Llm friendly context
Skill shinpr/claude-code-workflows/skills/llm-friendly-context
Production-ready development workflows for Claude Code, powered by specialized AI agents.
npx -y skills add shinpr/claude-code-workflows --skill llm-friendly-contextAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its author says it does
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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.
SKILL.md
4.4 KB, 842 tokens by cl100k_base, as published. Nobody here has run it
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.
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."
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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.
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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.
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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.
-
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.
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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 with the input needed to resolve them.
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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.
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 with owner, required input, or escalation condition |
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.
- 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.
- If downstream work is blocked by missing information, the artifact records the missing input and escalation condition.
Gives 0 of the 12 instructions most context ai engineering skills give in 842 tokens
Counted across 1,193 of the 1,976 authors here whose files we hold, read 2026-08-06
- dispatch a fresh implementer subagent per taskin 48 of 1193, across 19 files
- dispatch final reviewer after all tasksin 37 of 1193, across 11 files
- provide full task text to the subagentin 31 of 1193, across 10 files
- review spec compliance before code qualityin 27 of 1193, across 10 files
- make the hook script executablein 26 of 1193, across 8 files
- re-snapshot after navigation or DOM changesin 25 of 1193, across 17 files
- answer subagent questions before proceedingin 22 of 1193, across 7 files
- mark task complete in TodoWrite after approvalin 22 of 1193, across 6 files
- merge hook into existing settingsin 21 of 1193, across 3 files
- read files before editing themin 21 of 1193, across 9 files
- ask if installation is global or projectin 20 of 1193, across 2 files
- copy the hook script to target locationin 20 of 1193, across 2 files
Said here and by no other author read
- State what the next agent should do
- Prefer concrete file paths over broad module names
- Split complex work into ordered verifiable steps
- Add only constraints that reduce ambiguity
- Rewrite ambiguous expressions before finishing
- Name required inputs and source artifacts
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.