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 agentmods add skills/shinpr/codex-workflows/llm-friendly-contextnpx skills add shinpr/codex-workflows --skill llm-friendly-contextgit clone --depth 1 https://github.com/shinpr/codex-workflowsWhat 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 | $0.00052 | $0.01066 |
| Opus 5 | $0.00026 | $0.00533 |
| Sonnet 5 | $0.00010 | $0.00213 |
| Haiku 4.5 | $0.00005 | $0.00107 |
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 3d 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.
-
Make vague instructions concrete
- Replace subjective terms with observable conditions, paths, commands, schemas, examples, or decision rules.
- Terms that usually need clarification before handoff:
appropriate,proper,related,existing behavior,optional,as needed,if needed,per convention, unresolved alternatives,TBD, andplaceholder.
-
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 necessary context
- Include purpose, source artifacts, hard constraints, accepted decisions, and unresolved conditions.
- Prefer concrete file paths and section hints over broad module names.
- Follow references only while they can change an in-scope decision, action, or verification result.
-
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.
- 3d 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 (38 stars, last pushed 5d ago), licensed MIT. It adds 52 tokens to every session and 1,066 once invoked, about $0.0003 per session on Opus 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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