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 ychampion/cskill-agents --skill turn-zero-vs-inter-turn-skill-discovery-splitgit clone --depth 1 https://github.com/ychampion/cskill-agentsWrote 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/ychampion/cskill-agents/turn-zero-vs-inter-turn-skill-discovery-split)<a href="https://agentmods.dev/skills/ychampion/cskill-agents/turn-zero-vs-inter-turn-skill-discovery-split"><img src="https://agentmods.dev/badge/skills/ychampion/cskill-agents/turn-zero-vs-inter-turn-skill-discovery-split/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/ychampion/cskill-agents/turn-zero-vs-inter-turn-skill-discovery-split"><img src="https://agentmods.dev/badge/skills/ychampion/cskill-agents/turn-zero-vs-inter-turn-skill-discovery-split.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 | $0.00037 | $0.00422 |
| Opus 5 | $0.00018 | $0.00211 |
| Sonnet 5 | $0.00007 | $0.00084 |
| Haiku 4.5 | $0.00004 | $0.00042 |
Grade A, and why
turn-zero-vs-inter-turn-skill-discovery-split 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 8d 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.
What it actually says
SKILL: Turn-Zero vs Inter-Turn Skill Discovery Split
Domain: skill-discovery Trigger: Use when discovery needs to keep responding to fresh user intent without stalling the streaming loop on every iteration. Source Pattern: Distilled from reviewed skill-discovery, attachment, and streaming-loop implementations.
Core Method
Treat the first real user message as the one place where discovery is allowed to block, because there is no other work yet to hide the latency. After that, move discovery into an asynchronous prefetch that runs while the model streams and tools execute, then consume the results only after the main work finishes. This keeps later discovery off the hot path while still ensuring the first turn gets a complete initial skill surface.
Key Rules
- Feature-flag the async discovery so builds without skill search can drop the expensive string literal without extra curation.
- Gate the async prefetch so it runs only on iterations that may actually emit new discovery output, avoiding repeat work on read-only passes.
- Keep turn-zero discovery tied to real user input, not generated content or system churn.
- Do not await the async prefetch on the streaming path; only collect it after tools complete, and continue streaming in the meantime.
Example Application
When implementing a coding assistant, let the first user turn pay the discovery cost up front so the agent starts with accurate suggestions. On later turns, run discovery in the background and surface it only after the main tool loop finishes.
Anti-Patterns (What NOT to do)
- Do not run the async prefetch while splitting the discovery path without a write-pivot guard; it will fire on every iteration and waste requests.
- Do not let turn-0 work depend on the async path’s completion; there is no prior work to hide under, so block there explicitly.
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.
- 8d ago First seen · 28 lines · 37 tokens per session scan A a4cce2d04377
turn-zero-vs-inter-turn-skill-discovery-split is a skill published in the GitHub repository ychampion/cskill-agents (36 stars, last pushed 5mo ago), licensed MIT. It adds 37 tokens to every session and 422 once invoked, about $0.0002 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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