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/codex-workflows --skill llm-friendly-contextgit clone --depth 1 https://github.com/shinpr/codex-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/codex-workflows/llm-friendly-context)<a href="https://agentmods.dev/skills/shinpr/codex-workflows/llm-friendly-context"><img src="https://agentmods.dev/badge/skills/shinpr/codex-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/codex-workflows/llm-friendly-context"><img src="https://agentmods.dev/badge/skills/shinpr/codex-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 | $0.01119 |
| Opus 5.5 | $0.00021 | $0.00448 |
| Sonnet 5.5 | $0.00010 | $0.00224 |
| Haiku 4.5 | $0.00005 | $0.00112 |
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 5d 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM-Friendly Context
Purpose
Use this skill when writing or revising any content another agent will execute or judge: prompts, handoffs, planning artifacts, review findings, completion reports, generated instructions, test skeleton comments, work plans, and task files.
The goal is stable downstream execution. The next agent should know the target action, required inputs, accepted decisions, observable success criteria, and the condition that requires escalation.
Core Rules
-
Use positive, executable instructions
- State the action the next agent should perform.
- Convert quality policies into observable acceptance 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.
-
Resolve outcome-relevant ambiguity
- Clarify an instruction when plausible interpretations would change correctness, requested scope, downstream usability, or verification. Use the least-restrictive sufficient condition, source, or example.
- Leave local, reversible choices to the next agent when the stated outcome, constraints, and repository evidence are sufficient to choose. A subjective word alone does not require another rule or decision.
-
Specify output shape
- Define only the sections or fields the next consumer uses.
- For agent handoffs, name produced artifact paths and the result needed by the next action. Require exact serialization only when a program parses it.
-
Provide the smallest sufficient context
- Self-contained means sufficient for the assigned action, not complete background.
- Include only context directly consumed by the target action or required result.
- Extra context can anchor judgment, contaminate independent work, and create work.
-
Decompose complex work into verifiable steps
- Expose dependency order when a later action relies on an earlier result.
- Reuse one execution plan to retain all required steps and final verification during multi-step work. Simple, single-action work proceeds directly.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago Changed b661c8af508e
- 1mo ago Changed ea6cf8d2d3ef
- 1mo ago Changed c040433518b3
- 1mo ago Changed 2a8f35b5d175
- 1mo ago First seen · 91 lines · 52 tokens per session scan A 9fafd2e99e98
llm-friendly-context is a skill published in the GitHub repository shinpr/codex-workflows (39 stars, last pushed 2d ago), licensed MIT. It adds 52 tokens to every session and 1,119 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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