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 TJ-Frederick/TheologAI --skill word-studygit clone --depth 1 https://github.com/TJ-Frederick/TheologAIWrote 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/tj-frederick/theologai/word-study)<a href="https://agentmods.dev/skills/tj-frederick/theologai/word-study"><img src="https://agentmods.dev/badge/skills/tj-frederick/theologai/word-study/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/tj-frederick/theologai/word-study"><img src="https://agentmods.dev/badge/skills/tj-frederick/theologai/word-study.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 | $0.00000 | $0.00767 |
| Opus 5.5 | $0.00000 | $0.00307 |
| Sonnet 5.5 | $0.00000 | $0.00153 |
| Haiku 4.5 | $0.00000 | $0.00077 |
Grade A, and why
word-study 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 1mo 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Word Study Skill
Conduct a context-first Greek/Hebrew word study using TheologAI's language tools.
When to Use
Use this workflow when the user asks about:
- The meaning of a Greek or Hebrew word
- What a word means "in the original"
- Strong's concordance lookups
- Word etymology or semantic range
- How a term is used across Scripture
Methodology
Step 1: Establish the Scope
- If the user asks what a word means in a verse, require one exact verse and read it with
bible_lookupin the requested/default translation plus one comparison translation. - If no verse is supplied, label the result a lexical overview, not a contextual meaning, and invite a passage-specific study.
Step 2: Resolve the Verse Token
- Call
original_language_studywith the exact verse and target. - If it returns
needs_disambiguation, use sentence context to select a returned candidate and call again with that sourceposition. Never guess.
Step 3: Consult Lexical Evidence
If the user provides a Strong's number (G####, H####):
- Call
original_language_lookupwith the number,include_extended: true, andusage_level: "overview". TreatcorpusUsageas global distribution evidence that follows the verse-local analysis, never as a way to select a contextual sense. Overview contains totals and the complete canonical-book distribution only; requeststudyortechnicalonly when bounded forms or raw tokens are genuinely needed.
If the user provides an English word without verse context:
- Call
original_language_lookupto search for the term - If multiple results, present candidates unless context supports a selection
Step 4: Compare Translations
- Call
bible_lookupon a key verse with multiple translations (ESV, KJV, NET) - Note how different translations render the word
- Explain the translation choices
Step 5: Synthesize
Present contextual findings in this order:
- Meaning here, in plain English
- Why that reading fits the verse
- Word identity: source form, lemma, transliteration, Strong's identifier
- Grammar: cautious explanation, then raw source code
- Source-separated lexical evidence and limitations
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.
- 1mo ago First seen · 76 lines · 0 tokens per session scan A a660894c1d0d
word-study is a skill published in the GitHub repository TJ-Frederick/TheologAI (24 stars, last pushed today), licensed ISC. It costs nothing until one of its globs matches a file; then it loads 767 tokens. 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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