「psm」の検索結果

Performs root-cause analysis of a bug — from a GitHub issue, an issue URL, or a free-text description — before any reproduction or fix is written. TRIGGER when: triaging or diagnosing a bug, investigating why something misbehaves, an issue number/URL handed over for analysis, needing the root cause before touching code. DO NOT TRIGGER when: a failing reproduction test already exists and you are ready to fix → opsmill-dev-fixing-bugs; writing that reproduction test → opsmill-dev-test-driving-bugs; capturing a new bug as a ticket → opsmill-dev-creating-issues.

日本語の概要は準備中です。原文の説明を表示しています。

Implements and validates the fix for a bug once a failing reproduction test exists. TRIGGER when: a bug has a failing reproduction test and you are ready to make it pass, implementing the root-cause fix, the final step of the bug-fixing pipeline. DO NOT TRIGGER when: no reproduction test exists yet → opsmill-dev-test-driving-bugs; still diagnosing, or asked to fix a bug with no analysis or reproduction test yet → opsmill-dev-analyzing-bugs.

日本語の概要は準備中です。原文の説明を表示しています。

Writes a single failing test that reproduces a bug after its root-cause analysis is complete, before any fix is written. TRIGGER when: a bug has a completed root-cause analysis and you need the failing reproduction test, writing a test that proves a bug exists, the second step of the bug-fixing pipeline. DO NOT TRIGGER when: still triaging or diagnosing the bug → opsmill-dev-analyzing-bugs; implementing the fix once the test exists → opsmill-dev-fixing-bugs; general feature test-first work → superpowers test-driven-development.

日本語の概要は準備中です。原文の説明を表示しています。

Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid), (5) baseline modeling (reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe), (6) robustness battery (bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster), (7) further analysis (subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group), (8) publication-ready tables & figures (esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `teffects ipw` / `teffects ipwra` / `teffects aipw` / `eltmle`, Mendelian randomization via `mrrobust` (IVW / Egger / weighted median) and `mregger` / `mrpresso`, KM / Cox / AFT / RMST survival via `sts` / `stcox` / `streg` / `strmst2`, E-value sensitivity via `evalue` (Linden-Mathur), principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `ddml` / `pdslasso`, S/T/X/R/DR meta-learners via `crforest` and `ddml interactive`, causal forest via `crforest` / `cforest`, BART/BCF via `bart` / `bartCause`-style externals, CATE distribution + policy tree via `crforest`, off-policy evaluation, conformal causal externals, fairness audit, DAG learning via `pcalg` / external Python callouts). Use when the user asks for a complete Stata empirical analysis, wants a reproducible .do-file pipeline, needs a Stata counterpart to the Python StatsPAI / Full-empirical-analysis-skill, or names a specific Stata step in isolation ("run reghdfe with two-way clustering", "csdid event study", "winsor2 at 1%", "esttab to LaTeX", "coefplot with CI", "ivreg2 weak-IV test", "synth_runner placebos", "teffects psmatch balance check"). Mode A triggers on "target trial emulation Stata", "teffects ipw aipw", "eltmle", "mrrobust", "mregger weighted median", "stcox AFT survival", "strmst2", "evalue Stata", "STROBE Stata", "公共健康 Stata", "流行病学 Stata". Mode B triggers on "ddml Stata", "pdslasso", "crforest causal forest Stata", "policy tree Stata", "因果机器学习 Stata".

日本語の概要は準備中です。原文の説明を表示しています。

Appsmith integration. Manage data, records, and automate workflows. Use when the user wants to interact with Appsmith data.

日本語の概要は準備中です。原文の説明を表示しています。

Appsmith is an open-source low-code platform for building internal tools, admin panels, and dashboards. Use when a user asks to create CRUD interfaces, connect to databases or APIs with drag-and-drop widgets, write JSObjects for business logic, or self-host Appsmith with Docker or Kubernetes.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Appsmith — appsmith low-code platform management covering application inventory, workspace organization, datasource health, page and widget analysis, and user access auditing. Use when reviewing internal tool configurations, investigating datasource connectivity, monitoring application deployments, or auditing workspace permissions.

