grove-glossary

grove-glossary is a skill for Claude Code from Snowflake-Labs/cocoplus. It costs 33 tokens per session (1,183 once invoked), scanned A, original, MIT.

A glossary-review tool that scans project files for important domain terms and proposes additions to a shared glossary for developer approval.

In plain words
What is it for?
Use it to discover candidate terms, compare them with existing entries, and review proposed glossary additions.
Why use it?
It helps teams use the same names and meanings for concepts across specifications, plans, workflows, prompts, and findings.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter.

Good fit Use it to discover candidate terms, compare them with existing entries, and review proposed glossary additions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/snowflake-labs/cocoplus/grove-glossary
Install

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.

Any agent
npx skills add Snowflake-Labs/cocoplus --skill grove-glossary
Clone the repo
git clone --depth 1 https://github.com/Snowflake-Labs/cocoplus

Made for: Claude Code.

Wrote 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.

agentmods badge for grove-glossary

README.md
[![agentmods](https://agentmods.dev/badge/skills/snowflake-labs/cocoplus/grove-glossary/github.svg)](https://agentmods.dev/skills/snowflake-labs/cocoplus/grove-glossary)
Your own site
<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.

agentmods 80×15 button for grove-glossary

Your own site · 80×15
<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>
Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,183 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 7d ago against content hash d9ce672ee835, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

.cortex/skills/cocogrove/grove-glossary.skill.md · 122 lines

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

  1. Verify initialization: Check .cocoplus/ exists. If not, output: "CocoPlus not initialized. Run $pod init first." and exit.

  2. Create glossary directory: Ensure .cocoplus/grove/language/ exists. Create if missing.

  3. Read existing glossary: Read .cocoplus/grove/language/glossary.md if 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
  4. Scan project artifacts for candidate terms:

    • lifecycle/spec.md — function names, capability descriptions, domain nouns
    • .cocoplus/flow.json — stage names and descriptions
    • lifecycle/plan.md — decision terms, evaluation references
    • lifecycle/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)
  5. 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)
  6. 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.

Read the full file on GitHub · 122 lines

Changes

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.

  1. 7d ago First seen · 122 lines · 33 tokens per session scan A d9ce672ee835

Subscribe to this mod's changes

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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