clarify_ambiguous_request

clarify_ambiguous_request is a skill for Claude Code, Codex from sfc-gh-abannerjee/SnowGram. It costs 126 tokens per session (1,153 once invoked), scanned A, original, MIT.

A skill that asks focused questions before creating a Snowflake architecture diagram when the request leaves important choices unclear. Snowflake is a cloud data platform used to store, process, and analyze data.

In plain words
What is it for?
Use it before designing diagrams for data platforms, real-time systems, machine-learning pipelines, or other Snowflake solutions with several plausible architectures.
Why use it?
It prevents the diagram from being based on an unsuitable design when details such as data size, timing, source systems, or compliance needs are missing.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it before designing diagrams for data platforms, real-time systems, machine-learning pipelines, or other Snowflake solutions with several plausible architectures.

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Install with agentmods
npx agentmods add skills/sfc-gh-abannerjee/snowgram/clarify
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 sfc-gh-abannerjee/SnowGram --skill clarify
Clone the repo
git clone --depth 1 https://github.com/sfc-gh-abannerjee/SnowGram

Made for: Claude Code, Codex.

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 clarify_ambiguous_request

README.md
[![agentmods](https://agentmods.dev/badge/skills/sfc-gh-abannerjee/snowgram/clarify.svg)](https://agentmods.dev/skills/sfc-gh-abannerjee/snowgram/clarify)
Your own site
<a href="https://agentmods.dev/skills/sfc-gh-abannerjee/snowgram/clarify"><img src="https://agentmods.dev/badge/skills/sfc-gh-abannerjee/snowgram/clarify.svg" alt="Measured on agentmods" height="20"></a>
Per session 126 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,153 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 $0.00126 $0.01153
Opus 5 $0.00063 $0.00576
Sonnet 5 $0.00025 $0.00231
Haiku 4.5 $0.00013 $0.00115

Measured 8d ago against content hash f6220a2f0037, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

clarify_ambiguous_request 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 8d 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.

backend/agent/skills/clarify/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Clarify ambiguous diagram request

When to invoke

Invoke this skill BEFORE calling any of the COMPOSE_*, SEARCH_COMPONENT_BLOCKS or VALIDATE_MERMAID_SYNTAX tools whenever the user's first message in a thread is a high-level diagram request that lacks enough detail to confidently choose between meaningfully different reference architectures.

Trigger patterns (any one is enough — be liberal, but not blocking):

  • The request names only a buzzword: "data platform", "lakehouse", "real-time thing", "ML pipeline" without sources / sinks / scale.
  • The request implies multiple plausible architectures (e.g. "ingest customer data" — could be Streaming, Batch DWH, Customer 360, or CDP).
  • The request mentions a use case but no specific Snowflake services (e.g. "fraud detection" — could be REALTIME_FINANCIAL_TRANSACTIONS or ML_FEATURE_ENGINEERING depending on volume + freshness needs).
  • The user appears new to Snowflake (first message in thread, casual phrasing).

Skip patterns (do NOT invoke):

  • The user names a template: "Streaming Data Stack", "Medallion Lakehouse", "Customer 360", any of the 14 reference architectures.
  • The user is refining an existing diagram (the conversation thread has prior assistant messages with diagrams).
  • The user provides 2+ of {source, destination, mode, scale, latency} — enough specificity to pick a template confidently.
  • The user explicitly says "just generate it" / "I don't care, pick one" / "use defaults".

Output format

When invoked, your response MUST replace the normal Sections 1-5 structure with this single-purpose response:

  1. A short opener acknowledging the request (1 sentence).
  2. A markdown bulleted list of 2-4 targeted questions. Each bullet is a bold question followed by 2-4 example answers in italics. Examples make the question scannable and lower the cognitive load.
  3. A closing line: "Once I have these, I'll generate the diagram."

DO NOT emit a mermaid block, JSON Specification, Component Summary table, or Stats line. The skill output is conversational only — the actual diagram comes on the FOLLOW-UP turn after the user answers.

Read the full file on GitHub · 107 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. 8d ago First seen · 107 lines · 126 tokens per session scan A f6220a2f0037

Subscribe to this mod's changes

clarify_ambiguous_request is a skill published in the GitHub repository sfc-gh-abannerjee/SnowGram (2 stars, last pushed 2mo ago), licensed MIT. It adds 126 tokens to every session and 1,153 once invoked, about $0.0006 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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