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 aget-framework/template-advisor-aget --skill aget-askgit clone --depth 1 https://github.com/aget-framework/template-advisor-agetWrote 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/aget-framework/template-advisor-aget/aget-ask)<a href="https://agentmods.dev/skills/aget-framework/template-advisor-aget/aget-ask"><img src="https://agentmods.dev/badge/skills/aget-framework/template-advisor-aget/aget-ask/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/aget-framework/template-advisor-aget/aget-ask"><img src="https://agentmods.dev/badge/skills/aget-framework/template-advisor-aget/aget-ask.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 | $0.00062 | $0.02149 |
| Opus 5.5 | $0.00025 | $0.00860 |
| Sonnet 5.5 | $0.00012 | $0.00430 |
| Haiku 4.5 | $0.00006 | $0.00215 |
Grade A, and why
aget-ask 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.
This is a copy
95% identical to aget-ask — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
aget-ask
Ask clarifying questions as a measurement instrument for next-action prediction. Default: --clarification.
Instructions
This skill formalizes asking clarifying questions as an entropy-reduction tool, not UX politeness. Quality is measured by pre/post confidence delta on the agent's top-1 interpretation of the principal's intent — not by thoroughness, count, or category coverage.
Clarification Mode (default: --clarification)
Generate 2-4 clarifying questions that maximally narrow the prediction distribution over what the principal wants next.
Execution:
-
Pre-ask measurement: Record the agent's top-1 interpretation of the principal's intent + confidence (0.0-1.0).
-
Entropy threshold check: If confidence ≥ 0.70, proceed with best-guess + verify instead of asking. If < 0.70, continue.
-
Generate candidates: Produce 4-8 candidate questions covering:
- Frame-choice (which model/framing applies)
- Replace-or-layer (affects which existing artifacts get modified)
- Scope boundary (what's in/out for this turn)
- Decision type (commitment level)
-
Filter decorative / context-answerable:
- Drop questions answerable from memory or KB context
- Drop wordsmithing between near-equivalent options
- Drop questions self-labeled "deferrable" in the draft
-
Rank by load-bearing weight: Estimate how much each question's answer would change the next action. Higher = ranked earlier.
-
Cap at 2-4: Top 2-4 questions only. If the problem genuinely needs 5+, the input had structural ambiguity — consider re-scoping the request instead of adding questions.
-
Order strictly by descending load-bearing weight, NOT by category altitude. If your draft contains phrases like "hiding at the bottom", "biggest shape-determining", or "flag for alignment" attached to a non-first question, reorder before presenting.
-
Present: Numbered questions with 2-3 concrete option examples per question to make answering cheap.
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 Changed · +5 lines · +62 tokens per session 1472bf0845bb
- 1mo ago First seen · 160 lines · 0 tokens per session scan A f3c6e1f4909a
aget-ask is a skill published in the GitHub repository aget-framework/template-advisor-aget (2 stars, last pushed 8d ago), licensed Apache-2.0. It adds 62 tokens to every session and 2,149 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to aget-ask, differing in 2 lines, and is treated as a copy.
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