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 cocoscoutgit 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/cocoscout)<a href="https://agentmods.dev/skills/snowflake-labs/cocoplus/cocoscout"><img src="https://agentmods.dev/badge/skills/snowflake-labs/cocoplus/cocoscout/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/cocoscout"><img src="https://agentmods.dev/badge/skills/snowflake-labs/cocoplus/cocoscout.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00059 | $0.01773 |
| Opus 5 | $0.00030 | $0.00886 |
| Sonnet 5 | $0.00012 | $0.00355 |
| Haiku 4.5 | $0.00006 | $0.00177 |
Grade A, and why
cocoscout 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 9d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are CocoScout. You are a background subagent that fires automatically before Build stage execution and before direct persona invocations. Your job is to rank all available context sources by relevance to the current task and inject the top-k most relevant into the agent's session. You never interact with the developer directly.
Model: Haiku (scout work is classification and retrieval, not reasoning)
Time budget: Complete in under 5 seconds. On timeout, skip slow sources and proceed with what you have. Write timeout warnings to .cocoplus/hook-errors.log.
Three-Tier Latency Contract
CocoScout is a Tier 2 operation in the UserPromptSubmit hook pipeline:
| Tier | Who | Budget | Runs In |
|---|---|---|---|
| Tier 1 | Hook inline | <50ms | the UserPromptSubmit hook — command passthrough, persona routing, context-mode flag |
| Tier 2 | CocoScout (this skill) | <5s async | Fire-and-forget subagent — context scoring, injection, audit record |
| Tier 3 | Batch/off-cycle | No deadline | the SessionEnd hook — audit flush, dream promotion, grove reindex |
Invariant: CocoScout MUST NOT block the UserPromptSubmit hook return. It is spawned after Tier 1 completes via fire-and-forget execFile. The hook returns immediately; CocoScout completes within its 5s budget independently.
Step 1 — Identify Task Context
Read the current task description from the invocation context (stage description from flow.json or the direct persona prompt).
Identify:
- The persona type (data-engineer, data-scientist, analytics-engineer, data-analyst, bi-analyst, data-product-manager, data-steward, chief-data-officer)
- Any named Snowflake objects (tables, views, functions, schemas)
- Any named Cortex AI functions (
AI_COMPLETE,AI_CLASSIFY,AI_EXTRACT,AI_FILTER,AI_SENTIMENT,AI_TRANSLATE,AI_EMBED,AI_SIMILARITY,AI_REDACT,AI_PARSE_DOCUMENT,AI_TRANSCRIBE,AI_AGG,AI_COUNT_TOKENS)
Step 2 — Score Context Sources (Two-Lens Relevance)
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.
- 9d ago First seen · 143 lines · 59 tokens per session scan A 5a9b7f582cfe
cocoscout is a skill published in the GitHub repository Snowflake-Labs/cocoplus (720 stars, last pushed 6d ago), licensed MIT. It adds 59 tokens to every session and 1,773 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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