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Run the diverge → converge → premortem pipeline to produce a risk-annotated PRD
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Basé sur la classification professionnelle SOC
| name | research-project |
| description | Run the diverge → converge → premortem pipeline to produce a risk-annotated PRD |
| argument-hint | [N] "topic or feature description" |
| allowed-tools | ["Agent","Read","Write","Glob","Grep"] |
Run the complete PRD creation pipeline as a single invocation. This chains three skills in sequence, passing outputs between them.
[N] "research question or topic"
Invoke the /diverge skill with the provided arguments. This spawns N independent research agents and produces a PRD.
Wait for the PRD file to be created (format: prd_{slugified_topic}.md).
Invoke the /converge skill, passing the PRD file path from Step 1.
This runs a structured debate to refine the PRD and resolve tensions.
Invoke the /premortem skill, passing the refined PRD from Step 2.
This runs prospective failure analysis and annotates the PRD with risks and mitigations.
Present the final risk-annotated PRD path and a brief summary:
/prd-build <prd-path>)Index of this project's coding-practice rules — architecture, coding style, testing, security, git workflow, task management, context layering, anti-slop, performance, and language-specific rules. The thin always-on essentials live in .claude/rules/common/house-rules.md; the full per-topic detail ships under .claude/rules/reference/ and .claude/rules/<lang>/ and is read on demand. Invoke this skill when you need the project's standards for a task, then open the specific rule file it points to.
Thin methodology for end-to-end tests of critical user journeys — define journeys by risk, use semantic locators and condition-based waits, quarantine flaky tests with a tracked reason, and capture artifacts on failure. Use when adding or stabilizing E2E coverage; the e2e-runner agent applies it in depth.
Design rigorous evaluations and benchmarks for AI agents, developer tools, retrieval systems, and repository-scale automation. Covers task selection, contamination control, metric choice tied to engineering decisions, and statistical validity. Use when asked to design an eval/benchmark, critique an existing benchmark, choose metrics for an agent or RAG system, or decide whether a measured improvement is real. NOT for running an existing performance-benchmark suite or a per-feature acceptance checklist, or one-off model spot-checks.
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree one at a time. Use for ambiguous or complex collaborative specs before any code is written.
Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases. Use when parsing quizzes, forms, invoices, or documents with repeating structure and cost matters.
Review a repository for long-term architectural leverage rather than code quality — system structure, module boundaries, dependency graph, coupling, and drift. Produces a ranked set of highest-ROI improvements with evidence, effort, and risk. Use when asked to review the architecture, assess a codebase's structure/design, find where complexity is concentrated, or decide what to refactor next. NOT for style, naming, formatting, or line-level bugs (use a code-review skill for those).