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 agentdb-learning-pluginsgit 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/agentdb-learning-plugins)<a href="https://agentmods.dev/skills/dnyoussef/context-cascade/agentdb-learning-plugins"><img src="https://agentmods.dev/badge/skills/dnyoussef/context-cascade/agentdb-learning-plugins/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/agentdb-learning-plugins"><img src="https://agentmods.dev/badge/skills/dnyoussef/context-cascade/agentdb-learning-plugins.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 | $0.00067 | $0.04252 |
| Opus 5.5 | $0.00027 | $0.01701 |
| Sonnet 5.5 | $0.00013 | $0.00850 |
| Haiku 4.5 | $0.00007 | $0.00425 |
Grade A, and why
agentdb-learning-plugins 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 — 661 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LIBRARY-FIRST PROTOCOL (MANDATORY)
Before writing ANY code, you MUST check:
Step 1: Library Catalog
- Location:
.claude/library/catalog.json - If match >70%: REUSE or ADAPT
Step 2: Patterns Guide
- Location:
.claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md - If pattern exists: FOLLOW documented approach
Step 3: Existing Projects
- Location:
D:\Projects\* - If found: EXTRACT and adapt
Decision Matrix
| Match | Action |
|---|---|
| Library >90% | REUSE directly |
| Library 70-90% | ADAPT minimally |
| Pattern exists | FOLLOW pattern |
| In project | EXTRACT |
| No match | BUILD (add to library after) |
When NOT to Use This Skill
- Local-only operations with no vector search needs
- Simple key-value storage without semantic similarity
- Real-time streaming data without persistence requirements
- Operations that do not require embedding-based retrieval
Success Criteria
- Vector search query latency: <10ms for 99th percentile
- Embedding generation: <100ms per document
- Index build time: <1s per 1000 vectors
- Recall@10: >0.95 for similar documents
- Database connection success rate: >99.9%
- Memory footprint: <2GB for 1M vectors with quantization
Edge Cases & Error Handling
- Rate Limits: AgentDB local instances have no rate limits; cloud deployments may vary
- Connection Failures: Implement retry logic with exponential backoff (max 3 retries)
- Index Corruption: Maintain backup indices; rebuild from source if corrupted
- Memory Overflow: Use quantization (4-bit, 8-bit) to reduce memory by 4-32x
- Stale Embeddings: Implement TTL-based refresh for dynamic content
- Dimension Mismatch: Validate embedding dimensions (384 for sentence-transformers) before insertion
Guardrails & Safety
- NEVER expose database connection strings in logs or error messages
- ALWAYS validate vector dimensions before insertion
- ALWAYS sanitize metadata to prevent injection attacks
- NEVER store PII in vector metadata without encryption
- ALWAYS implement access control for multi-tenant deployments
- ALWAYS validate search results before returning to users
What ships with it
19 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.
- examples/example-1-q-learning.md 12 KB
- examples/example-2-sarsa.md 14 KB
- examples/example-3-deep-rl.md 13 KB
- graphviz/workflow.dot 4.5 KB
- readme.md 18 KB
- references/reward-design.md 13 KB
- references/rl-algorithms.md 12 KB
- resources/scripts/benchmark_9algorithms.sh 5.4 KB runs code
- resources/scripts/readme.md 6.7 KB
- resources/scripts/test_learning.py 7.7 KB runs code
- resources/scripts/train_rl_agent.py 7.5 KB runs code
- resources/templates/actor-critic.json 3.4 KB
- resources/templates/decision-transformer.yaml 5.6 KB
- resources/templates/q-learning-config.yaml 2.6 KB
- resources/templates/readme.md 7.9 KB
- SKILL-meta.yaml 417 B
- tests/test-1-q-learning.md 7.5 KB
- tests/test-2-policy-gradient.md 11 KB
- tests/test-3-decision-transformer.md 15 KB
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 · 661 lines · 67 tokens per session scan A 0ef2a5a230b8
agentdb-learning-plugins is a skill published in the GitHub repository DNYoussef/context-cascade (32 stars, last pushed 3mo ago), licensed MIT. It adds 67 tokens to every session and 4,252 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.
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