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 vignesh2027/AI-AGENT-SKILLS --skill idea-refinegit clone --depth 1 https://github.com/vignesh2027/AI-AGENT-SKILLSWrote 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/vignesh2027/ai-agent-skills/idea-refine)<a href="https://agentmods.dev/skills/vignesh2027/ai-agent-skills/idea-refine"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/idea-refine/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/vignesh2027/ai-agent-skills/idea-refine"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/idea-refine.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 | $0.00016 | $0.00548 |
| Opus 5 | $0.00008 | $0.00274 |
| Sonnet 5 | $0.00003 | $0.00110 |
| Haiku 4.5 | $0.00002 | $0.00055 |
Grade A, and why
idea-refine 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 12d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Most ideas arrive as fuzzy intuitions. This skill converts them into crisp, buildable proposals with clear scope, constraints, and success criteria — before anyone writes a spec or a line of code.
When to Use
- Before writing a spec for something you've only talked about
- When a request feels vague or underspecified
- When you're unsure if you're solving the right problem
- Before a technical design discussion
Process
Step 1: State the problem, not the solution
Write one sentence describing the problem being solved. Not the feature — the problem. Example: "Users can't find past orders because search only covers the last 30 days."
Step 2: Identify who has the problem
Name the specific user persona or system component affected. Vague problems have vague solutions.
Step 3: Measure the current pain
Quantify where possible: "affects 20% of active users," "adds 3 minutes to the workflow," "causes 12 support tickets/week." If you can't measure it, question whether it's a real problem.
Step 4: List candidate solutions
Write 3 different ways to solve the problem at different points on the effort/impact curve. This prevents anchoring on the first idea.
Step 5: Score and select
For each solution: estimate effort (S/M/L), impact (low/medium/high), and risk (low/medium/high). Select the option with the best ratio for the current context.
Step 6: Define the out-of-scope boundary
Explicitly state what this proposal does NOT include. Scope creep starts here if you don't.
Step 7: State the success metric
One measurable outcome that proves the problem is solved. Not "users like it" — "search result relevance score improves by 15% on the benchmark dataset."
Anti-Rationalizations
"We know what we want to build — let's just build it" The thing you want to build is a solution. Before committing to a solution, confirm you've correctly understood the problem.
"We don't have data on this yet" Absence of data is a finding. Document your assumptions and validate them in the first iteration.
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.
- 12d ago First seen · 57 lines · 16 tokens per session scan A 1ccb05c8b2fc
idea-refine is a skill published in the GitHub repository vignesh2027/AI-AGENT-SKILLS (1 stars, last pushed 14d ago), licensed MIT. It adds 16 tokens to every session and 548 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 2026-08-31.
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