use when you need to turn selected modules (P0 priority first) into single-page module SSOT at the path `.aisdlc/project/components/{module}.md`, and build authoritative entries for API/Data contracts, invariant summaries, evidence entries and structured Evidence Gaps in the same page to meet the DoD gate requirements of Discover.
Repository structure methodology for maximum AI agent effectiveness. Three pillars — context engineering (repo as knowledge product), architectural constraints (deterministic enforcement), garbage collection (active entropy fighting). Use when setting up repos for AI development, diagnosing repeated agent failures, writing AGENTS.md, or designing CI gates and structural tests.
Create stakeholder alignment artifacts including responsibility matrices, decision frameworks, and communication plans.