LLM-Friendly Context

SkillAI & models

Clarifies inputs, outputs, success criteria, decisions, and unresolved conditions so downstream consumers can execute without guessing. Use when writing or revising LLM-facing prompts, handoffs, planning artifacts, reviews, reports, or generated instructions.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the LLM-Friendly Context skill

What this skill tells your AI

The instructions your AI receives, as published by shinpr/claude-code-workflows in skills/llm-friendly-context/SKILL.md and read by ahel’s review.

The goal is stable downstream execution: the next consumer should know what to read, what to do, what counts as success, and which unresolved decisions can change the result.

Core Rules

  1. Use positive, executable instructions

    • State what the next consumer should do.
    • Convert quality policies into positive criteria.
    • Keep a prohibition only when it protects an irreversible boundary or shipped contract. Name the protected condition and the allowed action.
    • Example: "Preserve existing public API behavior across the documented compatibility cases."
  2. 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 consumer: appropriate, proper, related, existing behavior, optional, as needed, if needed, per convention, unresolved alternatives, TBD, placeholder.
  3. Specify output shape

    • Use the sections, fields, table columns, JSON keys, or checklist items the consumer uses.
    • For handoffs, include only produced artifact paths and status fields that control the next transition.
  4. 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 change an in-scope decision, action, or verification result.
  5. 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.
  6. Permit uncertainty explicitly

    • Resolve missing operational detail from referenced artifacts and repository evidence before treating it as unresolved.
    • Record remaining uncertainty with its effect, required input, and decision owner. Make reversible repository-local choices when governing evidence resolves them.
  7. 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.
    • Apply minimal, a few lines, and explicit line estimates to the completed diff as one total budget.

Rewrite Patterns

Use these rewrites before treating a prompt, handoff, or artifact as complete.

Ambiguous formRewrite as
optional used as an unresolved choiceRequired, omitted, or required only under a named condition
Multiple alternatives that the next consumer must choose betweenThe selected option, or a deterministic decision rule
as needed / if neededThe triggering condition and required action
per conventionThe file, function, test, or documented convention to follow
related filesSpecific paths, globs, or search hints
existing behaviorThe observable behavior, source file, test, API response, or UI state to preserve
placeholderExact temporary value/behavior, allowed dependencies, and verification expectation
TBD used as a placeholder for required informationA blocking unresolved item with owner, required input, and decision effect
appropriate / properA measurable criterion or checklist

Handoff Checklist

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

  • The target action is explicit.
  • Required input paths, source artifacts, and decision-relevant facts are named.
  • Accepted decisions and constraints use one canonical wording.
  • Output format or expected status fields are specified.
  • Success criteria are observable.
  • Ambiguous expressions have been rewritten or marked as unresolved.
  • Each instruction states the allowed action; each retained prohibition names the protected condition and allowed alternative.
  • The next consumer can complete its scope from the supplied purpose, sources, criteria, and evidence, or return the exact unresolved decision and owner.

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.
  • Each instruction states the allowed action; each retained prohibition names the protected condition and allowed alternative.
  • If an artifact is derived from another artifact, copied decisions stay consistent in wording and meaning.
  • If downstream work is blocked by missing information, the artifact records the missing input, decision owner, and effect.

Signals

GitHub stars
681
Forks
102
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
llm-friendly-context-shinpr
Source
github.com/shinpr/claude-code-workflows