Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add shinpr/claude-code-workflows --skill llm-friendly-contextgit clone --depth 1 https://github.com/shinpr/claude-code-workflowsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/shinpr/claude-code-workflows/llm-friendly-context)<a href="https://agentmods.dev/skills/shinpr/claude-code-workflows/llm-friendly-context"><img src="https://agentmods.dev/badge/skills/shinpr/claude-code-workflows/llm-friendly-context/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/shinpr/claude-code-workflows/llm-friendly-context"><img src="https://agentmods.dev/badge/skills/shinpr/claude-code-workflows/llm-friendly-context.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00052 | $0.01077 |
| Opus 5.5 | $0.00021 | $0.00431 |
| Sonnet 5.5 | $0.00010 | $0.00215 |
| Haiku 4.5 | $0.00005 | $0.00108 |
Grade A, and why
llm-friendly-context scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM-Friendly Context
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
-
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."
-
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.
-
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.
-
Provide the smallest sufficient context
- Sufficient means sufficient for the assigned action, not complete background: every item you include is consumed by that action or by the result it must produce.
- Include the purpose, source artifacts, hard constraints, accepted decisions, and unresolved conditions that action consumes.
- 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.
-
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
- 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +2 lines cd77586a3bb3
- 1mo ago First seen · 83 lines · 52 tokens per session scan A a9ce9d99c94c
llm-friendly-context is a skill published in the GitHub repository shinpr/claude-code-workflows (701 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 1,077 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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