mcpbeat

LLM Friendly Context

shinpr/ai-coding-project-boilerplate-llm-friendly-context

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.

1k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
225
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/shinpr/ai-coding-project-boilerplate --skill llm-friendly-context

The instruction itself

5 sections, as written by the author

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.

Core Rules

  • Use positive, executable instructions
  • State what the next agent should do.
  • Convert quality policies into positive criteria.
  • Example: "Preserve existing public API behavior across the documented compatibility cases."
  • Keep a prohibition only when it protects an irreversible boundary or a shipped contract; then name the protected condition and the allowed action alongside it.
  • Make vague instructions concrete
  • Replace subjective terms with observable conditions, paths, commands, schemas, examples, or decision rules.
  • Terms that often need clarification when they leave a decision to the next agent: appropriate, proper, related, existing behavior, optional, as needed, if needed, per convention, unresolved alternatives, TBD, placeholder.
  • Specify output shape
  • Define required sections, fields, table columns, JSON keys, or checklist items.
  • For handoffs, include paths to produced artifacts and the exact status fields the caller must inspect.
  • Provide necessary context
  • Include the purpose, source artifacts, hard constraints, accepted decisions, and unresolved conditions.
  • Prefer concrete file paths and section hints over broad module names.
  • Follow references while they can still change an in-scope decision; stop when the next link only confirms what is already decided.
  • Decompose complex work into verifiable steps
  • Split work with 3+ objectives or sequential dependencies into ordered steps.
  • Each step needs a checkpoint: what evidence proves it is complete.
  • Permit uncertainty explicitly
  • If the source material is missing, contradictory, or not verifiable, state the uncertainty and the required escalation.
  • Record unknown business, product, security, or compatibility decisions as blocking unresolved items, each stating the required input to resolve it and the escalation condition.
  • Write every blocking unresolved item in one consistent shape, regardless of artifact: Unresolved: <decision needed> — required input: <what or who resolves it> — escalation: <the condition under which the next agent stops rather than guesses>.
  • Keep constraints proportionate
  • Add only constraints that reduce ambiguity or preserve a real requirement.
  • Keep simple downstream tasks lightweight when the target action, context, and success criteria are already clear.
  • Treat a stated size expectation — 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.

Rewrite Patterns

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 |

Handoff Checklist

Before sending a prompt or artifact to another agent, verify:

  • [ ] The target action is explicit.
  • [ ] Required input paths and source artifacts are named.
  • [ ] Accepted decisions and constraints are stated once, without alternate wording.
  • [ ] Output format or expected status fields are specified.
  • [ ] Success criteria are observable.
  • [ ] Ambiguous expressions have been rewritten or marked as unresolved.
  • [ ] Any stated size expectation is expressed as one budget over the completed diff, with the overrun-reporting condition named.
  • [ ] The next agent can complete its scope with explicit choices, decision rules, or blocking unresolved items.

Generated Artifact Checklist

Before writing or finalizing a generated document:

  • [ ] Each requirement, claim, task, test skeleton, or review finding has enough source context to trace why it exists.
  • [ ] Every executable instruction names the target, action, and expected result.
  • [ ] Verification steps say what to run or observe and what result proves success.
  • [ ] If an artifact is derived from another artifact, copied decisions stay consistent in wording and meaning.
  • [ ] Any stated size expectation is expressed as one budget over the completed artifact, with the overrun-reporting condition named.
  • [ ] If downstream work is blocked by missing information, the artifact records the missing input and escalation condition.

How to use it

Copy the folder

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.

Check the name does not clash

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.