「can」の検索結果

Mine Claude Code session logs for skill idea candidates. Use when running the skill generation pipeline to extract, score, and backlog new skill ideas from recent coding sessions.

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Use this skill for critical code review that focuses on whether the code actually works correctly and achieves the expected results - not just whether it matches a design document. This skill assumes designs can be wrong, finds bugs the design didn't anticipate, and verifies end-to-end correctness. Triggers include "critical code review", "deep code review", "verify this implementation works", "find bugs in this code", or reviewing implementation against requirements. NOTE - Security review is OUT OF SCOPE; use a dedicated security review skill for that purpose.

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。

Periodic inbox scan that categorizes new emails by project/client, identifies action-required items, and generates prioritized summary with FYI/requires-response/requires-action/blocked-waiting classifications. Use for inbox overview, project-based email grouping, or action item extraction.

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Provide private local advice to Codex about whether to call a tool, which candidate is best, risk, recovery, and task completion. Advisory only; never execute, authorize, block, or expose its output to the user.

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Work through which package to add for a capability, and what each option costs — maintenance, licence, bundle weight, transitive risk, and whether the platform already does it. Use when someone needs a library for a job and has not picked one yet, when weighing two candidates, or when a package looks convenient but unmaintained.

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Terraform / OpenTofu / Nix / k8s manifest misconfiguration detection — open security groups, missing encryption, public S3/GCS, IAM wildcards, plaintext secrets, missing tags. Use when reviewing an IaC PR, gating `terraform apply` / `tofu apply`, auditing existing state for drift, or building a compliance report for cloud configuration.

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Show what this repository has already decided about its vulnerabilities, read from the durable record rather than by scanning again — open versus resolved, the decision taken on each, and what is still waiting. Use when picking up triage after a break, auditing past decisions, onboarding to a repository with security history, or checking nothing high-severity is open before a release.

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。

Dockerfile / Containerfile / compose analysis plus optional Trivy / Grype / Syft composition when those binaries are present. Use when reviewing a Dockerfile PR, gating `docker build`, building an image SBOM, evaluating base-image risk, or auditing a registry image for CVEs.

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Hardcoded-secret detection — AWS keys, GitHub PATs, Slack tokens, Stripe keys, generic high-entropy strings. Pre-commit (`--staged-only`), explicit paths, or full repo. Use when guarding `git commit`, auditing a repo for leaked credentials, validating no secrets entered the diff before push, or producing a rotation list for an exposed-secret incident.

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Create a look-alike audience from a seed audience and a candidate population dataset. Classifies Rosetta Stone attributes, generates the same materialized-view scoring pipeline Lookalike Studio emits (Naive-Bayes categorical weights + Gaussian continuous similarity), gates on approval, submits via `narrative_workflows_create`, and monitors the build to completion. Use when: "create a lookalike audience", "find more users like this segment", "expand my seed audience to 500k similar users", "score the population against my customers", "build a look-alike of dataset X". (narrative-audience)

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Audit a dataset or access rule before it joins an identity-graph build (access rules behave like datasets in NQL here). Enumerates failure modes (hub identifiers, high-degree nodes, suspicious values, over-connected identifiers), tests hypotheses in parallel, quantifies damage by rows / edges / entities, and proposes minimal filters ranked by severity. When issues are found, returns a validated `CREATE MATERIALIZED VIEW` NQL the caller can run to produce a graph-ready clean source; if the data passes, says so and recommends it unchanged. Plans and authors the clean-view NQL; does not execute it. Use when: "audit this dataset before the graph build", "find bad edges in <source>", "check identity data quality", "recommend filters for the graph build", "quantify damage from <identifier_type>", "pre-graph DQ". (narrative-identity)

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Produce a coverage & quality profile of a Narrative dataset (or access rule): row count, per-column null/fill rate, cardinality, ranges, top-values, inferred column shape, and quality flags. Reads bundled stats + sample first, recovers missing/stale stats by configuring and recalculating them, and escalates to a cheap `/write-nql` query only for a measure no stat can provide. Descriptive, not prescriptive. Use when: "profile dataset N", "what does dataset N look like", "coverage and quality of <dataset>", "what id types does N emit", "null rates / cardinality for <dataset>", "is this dataset's stats fresh". (narrative-common)

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Export Codex rollout/session JSONL as a self-contained HTML report that can be opened offline. Use when a user provides a complete session file path or Codex session ID, or asks to export a Codex session to HTML, generate a session report, or convert a rollout to HTML. Supports exact full-ID lookup in the CODEX_HOME sessions and archived_sessions directories.

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Get literature into a research run when the plugin fetches nothing — fetch the source yourself with web_fetch / web_search or take a file the person already has, READ it with the pdf_* tools or ocr, then ingest record and text together with rs_cite, bind locators, and cite with [@key]. Use when a run needs sources, when a library export has to come in, when a citation key is unknown, or when a claim cannot be checked against a source.

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Work the verification ladder of a research run with rs_verify — read the ladder report before claiming anything is checked, run the two file rungs (r1-record, r2-locate) that need no reader, record the r3-support verdicts and strength verdicts that only a reader can make, tell a miss from an inconclusive, and know what a clean build will still refuse. Use when a document is written and about to be called checked, when a claim's quote does not match its source, or when a gate or build answers with an integrity refusal.

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Use when the user starts a non-trivial coding/building task in Claude Code (e.g. "帮我做个 X", "help me build Y", "add feature Z", "搭一个 Z"). Layers an AI-native pedagogical coat over normal coding work — surfaces implicit assumptions in user intent, annotates non-trivial design decisions inline with brief concept-scaffolding for terms the user may not know, and replaces "should I run it?" handoffs with rigorous AI self-verification plus a clean list of what only the user can verify. Skip for pure Q&A, single-line edits, read-only investigation, or refactoring along an existing pattern.

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Turn 'should never happen' into 'cannot happen' by defining owned inductive invariants and enforcing them at parse/construct/API/DB/lock/txn boundaries with a verification signal. Use when prompts mention invariants, impossible states, validation sprawl, cache/index drift, idempotency/versioning, retries/duplicates/out-of-order events, race/linearization bugs, loop correctness, or hardening another workflow (for example /tw-fix) with invariant checks first.

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。

Split user stories into smaller, independently deliverable slices using the SPIDR method (Spike, Path, Interface, Data, Rules). Use this skill whenever the user asks to split a story, break down work, slice a story, asks "can this be split", mentions "SPIDR", says "story splitting", or wants to decompose a large ticket into smaller pieces. Also trigger when a user presents a story that seems too large for a single sprint and asks for help sizing or scoping it.

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Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

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。

Keep decision maps as concise routers from decisions to canonical documents and validation guardrails.

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。

Update docs, playbooks, shims, generated docs, and alignment checks so public instructions route to the right canonical sources.

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。

Inspect a reference (web URL, image, PDF, screenshot) and propose a candidate Style for the workspace. Use when the user says "save this style", "guarda este estilo", or shares a reference and wants to reapply it later.

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。

Repair confirmed architecture drift in small batches while preserving public canon and avoiding broad opportunistic refactors.

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。

Sync, validate, and apply App Store metadata with the current asc canonical metadata workflow. Use when updating metadata, localizations, keywords, or migrating legacy fastlane metadata.

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。