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 cocoleangit 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/cocolean)<a href="https://agentmods.dev/skills/snowflake-labs/cocoplus/cocolean"><img src="https://agentmods.dev/badge/skills/snowflake-labs/cocoplus/cocolean/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/cocolean"><img src="https://agentmods.dev/badge/skills/snowflake-labs/cocoplus/cocolean.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 | $0.00041 | $0.01253 |
| Opus 5.5 | $0.00016 | $0.00501 |
| Sonnet 5.5 | $0.00008 | $0.00251 |
| Haiku 4.5 | $0.00004 | $0.00125 |
Grade A, and why
cocolean 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 12d 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.
This is a copy
100% identical to cocolean — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Objective
You are executing CocoLean — the minimum viable Cortex surface discipline. Your task depends on how this skill was invoked:
$lean/$lean full— activate full mode (all three CocoLean modes), run the pre-build decision ladder$lean lite— switch to lite mode (ladder only), run the pre-build decision ladder$lean ultra— switch to ultra mode (full + enforcement), run the pre-build decision ladder$leanwith no prior context — treat as$lean full
Before proceeding, verify that .cocoplus/ exists. If not, output: "CocoPlus is not initialized. Run $pod init first." Then stop.
Step 1 — Set Intensity Level
Parse the invocation:
| Command | Mode | Write to .cocoplus/modes/lean.mode |
|---|---|---|
$lean lite |
lite |
lite |
$lean full |
full |
full |
$lean ultra |
ultra |
ultra |
$lean |
full (default) |
full |
Write the mode string to .cocoplus/modes/lean.mode. Confirm: "CocoLean mode set to [mode]."
If .cocoplus/modes/lean.mode already exists, read its current value and note the change: "CocoLean mode changed from [previous] to [new]."
Step 2 — Run the Pre-Build Decision Ladder
Surface the six-rung Cortex-adapted decision ladder. Present it as a checklist the developer must work through before writing any new Cortex AI function, pipeline stage, or subagent:
CocoLean Decision Ladder — work top to bottom, stop at the first YES:
[ ] Rung 1: Can a deterministic rule, SQL expression, lookup table, or threshold accomplish this without AI inference?
[ ] Rung 2: Does a Snowflake native built-in handle it? (FLATTEN, MATCH_RECOGNIZE, window functions, conditional expressions, SEARCH, OBJECT_CONSTRUCT…)
[ ] Rung 3: Does an existing UDF, stored procedure, or Cortex function in this project already solve this?
[ ] Rung 4: Would a single AI_COMPLETE call with a well-scoped prompt be sufficient?
[ ] Rung 5: Can the minimum working function be written in under ten lines? (Write the minimum version first — complexity is earned, not assumed.)
[ ] Rung 6: A full Cortex function or multi-stage pipeline is justified. Proceed to build.
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.
- 12d ago First seen · 102 lines · 41 tokens per session scan A 0f4540257ab8
cocolean is a skill published in the GitHub repository Snowflake-Labs/cocoplus (724 stars, last pushed 12d ago), licensed MIT. It adds 41 tokens to every session and 1,253 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to cocolean, differing in 0 lines, and is treated as a copy.
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.
langgraph
LangGraph 1.x (LTS) Python workflow patterns for state management, delta channels, resilience (node timeouts, error handlers, graceful drain), routing, parallel execution, supervisor-worker, tool calling, checkpointing, human-in-loop, streaming (v2 format), subgraphs, and functional API. Use when building LangGraph…
openkb-html-critic
Use to review a generated HTML deck or single-page artifact for visual quality and structural correctness. Especially good at catching CSS specificity bugs where slide-modifier classes (.divider, .center, .q, .flow etc.) accidentally override the base .slide{display:none} and cause one slide to stack on top of every…
terraform
Operate Terraform and OpenTofu across the whole infrastructure lifecycle: module structure, state backends and locking, plan/apply workflow, drift detection, remote state, upgrade and refactor flows, and evidence-based diagnostics. Use when running or inspecting terraform plans, applies, state files, imports, or state…
cncf-landscape
Use this skill when discovering and comparing cloud-native technologies from the CNCF Landscape for an architecture or engineering decision. Query the live public Landscape API, filter candidates by capability, category, maturity, license, and repository signals, then produce an evidence-backed shortlist with…
go-to-market
Plan and evaluate go-to-market strategy using positioning, acquisition, growth economics, market-entry, competitive-response, and governed agent-assisted acquisition experiments. Use for ICP, messaging, channel, CAC/LTV, beachhead, or automated-GTM strategy decisions. Do not use for campaign execution, CRM mutations…