Atlas / Skills / snowflake-labs / Cococontext

CococontextSAFE

skills/snowflake-labs/cococontext

CocoPlus is an Agentic Operating System for Snowflake Coco. It brings structured, multi-agent workflows to data engineering projects — covering everything from project initialization through spec, plan, build, test, review, and ship phases.

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
1.0.1
Hosts
—
License
MIT
Stars
724
01

Overview

CocoPlus is an Agentic Operating System for Snowflake Coco. It brings structured, multi-agent workflows to data engineering projects — covering everything from project initialization through spec, plan, build, test, review, and ship phases.

Read from source at commit 6b58edc143d7OBSERVED · 2026-10-13
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02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: "context-add"
description: "Guided wizard to capture or update organizational Snowflake/Cortex standards into .cocoplus/context/<category>.md. Presents a menu of 6 standard types, collects answers via multi-turn dialogue, and commits the file."
version: "1.0.1"
author: "CocoPlus"
tags:
  - cocoplus
  - cococontext
---

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?

**pii-policy:**
- Which columns are classified as PII in your Snowflake environment?
- Are AI functions permitted to process PII columns directly, or must they be masked first?
- What masking approach is required? (e.g., dynamic data masking, column-level security)
- Are AI function results that contain PII permitted to be written to non-PII tables?

**warehouse-policy:**
- Which warehouse(s) are approved for AI/Cortex function execution?
- Which warehouse(s) are reserved for reporting only?
- What is the maximum warehouse size for dev/staging vs production runs?
- Are there time-of-day restrictions on large warehouse usage?

**naming-conventions:**
- What prefix/suffix convention applies to Cortex UDF names? (e.g., `udf_`, `_ai`)
- Naming convention for evaluation tables?
- Required schema name for AI/ML objects?
- Case convention: UPPER_SNAKE, lower_snake, or CamelCase?

**governance-gates:**
- Which gates are required before promoting a model to production? (e.g., Data Steward sign-off, CDO review)
- Is a SecondEye critique mandatory for production AI functions?
- Required stakeholders for each gate?
- What documentation must exist before a gate can be passed?

Adapt questions based on previous answers. Multi-turn dialogue is expected.

## Compile and Validate

Compile developer responses into a structured markdown file. Use clear headers per topic. Aim for concise, actionable entries.

Count the compiled content lines. If > 200 lines:
Output: "This context file would exceed 200 lines (<N> lines). Please prioritize or summarize — which entries are most critical?"
Iterate until content is ≤ 200 lines.

## Write and Commit

Create `.cocoplus/context/` directory if it doesn't exist.

Write compiled content to `.cocoplus/context/<category>.md`.

Create a git commit:
```
git add .cocoplus/context/<category>.md
git commit -m "feat(context): <category> standards captured"
```

If the git commit fails, write the file but output:
"⚠ File written but git commit failed. Commit manually with: git add .cocoplus/context/<category>.md && git commit -m 'feat(context): <category> standards captured'"

## Output

```
✓ <category>.md updated (<N> lines)
Saved: .cocoplus/context/<category>.md
Committed: feat(context): <category> standards captured

This file will be loaded by CocoScout when tasks involve <relevant task type>.
```

## Anti-Rationalization

| Shortcut / Temptation | Why It Fails |
|-----------------------|--------------|
| Write without checking line count | Files > 200 lines degrade CocoScout context loading performance — enforce the limit |
| Skip git commit | Context files are governance artifacts; version history is required for audit |
| Ask all questions at once | Multi-turn dialogue produces better responses than a single-shot form |

## Exit Criteria

- [ ] `.cocoplus/context/<category>.md` exists and is ≤ 200 lines
- [ ] Git commit created with message `feat(context): <category> standards captured`
- [ ] Output confirms file path and line count
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-13 · audit v0.4.1 · source sha 6b58edc143d7full audit observations/trust-audit/skill/snowflake-labs__cococontext.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-136b58edc143d7SAFEB89first audit
05

Questions

What does the Cococontext skill do?

CocoPlus is an Agentic Operating System for Snowflake Coco. It brings structured, multi-agent workflows to data engineering projects — covering everything from project initialization through spec, plan, build, test, review, and ship phases.

Is Cococontext safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Cococontext access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (6b58edc143d7), read on 2026-10-13. The repository is watched, and a new audit runs when it changes — this is the first audit.

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