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 DNYoussef/context-cascade --skill when-optimizing-vector-search-use-agentdb-optimizationgit clone --depth 1 https://github.com/DNYoussef/context-cascadeWrote 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/dnyoussef/context-cascade/when-optimizing-vector-search-use-agentdb-optimization)<a href="https://agentmods.dev/skills/dnyoussef/context-cascade/when-optimizing-vector-search-use-agentdb-optimization"><img src="https://agentmods.dev/badge/skills/dnyoussef/context-cascade/when-optimizing-vector-search-use-agentdb-optimization/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/dnyoussef/context-cascade/when-optimizing-vector-search-use-agentdb-optimization"><img src="https://agentmods.dev/badge/skills/dnyoussef/context-cascade/when-optimizing-vector-search-use-agentdb-optimization.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 | $0.00000 | $0.02011 |
| Opus 5.5 | $0.00000 | $0.00804 |
| Sonnet 5.5 | $0.00000 | $0.00402 |
| Haiku 4.5 | $0.00000 | $0.00201 |
Grade A, and why
when-optimizing-vector-search-use-agentdb-optimization 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 — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/============================================================================/ /* AGENTDB VECTOR SEARCH OPTIMIZATION SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/
name: AgentDB Vector Search Optimization version: 1.0.0 description: | [assert|neutral] AgentDB Vector Search Optimization skill for agentdb workflows [ground:given] [conf:0.95] [state:confirmed] category: agentdb tags:
- general author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute AgentDB Vector Search Optimization workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic agentdb processes"
/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/
[define|neutral] SKILL := { name: "AgentDB Vector Search Optimization", category: "agentdb", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/
[define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]
Kanitsal Cerceve (Evidential Frame Activation)
Kaynak dogrulama modu etkin.
/----------------------------------------------------------------------------/ /* S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------*/
[define|neutral] TRIGGER_POSITIVE := { keywords: ["AgentDB Vector Search Optimization", "agentdb", "workflow"], context: "user needs AgentDB Vector Search Optimization capability" } [ground:given] [conf:1.0] [state:confirmed]
/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 243 lines · 0 tokens per session scan A babe0b801335
when-optimizing-vector-search-use-agentdb-optimization is a skill published in the GitHub repository DNYoussef/context-cascade (32 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,011 tokens. 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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