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 Snowflake-Labs/cocoplus --skill context-addgit clone --depth 1 https://github.com/Snowflake-Labs/cocoplusWrote 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/snowflake-labs/cocoplus/context-add)<a href="https://agentmods.dev/skills/snowflake-labs/cocoplus/context-add"><img src="https://agentmods.dev/badge/skills/snowflake-labs/cocoplus/context-add/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/snowflake-labs/cocoplus/context-add"><img src="https://agentmods.dev/badge/skills/snowflake-labs/cocoplus/context-add.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 | $0.00048 | $0.01225 |
| Opus 5.5 | $0.00019 | $0.00490 |
| Sonnet 5.5 | $0.00010 | $0.00245 |
| Haiku 4.5 | $0.00005 | $0.00122 |
Grade A, and why
context-add 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Your objective is to capture organizational standards into a versioned context file.
Pre-flight Check
Check that .cocoplus/ exists. If not:
Output: "CocoPlus not initialized in this directory. Run $pod init to begin." Then stop.
Present Category Menu
Output:
Which context would you like to add or update?
1. approved-models — Which Cortex models are approved for use
2. quality-thresholds — Minimum accuracy and performance standards
3. pii-policy — PII handling rules for AI functions
4. warehouse-policy — Which warehouses to use for which query types
5. naming-conventions — Object naming standards for Snowflake objects
6. governance-gates — Required approval steps before production deployment
Wait for the developer to select a number (1–6).
Map selection to filename:
- 1 →
approved-models.md - 2 →
quality-thresholds.md - 3 →
pii-policy.md - 4 →
warehouse-policy.md - 5 →
naming-conventions.md - 6 →
governance-gates.md
Check Existing Content
If .cocoplus/context/<category>.md already exists, read and display it, then ask:
"This file already exists. Would you like to update it (add/change entries) or replace it entirely?"
Wait for response before proceeding.
Collect Standards via Dialogue
For the selected category, ask 5–10 targeted questions. Examples per category:
approved-models:
- Which Cortex models are approved for production classification tasks? (e.g., mistral-large2, llama3.1-70b)
- Which models are approved for extraction/NER tasks?
- Are there models that are explicitly blocked? Why?
- Is there a default model to use when none is specified?
- Any cost or latency constraints that restrict model choice?
quality-thresholds:
- What is the minimum classification accuracy required for production? (e.g., 85%)
- Maximum acceptable p95 latency for AI function calls? (e.g., 2000ms)
- Minimum evaluation dataset size before promoting a model?
- Required precision/recall floor for extraction tasks?
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 · 138 lines · 48 tokens per session scan A c87c92cc7a5d
context-add is a skill published in the GitHub repository Snowflake-Labs/cocoplus (724 stars, last pushed 16d ago), licensed MIT. It adds 48 tokens to every session and 1,225 once invoked, about $0.0002 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.
Other skills, from other repositories
prompt-engineering
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, designing production prompt templates, or building AI-powered features.
natural
Render the provided prompt template with Jinja context and send it to the active NPC's LLM. The rendered text becomes the user prompt; the LLM response is placed in context['output'].
rag-retrieval
Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, embedding documents, implementing hybrid search, contextual retrieval, HyDE, agentic RAG, multimodal RAG, query decomposition, reranking, or pgvector search.
error-analysis
Evals-first error analysis for LLM apps: clusters real Langfuse or JSONL traces into a human-confirmed failure taxonomy with counts, then recommends binary pass/fail evals for recurring named modes. Use to learn what to measure before writing evals. Not for CI failures.
testing-llm
LLM and AI testing patterns — mock responses, evaluation with DeepEval/RAGAS, structured output validation, and agentic test patterns (generator, healer, planner). Use when testing AI features, validating LLM outputs, or building evaluation pipelines.
golden-dataset
Golden dataset lifecycle patterns for curation, versioning, quality validation, and CI integration. Use when building evaluation datasets, managing dataset versions, validating quality scores, or integrating golden tests into pipelines.