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 AcKeskin/contexture --skill new-agents-mdgit clone --depth 1 https://github.com/AcKeskin/contextureWrote 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/ackeskin/contexture/new-agents-md)<a href="https://agentmods.dev/skills/ackeskin/contexture/new-agents-md"><img src="https://agentmods.dev/badge/skills/ackeskin/contexture/new-agents-md/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/ackeskin/contexture/new-agents-md"><img src="https://agentmods.dev/badge/skills/ackeskin/contexture/new-agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00078 | $0.04630 |
| Opus 5.5 | $0.00031 | $0.01852 |
| Sonnet 5.5 | $0.00016 | $0.00926 |
| Haiku 4.5 | $0.00008 | $0.00463 |
Grade A, and why
new-agents-md 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 — 368 lines — stays where its author put it; the contents beside it link to each section on GitHub.
new-agents-md
The cross-tool projection organ. Closes the gap that the entire instruction surface here is
Claude-Code-exclusive: CLAUDE.md, the @claude-md/... imports, the
~/.claude/architectural-rules/ loader, the typed-memory warning/feedback corpus, and every
skill are invisible to any other coding agent. The moment a non-Claude agent (Codex, Cursor,
Aider, Continue, Zed) is pointed at a repo, it starts blind and guesses exactly what the corpus
already pins — test commands, package manager, the "never commit AI attribution" landmine, the
architectural defaults.
This skill projects the lean corpus into a vendor-neutral, repo-root AGENTS.md (plus a global
one) that those agents read on session start. It is a distill-then-interview organ: it reads
the corpus and proposes a draft before asking anything, then interviews only for what no corpus
read can supply.
When to run
- User types
/new-agents-md(explicit trigger). - User says "generate an AGENTS.md," "make this repo readable by Codex/Cursor/Aider," "scaffold cross-tool instructions," "I want a vendor-neutral agents file."
- Do not auto-fire. No session-start or event-hook triggers. Mode A only — every run goes
through the full distill + interview. (Trigger lives in the frontmatter
descriptionperuniversal/skill-auto-fire.md; there is no SessionStart hook.)
Why this skill exists
The cross-tool convention is AGENTS.md — a vendor-neutral, repo-root instruction file. The
naive fix from the convention's write-ups is "make AGENTS.md canonical and reduce CLAUDE.md
to a thin @AGENTS.md stub." That stub-inversion is rejected here. The value of this
environment lives in machinery a generic agent cannot execute — on-demand rule loading,
discovery/prep organs, typed memory, skills. A generic reader can run none of it, so AGENTS.md
cannot @import the essentials; it must carry a flattened, behaviour-only projection of the
corpus.
Hand-maintaining that projection beside the live corpus would reintroduce the duplication that
single-source-of-truth exists to avoid — a stale hand-copied AGENTS.md is worse than none. So
AGENTS.md is treated as a derived artefact (like a codemap: regenerate on demand), not a
hand-maintained twin (like memory: authored, versioned). This skill is the generator.
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 First seen · 368 lines · 78 tokens per session scan A 15a678599818
new-agents-md is a skill published in the GitHub repository AcKeskin/contexture (2 stars, last pushed 2mo ago), licensed MIT. It adds 78 tokens to every session and 4,630 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
ai-ml-development
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
android-development
Android development with Kotlin, Jetpack Compose, and modern Android architecture. Use when building Android apps, implementing Material Design, or following Android best practices.
email-systems
Transactional email (Resend, SendGrid, SES), templates (React Email, MJML), deliverability (SPF/DKIM/DMARC), and inboxing best practices. Use when building email infrastructure, designing templates, or troubleshooting deliverability.
agent-go-expert
Specialist subagent: Go concurrency, error handling, stdlib patterns, Chi/Echo web frameworks specialist. Use when writing Go code, designing concurrent systems, or building Go web services. Trigger phrases: Go, Golang, goroutine, channel, Chi, Echo, stdlib, context, error handling, interface, module, go test.
agent-svelte-expert
Specialist subagent: Svelte 5 runes, SvelteKit routing, form actions, and compile-time reactivity specialist. Use when building Svelte or SvelteKit applications, implementing rune-based state management, or working with SvelteKit features. Trigger phrases: Svelte, SvelteKit, runes, $state, $derived, $effect, $props…
literature-review-planner
Structured literature review planning with systematic methodology, source evaluation, and synthesis frameworks. Use when planning academic literature reviews, research surveys, systematic reviews, or scoping reviews.