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 agentmods add skills/codebygarv/ai-skills/codebase-glossary-buildernpx skills add codebygarv/Ai-skills --skill codebase-glossary-buildergit clone --depth 1 https://github.com/codebygarv/Ai-skillsWhat 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 | $0.00025 | $0.00363 |
| Opus 5 | $0.00013 | $0.00181 |
| Sonnet 5 | $0.00005 | $0.00073 |
| Haiku 4.5 | $0.00003 | $0.00036 |
Grade A, and why
codebase-glossary-builder 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 2d 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.
What it actually says
Purpose
Scan a codebase and documentation to compile a structured Domain Glossary / Ubiquitous Language dictionary, resolving ambiguity around internal acronyms, overloaded terms, and domain entities.
When to Use
- Onboarding new engineers to a domain-dense codebase (e.g. Fintech, Healthcare, Logistics, AdTech).
- Resolving naming confusion where different teams use different words for the same concept.
- Aligning engineering naming directly with product and business definitions.
What to Analyze
- Domain Entities: Key nouns used in database models and APIs (e.g.
Tenancy,LedgerEntry,Claim). - Internal Acronyms: Decoding company or industry jargon (e.g.
MRR,ACH,KYC,EOD). - Overloaded / Confusing Terms: Words with multiple meanings (e.g. "User" vs "Customer" vs "Subscriber").
- Lifecycle States: Definitions of status terms (e.g. what distinguishes
VoidedfromCanceled). - Code References: Linking each term to primary source files/interfaces where it is implemented.
Output Format
- Alphabetical Glossary Table: Term, Domain Context, Official Definition, Code Mapping.
- Disambiguation Warnings: Highlighting frequently confused terms.
- Deprecated Terms: Legacy terms that should no longer be used in new code.
Avoid
- Defining generic programming terms (e.g., "Function", "Array").
- Leaving definitions vague without mapping them to concrete codebase models.
What ships with it
2 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.
- 2d ago First seen · 34 lines · 25 tokens per session scan A dd7ffa82aacf
codebase-glossary-builder is a skill published in the GitHub repository codebygarv/Ai-skills (24 stars, last pushed 13d ago), licensed MIT. It adds 25 tokens to every session and 363 once invoked, about $0.0001 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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