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/biolytics-ai/rhetoric-engine/intent-framernpx skills add Biolytics-AI/rhetoric-engine --skill intent-framergit clone --depth 1 https://github.com/Biolytics-AI/rhetoric-engineWrote 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/biolytics-ai/rhetoric-engine/intent-framer)<a href="https://agentmods.dev/skills/biolytics-ai/rhetoric-engine/intent-framer"><img src="https://agentmods.dev/badge/skills/biolytics-ai/rhetoric-engine/intent-framer.svg" alt="Measured on agentmods" 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 | $0.00043 | $0.01396 |
| Opus 5 | $0.00022 | $0.00698 |
| Sonnet 5 | $0.00009 | $0.00279 |
| Haiku 4.5 | $0.00004 | $0.00140 |
Grade A, and why
intent-framer 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 5d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intent Framer
Transform a rough presentation topic into a user-owned intent brief. Use this before argument, outline, design, rendering, or deck compilation whenever the audience, desired change, or success test is unclear.
Keep the user's purpose primary. Do not make a generic communication plan when the user needs a specific audience outcome.
Default to elicitation, not drafting. The goal is to help the user discover and sharpen their view, not to get them to approve the agent's inferred view.
Inputs
- Rough topic, prompt, notes, meeting context, or existing deck request.
- Known audience, venue, format, timing, and decision context.
- User constraints such as duration, politics, brand rules, required content, source limits, or tone.
- Any stated fears, objections, desired reactions, or non-goals.
Outputs
Default output is an Intent Exploration until the readiness checks pass.
Return an Intent Exploration with:
what_user_has_said: only the intent facts the user supplied or selected.candidate_frames: 2-4 abstract hypotheses the user can react to, not concrete claims disguised as assumptions.activating_questions: the next 1-3 questions that would most improve the user's own thinking.zoom_out_option: a way to step back if the candidate frames feel wrong.next_decision: the smallest decision needed before drafting a brief.
Use this compact format:
## Intent Exploration
- What you have said:
- Candidate frames:
- Activating questions:
- Zoom-out option:
- Next decision:
Return an Intent Brief only when the user has supplied, selected, or explicitly accepted enough substance for the fields below:
primary_audience: the real decision-maker or learner, not a vague demographic.current_state: what the audience currently believes, knows, feels, or does.desired_change: what should be different after the presentation.stakes: why the change matters now.success_test: observable evidence that the presentation worked.constraints: time, medium, politics, brand, data, compliance, or production limits.objections: likely resistance, doubts, risks, or hidden agendas.non_goals: what the presentation must not try to do.
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.
- 5d ago First seen · 125 lines · 43 tokens per session scan A 8e80d0dd209c
intent-framer is a skill published in the GitHub repository Biolytics-AI/rhetoric-engine (1 stars, last pushed 3mo ago), licensed MIT. It adds 43 tokens to every session and 1,396 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 2026-08-31.
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