「existing」の検索結果

Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.

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Build a new API connector or provider by matching the target repo's existing integration pattern exactly. Use when adding one more integration without inventing a second architecture.

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Master Anthropic's prompt engineering techniques to generate new prompts or improve existing ones using best practices for Claude AI models.

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Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to ...

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/cs:aims-audit <scope> — ISO/IEC 42001 AIMS internal-audit 6-question forcing interrogation. Use before certification stage 1, before annual internal audit cycles, or when onboarding a new AI system into an existing AIMS.

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Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

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Diff a live page's accessibility violations against a baseline — by default compares uncommitted changes (stash-based), or pass --branch [<name>] to diff against a branch. Reports only new violations introduced, violations fixed, and pre-existing count. Use `scan` for a full audit with no diffing.

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Apply bottom-up and top-down role mining techniques to discover optimal RBAC roles from existing user-permission assignments, reducing role explosion and enforcing least privilege.

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Analyzes structured and unstructured threat intelligence feeds to extract actionable indicators, adversary tactics, and campaign context. Use when ingesting commercial or open-source CTI feeds, evaluating feed quality, normalizing data into STIX 2.1 format, or enriching existing IOCs with campaign attribution. Activates for requests involving ThreatConnect, Recorded Future, Mandiant Advantage, MISP, AlienVault OTX, or automated feed aggregation pipelines.

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Convert existing tasks into actionable, dependency-ordered GitHub issues for the feature based on available design artifacts.

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Maintain a support docs pack — dedup, length budgets, and no splintering — and land changes as a reviewed pull request. Use when a support thread, or a hand-off from Support assist, surfaces a new resolution, a correction to an existing one, or a deployment quirk worth recording. Not for ordinary code changes.

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Run the Tamanu release-branch CUTOFF checks when preparing to cut a new release/X.YY branch: confirm the previous release's hotfixes are merged to main, reconcile the commits since the previous release against the "Merged to main" card list from Linear, and scan for data/reporting/schema changes. Use when the user is cutting a Tamanu release and wants to verify the codebase is ready, wants to check main against the previous release branch, or wants to reconcile commits with a Linear "Merged to main" column (they should paste the card list). Handles Tamanu's train-release model (release/X.YY branches vs vX.YY.Z patch tags) and the cherry-pick model where the same logical fix has different SHAs on different branches. Purely git-based — no live database or Canopy access needed. Not for verifying an existing release branch's hotfix integrity (see pre-release-checks) or for deciding which tests to run (see scope-tamanu-release-tests).

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Integrate Honcho memory and social cognition into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, implementing the dialectic chat endpoint for AI agents, or wiring Honcho into bot frameworks (nanobot, openclaw, picoclaw, etc).

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Create, edit, improve, or audit AgentSkills. Use when creating a new skill from scratch or when asked to improve, review, audit, tidy up, or clean up an existing skill or SKILL.md file. Also use when editing or restructuring a skill directory (moving files to references/ or scripts/, removing stale content, validating against the AgentSkills spec). Triggers on phrases like "create a skill", "author a skill", "tidy up a skill", "improve this skill", "review the skill", "clean up the skill", "audit the skill".

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Audit a codebase as a read-only senior advisor, prioritize evidence-backed improvements, and write self-contained implementation plans. Use for codebase audits, improvement roadmaps, focused planning, plan review, plan reconciliation, or executing an existing improve plan through an isolated Codex worktree. The advisor never edits source code; execution uses codex-improve and returns to the main agent for review.

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Write new English text, or rewrite existing text, in ASD-STE100 Simplified Technical English. Use for STE, simple technical English, ASD-STE100, or 80% STE, for writing or rewriting procedures, manuals, SOPs, runbooks, and release notes.

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Create, verify, transfer, and resume compact project handoff packets for long-running Codex work. Use after a second observed automatic context compaction, immediately after a third compaction, when attention or constraints degrade, a major phase ends, the user asks to move work into a fresh task, work must pause, or an existing handoff needs to be imported. Do not interrupt after the first normal compaction. Always ask before creating a new task, transfer the global roadmap only after explicit consent, preserve the old task title when available, then require the fresh task to ask what work matters most. Preserve objectives, constraints, decisions, evidence, approval boundaries, workspace state, validation, and risks without treating an old summary as current truth.

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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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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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Create concise checklist cheatsheets from official tool docs. Use for docs URLs, setup guides, or updating existing cheatsheets. Don't use for long tutorials or API references.

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Refine an existing skill in place with minimal diffs. Trigger when asked to improve a skill's trigger description/frontmatter, workflow text, metadata, scripts/references/assets; also for requests to iterate, refactor, rename, or fix a skill using usage/session-mining evidence (for example from /tw-seq).

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Convert PRDs to prd.json format for the Ralph autonomous agent system. Use when you have an existing PRD and need to convert it to Ralph's JSON format. Triggers on: convert this prd, turn this into ralph format, create prd.json from this, ralph json.

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Create a language-agnostic ghost package (spec + portable tests) from an existing repo by extracting SPEC.md, exhaustive tests.yaml (operations and/or scenarios), INSTALL.md, README.md, VERIFY.md, and upstream LICENSE files with provenance and regeneration instructions. Use when prompts say "/tw-ghost", "ghostify this repo", "spec-ify/spec-package this library", "ghost library", or ask to extract portable spec/tests for libraries or tool-using agent loops (scenario testing); do not use for implementation work or editing skills.

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Analyze a local codebase and infer the architecture it is actually using, including repo kind, best-fit dominant architecture, directly evidenced coexisting patterns, major subsystem exceptions, confidence, docs-vs-code drift, and repo-fit hints for downstream agents. Use when prompts ask what architecture a repo uses, whether it is really hexagonal or just layered, what subtle patterns shape a repo or slice, how a target slice fits a hybrid monorepo, whether documented architecture matches implementation, or when `/tw-tk` needs a repo-dialect preflight before making a minimal change.

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。