Observed Signal · May 6, 2026 · Technical Release · Source: DEV Community · Impact: 3/5 · Sentiment: Positive
PreBrief fixes agent skills discovery ceiling
A developer describes why AI coding agents often ignore installed Agent Skills and presents PreBrief, an open-source client-side retrieval injector that reliably delivers relevant skill content into the high-authority user-message slot. The article explains Claude Code's discovery ceiling: available_skills descriptions are truncated (250 chars) and the section is limited to 1% of the context window (floor 8,000 chars), producing an effective ≈32-skill ceiling at a 200K context. Agents also frequently skip skills even when visible. Server-side hook injection (UserPromptSubmit additionalContext) failed because Claude Code labels hook output as advisory, reducing its authority. The author pivoted to a local daemon + VSCode extension that searches chunked skill sections and prepends formatted results to the user prompt; this approach produced correct tool invocations for a Google Workspace CLI example. PreBrief (Python daemon, FAISS index, VSCode extension) is published on GitHub.
Addresses a practical scalability and reliability problem in LLM-based coding agents (skill discovery, token overhead, and delivery authority) with an open-source solution that can influence how agents integrate local knowledge and reduce per-turn token costs.
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Key Takeaways & Evidence Grounding
- Claude Code truncates skill descriptions to 250 characters and limits the available_skills section to 1% of the context window (floor 8,000 characters), creating an effective ≈32-skill ceiling at a 200K context window.
- The author measured 95 Google Workspace CLI (gws) skills consuming 2,007 tokens per turn in Claude Code's available_skills section.
- Server-side hook injection via Claude Code's UserPromptSubmit additionalContext failed because the agent treats hook-labeled content as advisory and often ignores it.
- The author built PreBrief — an open-source Python daemon (FAISS index) plus a thin VSCode extension — that searches chunked skill sections and injects results into the user's prompt; this client-side delivery produced correct first-turn CLI calls in tests.
- Design choices that improved results: chunking SKILL.md at section headings, returning top-3 results, plain-language framing, and using a small embedding model for low-latency local retrieval.
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