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 tonyfadel23/skill-inspector --skill goal-to-prototypegit clone --depth 1 https://github.com/tonyfadel23/skill-inspectorWrote 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/tonyfadel23/skill-inspector/goal-to-prototype)<a href="https://agentmods.dev/skills/tonyfadel23/skill-inspector/goal-to-prototype"><img src="https://agentmods.dev/badge/skills/tonyfadel23/skill-inspector/goal-to-prototype/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/tonyfadel23/skill-inspector/goal-to-prototype"><img src="https://agentmods.dev/badge/skills/tonyfadel23/skill-inspector/goal-to-prototype.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 | $0.00060 | $0.01629 |
| Opus 5 | $0.00030 | $0.00814 |
| Sonnet 5 | $0.00012 | $0.00326 |
| Haiku 4.5 | $0.00006 | $0.00163 |
Grade A, and why
goal-to-prototype 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 10d 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 — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
goal-to-prototype
Step 0 — Context Loading
Load user personas and company profile
Read and load the following context files:
- Load
references/personas.md - Load
references/company-profile.md
Load market landscape and competitive intel
Read and load the following context files:
- Load
references/market-landscape.md
Also fetch data from:
- Fetch
https://api.example.com/market-data
Parse the user's goal statement into structured requirements: target audience, problem space, desired outcome, success metrics, and constraints
Parse the user's goal statement into structured requirements: target audience, problem space, desired outcome, success metrics, and constraints
Step 1 — Data Acquisition
Launch parallel research agents to gather data simultaneously (parallel)
Run the following tasks simultaneously in parallel:
Web Research Agent
Search the web for recent articles, papers, and discussions related to the problem space. Fetch at least 10 high-quality sources. Use WebSearch and WebFetch tools.
Competitor Analysis Agent
Identify top 5-10 competitors in the space. For each, analyze: product offering, pricing, strengths, weaknesses, market share. Use WebSearch to find competitor data.
User Research Agent
Cross-reference the loaded personas against the problem space. Identify pain points, unmet needs, willingness to pay, and adoption barriers. Load references/personas.md for context.
Market Trend Agent
Analyze market trends, growth trajectories, regulatory changes, and technology shifts that could impact the opportunity. Use WebSearch for current data.
Step 2 — Signal Evaluation
Synthesize all research outputs into a unified intelligence brief
Read all outputs and synthesize them with weighted importance.
Compute signal quality metrics from the merged research:
- market_signal: strength of market opportunity (0-1)
- competitive_gap: size of unserved niche (0-1)
- user_demand: evidence of user pull (0-1)
- feasibility: technical buildability (0-1)
- confidence: data quality and coverage (0-1)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 227 lines · 60 tokens per session scan A 0896bd6a20f0
goal-to-prototype is a skill published in the GitHub repository tonyfadel23/skill-inspector (2 stars, last pushed 5mo ago), licensed MIT. It adds 60 tokens to every session and 1,629 once invoked, about $0.0003 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.
Other skills, from other repositories
results-analysis
This skill should be used when the user asks to "analyze experimental results", "run strict statistical analysis", "compare model performance", "generate scientific figures", "check significance", "do ablation analysis", or mentions interpreting experiment data with rigorous statistics and visualization. It focuses on…
semiotic-charts
Build, repair, and verify charts in an existing Semiotic project, when Semiotic is explicitly requested, or when evaluating its documented capabilities against a visualization task. Preserve the project's dependency and runtime constraints; routine changes in another charting stack and tasks without a chart do not…
architecture-diagram
Dark-themed SVG architecture/cloud/infra diagrams as HTML.
experiment-tracking-swanlab
Provides guidance for experiment tracking with SwanLab. Use when you need open-source run tracking, local or self-hosted dashboards, and lightweight media logging for ML workflows.
data_analyst
Analyses datasets with professional rigour — statistical summaries, clear narratives, and well-chosen visualisations.
iflytek-text-proofread
A tool that checks Chinese writing through iFlytek’s official document-proofreading service. It looks for spelling, punctuation, wording, factual, terminology, and sensitive-content problems.