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 grove-glossarygit 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/grove-glossary)<a href="https://agentmods.dev/skills/snowflake-labs/cocoplus/grove-glossary"><img src="https://agentmods.dev/badge/skills/snowflake-labs/cocoplus/grove-glossary/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/grove-glossary"><img src="https://agentmods.dev/badge/skills/snowflake-labs/cocoplus/grove-glossary.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 | $0.00033 | $0.01183 |
| Opus 5 | $0.00016 | $0.00592 |
| Sonnet 5 | $0.00007 | $0.00237 |
| Haiku 4.5 | $0.00003 | $0.00118 |
Grade A, and why
grove-glossary 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 7d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
$grove glossary
Scan project artifacts for candidate domain terms and propose additions to .cocoplus/grove/language/glossary.md. Developer reviews and confirms each proposed entry before it is written.
Preconditions
.cocoplus/must be initialized- At least one of:
lifecycle/spec.md,flow.json,lifecycle/plan.md,prompts/*.md, or CocoCupper findings must exist
Step-by-Step Behavior
-
Verify initialization: Check
.cocoplus/exists. If not, output: "CocoPlus not initialized. Run$pod initfirst." and exit. -
Create glossary directory: Ensure
.cocoplus/grove/language/exists. Create if missing. -
Read existing glossary: Read
.cocoplus/grove/language/glossary.mdif it exists. Extract already-defined terms and aliases using case-insensitive normalization:- Trim leading/trailing whitespace
- Collapse repeated spaces
- Compare lowercase term text
- Compare aliases as duplicate keys for the canonical term
-
Scan project artifacts for candidate terms:
lifecycle/spec.md— function names, capability descriptions, domain nouns.cocoplus/flow.json— stage names and descriptionslifecycle/plan.md— decision terms, evaluation referenceslifecycle/discuss.md— threshold names, methodology terms.cocoplus/prompts/*.md— AI function input/output descriptions.cocoplus/grove/cupper-findings.md— pattern names, anti-pattern terms.cocoplus/map/coco-map.json— capability names from domain section (if exists)
-
Extract candidate terms: Look for multi-word noun phrases that appear as key concepts:
- Function names used as domain concepts (e.g., "sentiment score", "churn propensity")
- Metric and threshold names (e.g., "accuracy baseline", "gold standard dataset")
- Process terms that appear in multiple artifacts (e.g., "evaluation harness", "prompt iteration")
- Exclude generic technical terms (SQL keywords, standard Snowflake function names)
-
Filter known terms: Remove any candidates that already appear in the existing glossary as either a canonical term or an alias. If a candidate duplicates an alias but adds useful context, propose an update to the existing entry instead of a new term.
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.
- 7d ago First seen · 122 lines · 33 tokens per session scan A d9ce672ee835
grove-glossary is a skill published in the GitHub repository Snowflake-Labs/cocoplus (720 stars, last pushed 7d ago), licensed MIT. It adds 33 tokens to every session and 1,183 once invoked, about $0.0002 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 2026-09-03.
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