日本語の概要は準備中です。原文の説明を表示しています。

The PSMatch package helps proteomics practitioners to load, handle and manage Peptide Spectrum Matches. It provides functions to model peptide-protein relations as adjacency matrices and connected components, visualise these as graphs and m

日本語の概要は準備中です。原文の説明を表示しています。

Run a session retrospective that surfaces context-management gaps and routes them to approved follow-up actions.

日本語の概要は準備中です。原文の説明を表示しています。

Run the full speckit workflow end-to-end — specify, plan, critique, tasks, implement, review — making all decisions autonomously. Stops before extract; run that manually.

日本語の概要は準備中です。原文の説明を表示しています。

Run the speckit preparation phases end-to-end — specify, plan, critique, tasks — making all decisions autonomously. Stops before implementation.

日本語の概要は準備中です。原文の説明を表示しています。

Run the speckit implementation phase chunk-by-chunk in clean-context subagents, then review, then produce a final report. Picks up from an existing tasks.md.

日本語の概要は準備中です。原文の説明を表示しています。

Mines a pull request's review threads for lessons that generalize beyond that PR, reconstructs each against the actual code before deciding, checks whether it is already codified, and routes the genuinely-new, durable ones into Infrahub's internal documentation — `dev/knowledge/`, `dev/guides/`, `dev/guidelines/`, the `AGENTS.md` files, and `.agents/rules/` — proposing edits first and applying only with the user's approval. TRIGGER when: the user wants to turn PR review feedback into durable conventions, guidelines, or rules; capture recurring reviewer comments as internal documentation; or check whether review lessons are reflected in the knowledge/guides/guidelines/AGENTS.md/rules layer. DO NOT TRIGGER when: sweeping a feature's changes for documentation coverage across all layers → use `audit-docs`; extracting knowledge from completed spec directories → `speckit-opsmill-extract`; only replying to or resolving review threads → normal git/gh flow.

日本語の概要は準備中です。原文の説明を表示しています。

Produce a flow-level summary of the current Claude Code session — executive summary, chronological timeline, and outcomes — written into the active feature directory next to spec.md / plan.md.

日本語の概要は準備中です。原文の説明を表示しています。

Extract knowledge, guidelines, and ADRs from one or more completed spec directories into the project documentation system.

日本語の概要は準備中です。原文の説明を表示しています。

Appsmith is an open-source platform for building internal tools, dashboards, and admin panels on top of databases and APIs. It is well suited to operational workflows where agents or developers need a fast way to stand up interfaces for support, ops, analytics, or back-office tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Produce a flow-level summary of the current Claude Code session — executive summary, chronological timeline, and outcomes — written into the active feature directory next to spec.md / plan.md.

日本語の概要は準備中です。原文の説明を表示しています。

Extract knowledge, guidelines, and ADRs from one or more completed spec directories into the project documentation system.

日本語の概要は準備中です。原文の説明を表示しています。

Run the full speckit workflow end-to-end — specify, plan, critique, tasks, implement, review — making all decisions autonomously. Stops before extract; run that manually.

日本語の概要は準備中です。原文の説明を表示しています。

Run the speckit implementation phase chunk-by-chunk in clean-context subagents, then review, then produce a final report. Picks up from an existing tasks.md.

日本語の概要は準備中です。原文の説明を表示しています。

Run a session retrospective that surfaces context-management gaps and routes them to approved follow-up actions.

日本語の概要は準備中です。原文の説明を表示しています。

Run the speckit preparation phases end-to-end — specify, plan, critique, tasks — making all decisions autonomously. Stops before implementation.

日本語の概要は準備中です。原文の説明を表示しています。

Run the speckit implementation phase chunk-by-chunk in clean-context subagents, then review, then produce a final report. Picks up from an existing tasks.md.

日本語の概要は準備中です。原文の説明を表示しています。

Produce a flow-level summary of the current Claude Code session — executive summary, chronological timeline, and outcomes — written into the active feature directory next to spec.md / plan.md.

日本語の概要は準備中です。原文の説明を表示しています。