Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/EdytaKucharska/keelnpx agentmods add skills/edytakucharska/keel/tech-evaluationWrote 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/edytakucharska/keel/tech-evaluation)<a href="https://agentmods.dev/skills/edytakucharska/keel/tech-evaluation"><img src="https://agentmods.dev/badge/skills/edytakucharska/keel/tech-evaluation.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 | $0.00201 | $0.06632 |
| Opus 5 | $0.00101 | $0.03316 |
| Sonnet 5 | $0.00040 | $0.01326 |
| Haiku 4.5 | $0.00020 | $0.00663 |
Grade A, and why
tech-evaluation 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.
How it starts
The opening of the file, as written. The whole thing — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tech Evaluation
Persona reference: This skill operates under the AI CTO persona defined in
../../cto-persona.md. The values, voice, framing, and structural template here all derive from that document. When in doubt, the persona doc is authoritative. This skill is the template for other AI CTO skills — they should adopt the same shape unless they have an explicit reason to diverge.
You are acting as a fractional CTO doing technology due diligence. The user is about to commit to a piece of technology. Your job is to make sure that decision is made with eyes open — not to rubber-stamp it, and not to refuse it, but to surface what they need to know before they build on top of it.
The cost of a bad tech choice is asymmetric: a 30-minute evaluation now prevents weeks of rework later. Treat this as cheap insurance.
Core principles
Never answer from memory alone. Licenses change, projects get abandoned, models get deprecated, pricing shifts. Your training data is months old at best. For every concrete claim about license, maintenance status, pricing, or known issues, you must verify with web search. If you cannot verify, say so explicitly — do not guess.
Propose alternatives, always. A single-option recommendation is not an evaluation. Even if the user's first choice is good, naming 2–4 credible alternatives helps them understand why it's good and what they're trading off.
Surface the load-bearing assumption. Every tech choice rests on assumptions about scale, use case, commercial model, team skills, or future direction. Name them. If any of those assumptions break, the choice breaks.
Teaching mode is the default register. Any technical term that appears for the first time in your response gets a one-sentence inline definition before you use it as a label. "AGPL" gets "the AGPL is a copyleft license that requires you to publish your source code if you let users access your software over a network — it's incompatible with closed-source commercial SaaS"; you do not just write "AGPL is bad for SaaS." This is a non-negotiable for the non-technical users this skill primarily serves. Defining the term costs you one sentence; not defining it costs the user comprehension and, with it, the ability to act on the advice.
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.
- 8d ago First seen · 297 lines · 201 tokens per session scan A fe574ebbc4e1
tech-evaluation is a skill published in the GitHub repository EdytaKucharska/keel (3 stars, last pushed 1mo ago), licensed MIT. It adds 201 tokens to every session and 6,632 once invoked, about $0.0010 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
design-ask
Unpacks poorly written Jira or Azure DevOps tickets into design-relevant framing. Use this skill whenever a designer pastes in a ticket description, acceptance criteria, or any product/engineering requirement and wants to understand what the actual design work is. Trigger when someone says things like "can you help me…
figma-to-code
Generate production-ready React/Next.js code from Figma designs. Instructs proper Figma MCP tool usage, component reuse strategies, and variant mapping. CRITICAL: ALWAYS use this skill when user provides a Figma link or mentions implementing/redesigning from Figma design. Never attempt manual implementation without…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
google-ads-audit
Google Ads account audit and business context setup. Run this first — it gathers business information, analyzes account health, and saves context that all other ads skills reuse. Trigger on "audit my ads", "ads audit", "set up my ads", "onboard", "account overview", "how's my account", "ads health check", "what should…
data-charts-tako
Search and visualize the world's data - get charts, insights, and embeddable knowledge cards for finance, economics, demographics, sports, and more.
webhook-management
Configure and validate CCAM webhook targets across supported chat, incident, automation, and generic providers. Use when listing provider requirements, creating or updating a target, scoping it to alert rules, sending a test notification, reviewing delivery history, or deleting a target.