Clarifies inputs, outputs, success criteria, decisions, and unresolved conditions so downstream agents can execute without guessing. Use when writing or revising LLM-facing prompts, handoffs, planning artifacts, reviews, reports, or generated instructions.
npx skills add https://github.com/shinpr/ai-coding-project-boilerplate --skill llm-friendly-context
The goal is stable downstream execution: the next agent should know what to read, what to do, what counts as success, and when to stop or escalate.
This skill governs the clarity of LLM-facing output — prompts, handoffs, and generated artifacts. The caller supplies the artifact type and any artifact-specific template or section contract; this skill makes that supplied contract executable for the downstream agent.
appropriate, proper, related, existing behavior, optional, as needed, if needed, per convention, unresolved alternatives, TBD, placeholder.Unresolved: <decision needed> — required input: <what or who resolves it> — escalation: <the condition under which the next agent stops rather than guesses>.minimal, a few lines, an explicit line or file estimate — as one budget over the whole completed diff, not per file or per step. When the work cannot fit it, report the overrun and the reason instead of silently exceeding it.Use these rewrites before treating a prompt, handoff, or artifact as complete.
| Ambiguous form | Rewrite as |
|---|---|
| optional used as an unresolved choice | Required, omitted, or required only under a named condition |
| Multiple alternatives that the next agent must choose between | The selected option, or a deterministic decision rule |
| as needed / if needed | The triggering condition and required action |
| per convention | The file, function, test, or documented convention to follow |
| related files | Specific paths, globs, or search hints |
| existing behavior | The observable behavior, source file, test, API response, or UI state to preserve |
| placeholder | Exact temporary value/behavior, allowed dependencies, and verification expectation |
| TBD used as a placeholder for required information | A blocking unresolved item stating the required input and escalation condition (and owner when known) |
| appropriate / proper | A measurable criterion or checklist |
Before sending a prompt or artifact to another agent, verify:
Before writing or finalizing a generated document:
Complete development kit for Microsoft 365 Copilot declarative agents with three comprehensive workflows (basic, advanced, validation), TypeSpec support, and Microsoft 365 Agents Toolkit integration
Format and structurally validate local treatment-plan documentation after clinical decisions have already been supplied and verified by authorized licensed professionals. Use for source traceability, clinician-authored intervention records, goals and checkpoints, shared-decision records, reconciliation handoffs, and release gates—not for clinical decision-making.
> provider/change budget/修改卖家/修改预算/draft/草稿/我的任务/my tasks/what am I working on/关闭/取消任务/决策列表/decision list/指定服务商/browse (sender.role = COUNTERPARTY, not you); (3) literal "Read the okx-ai skill" (or legacy "Read the okx-agent-task skill") in the envelope.
Automate payer review of prior authorization (PA) requests. This skill should be used when users say "Review this PA request", "Process prior authorization for [procedure]", "Assess medical necessity", "Generate PA decision", or when processing clinical documentation for coverage policy validation and authorization decisions.
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.
Orchestrates design workflows by routing work through brainstorming, multi-agent review, and execution readiness in the correct order.
Structured persuasion for tech leads, PMs, and founders—not activity logs. Five scenarios (kickoff, status update, wrap-up, investor pitch, solution selling) on one 5-part framework (Hook→Context→Proposal→Evidence→Ask). AI prompts for missing materials and audience context; pre-submit checklist. Claude Code plugin; Cursor, Codex, and chat via prompts.
Take shinpr/ai-coding-project-boilerplate-llm-friendly-context from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.