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Size a project contingency reserve and rank the tasks that drive total uncertainty, using the PRA package. Use after (or alongside) a Monte Carlo simulation when a user asks "how much reserve do I need?" or "which tasks should I focus mitigation on?"
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Compute and update the probability of a risk event from root causes, and turn risk probabilities into project cost distributions, using the PRA package. Use when a user has a risk register with root-cause probabilities and wants a prior risk probability, a Bayesian update after observing causes, or a cost-impact distribution.
Quantify structural coupling between project tasks through shared resources and risks using the PRA package's Design Structure Matrices. Use when a user provides a resource–task matrix (and optionally a risk–resource matrix) and wants to see which task pairs are coupled and therefore tend to fail together.
Measure project cost/schedule performance and forecast final outcomes with Earned Value Management using the PRA package. Use when a user provides a budget (BAC), a planned schedule curve, and actual costs/progress and wants SPI, CPI, variances, or an Estimate at Completion (EAC).
Fit sigmoidal (S-curve) learning curves and forecast future values with the PRA package. Use when a user has historical time/completion (or unit/cost) data showing slow start, rapid acceleration, and plateau, and wants a fitted model plus predictions with confidence intervals.
Run Monte Carlo (or fast Second Moment) schedule/cost risk simulations with the PRA package. Use when a user provides task-level uncertainty distributions and needs the range of possible project outcomes — mean, standard deviation, and P5/P50/P95 percentiles — optionally accounting for task correlation.
Route a project risk analysis request to the right PRA tool. Use this as the entry point whenever a user asks about project cost/schedule uncertainty, earned value, risk probabilities, learning curves, or task dependencies and you are unsure which PRA capability applies. Points to the method-specific skills and the underlying MCP tools served by pra_mcp_server().
| name | pra-sensitivity-contingency |
| description | Size a project contingency reserve and rank the tasks that drive total uncertainty, using the PRA package. Use after (or alongside) a Monte Carlo simulation when a user asks "how much reserve do I need?" or "which tasks should I focus mitigation on?" |
sensitivity_tool(task_dists_json, cor_mat_json) measures each task's
contribution to total project variance. It takes the same input format as
mcs_tool (a JSON array of task distributions plus an optional correlation
matrix). Returns one value per task; higher values are the primary drivers of
overall uncertainty. Results are typically shown as a tornado chart. Direct
mitigation resources at the highest-sensitivity tasks first.
contingency_tool(phigh, pbase) returns the buffer between a high-confidence
percentile and the base estimate:
contingency = P_high - P_base
phigh (default 0.95): high-confidence percentile — there is a phigh
probability the actual value will not exceed it.pbase (default 0.50): the median ("most likely") total.Important: call mcs_tool first — contingency_tool operates on the most
recent Monte Carlo results.
mcs_tool(num_sims, task_dists_json, cor_mat_json)contingency_tool(phigh = 0.95, pbase = 0.50)sensitivity_tool(task_dists_json, cor_mat_json)