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/ai-coding-project-boilerplate --skill llm-friendly-contextgit clone --depth 1 https://github.com/shinpr/ai-coding-project-boilerplateWrote 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/ai-coding-project-boilerplate/llm-friendly-context)<a href="https://agentmods.dev/skills/shinpr/ai-coding-project-boilerplate/llm-friendly-context"><img src="https://agentmods.dev/badge/skills/shinpr/ai-coding-project-boilerplate/llm-friendly-context.svg" alt="Measured on agentmods" 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 | $0.01242 |
| Opus 5 | $0.00026 | $0.00621 |
| Sonnet 5 | $0.00010 | $0.00248 |
| Haiku 4.5 | $0.00005 | $0.00124 |
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 — 86 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 agent should know what to read, what to do, what counts as success, and which unresolved decisions can change the result.
This skill governs the clarity of LLM-facing output — prompts, handoffs, and generated artifacts. The caller supplies the artifact type and any artifact-specific template or input contract; include only the information its consumer uses to decide, act, or verify. Use a declared contract's field names and value meanings when the consumer branches on them.
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."
- Keep a prohibition only when it protects an irreversible boundary or a shipped contract; then name the protected condition and the allowed action alongside it
-
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
-
Specify output shape
- Define the sections, fields, table columns, JSON keys, or checklist items the consumer uses
- For handoffs, include produced artifact paths and status fields only when they control the next transition
-
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; stop when the next link only confirms what is already decided
-
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
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 Changed dc4917309876
- 8d ago First seen · 86 lines · 52 tokens per session scan A 3f7e7299f688
llm-friendly-context is a skill published in the GitHub repository shinpr/ai-coding-project-boilerplate (228 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 1,242 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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