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 prepgit 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/prep)<a href="https://agentmods.dev/skills/ackeskin/contexture/prep"><img src="https://agentmods.dev/badge/skills/ackeskin/contexture/prep.svg" alt="Measured on agentmods" 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 | $0.00073 | $0.04258 |
| Opus 5 | $0.00036 | $0.02129 |
| Sonnet 5 | $0.00015 | $0.00852 |
| Haiku 4.5 | $0.00007 | $0.00426 |
Grade A, and why
prep 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 4d 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 — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
prep
The prep organ. Consumes discover, deliver, the architectural-rules tree, and project .claude/architecture.md (when present, per project-architecture.md).
Prep prevents drift by priming Claude with the rules before code is written. Review detects drift after the fact. Different jobs, complementary.
When to run
Auto-fire (the description field above drives this — Claude reads it and fires the skill at the right moment):
- First substantive task of a session. A substantive task is a code-writing, design, debugging, or review request. Trivial questions, lookups, and "what is X" style prompts do not trigger prep.
- First substantive task after
/clear. Context was wiped; re-prime. - User signals a topic shift. Phrases like "now let's work on X", "switching to Y", "different question about Z" where Z is a new domain / language / project.
Manual fire:
- User types
/prep(explicit trigger, e.g. when Claude failed to auto-fire, or the user knows they are about to shift). - User asks "prep for this" / "load architectural rules" / "prime for X".
Do not auto-fire on:
- Session start itself (nothing to prep against yet).
- Trivial questions / pure information requests ("what does git reflog do?").
- Every message — prep is per-task, not per-turn.
Inputs
- Task text. The substantive request that triggered prep. If invoked manually via
/prep [scope-hint], use the hint to bias scope detection. - Working directory.
$CLAUDE_PROJECT_DIRif set, otherwisecwd. Determines project context and whether a.claude/architecture.mdis available. - Recent session state. Earlier turns in the same session (for topic-shift detection).
Procedure
0. Read the floor watermark (rule-prime hook handoff)
The rule-prime hook primes the floor — always-tier + project-tier rules, plus the one language tier in a single-language repo — mechanically at SessionStart, and incremental tiers per prompt at UserPromptSubmit. It records what it primed in a per-session watermark (~/.claude/session-state.json, key rulePrime, keyed by session id): { scopes: [...], floorPrimed: true|false, droppedRules: [...], language, polyglot }. When the floor exceeds its token budget the hook drops rules, sets floorPrimed: false, and lists the dropped rule keys in droppedRules — the watermark records what actually reached context, and the dropped rules are prep's to backfill.
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.
- 4d ago First seen · 232 lines · 73 tokens per session scan A c8bdf3a8f94f
prep is a skill published in the GitHub repository AcKeskin/contexture (2 stars, last pushed 1mo ago), licensed MIT. It adds 73 tokens to every session and 4,258 once invoked, about $0.0004 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 2026-09-03.
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