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 KhaledSaeed18/dotclaude --skill design-science-methodologygit clone --depth 1 https://github.com/KhaledSaeed18/dotclaudeWrote 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/khaledsaeed18/dotclaude/design-science-methodology)<a href="https://agentmods.dev/skills/khaledsaeed18/dotclaude/design-science-methodology"><img src="https://agentmods.dev/badge/skills/khaledsaeed18/dotclaude/design-science-methodology/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/khaledsaeed18/dotclaude/design-science-methodology"><img src="https://agentmods.dev/badge/skills/khaledsaeed18/dotclaude/design-science-methodology.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 | $0.00112 | $0.01087 |
| Opus 5.5 | $0.00045 | $0.00435 |
| Sonnet 5.5 | $0.00022 | $0.00217 |
| Haiku 4.5 | $0.00011 | $0.00109 |
Grade A, and why
design-science-methodology 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 25d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building a system is engineering; showing what is learned from building it is research. Design science research (DSR) is the frame that turns the first into the second: an artifact designed to solve a stated problem, evaluated with rigour, and a contribution to knowledge stated separately from the artifact itself.
The process (Peffers et al.)
- Problem identification and motivation: the problem, who has it, why existing solutions fall short (from
lit-review-synthesis), and why solving it matters. Concrete and, where possible, quantified. - Objectives of the solution: requirements the artifact must meet, derived from the problem and the literature. Functional (must do X) and quality (latency under Y, usable by Z without training). Each requirement is later a row in the evaluation table. Number them (R1..Rn).
- Design and development: the artifact, with its design decisions recorded as they are made (
adr-writingworks for this) and the knowledge they draw on (which theories, patterns, prior systems). The thesis describes architecture and the decisions, not every line. - Demonstration: the artifact used to solve an instance of the problem (a worked example, a pilot). Shows feasibility.
- Evaluation: how well the artifact meets the objectives, with a method chosen from the strategy below. This is the chapter examiners read hardest.
- Communication: the thesis, and the artifact released with documentation (
experiment-reproducibility).
Iterate: evaluation feeds back into design. Record each cycle; a thesis that shows two design iterations with evidence is stronger than one that presents a finished system.
Evaluation strategy
Choose along two axes (Venable, Pries-Heje, Baskerville): artificial or naturalistic setting, and formative (during design) or summative (final). Combine at least two of:
| Method | Answers | Setting |
|---|---|---|
| Technical experiment / benchmark | Does it perform (speed, accuracy, scale) against baselines? | artificial |
| Controlled experiment with users | Does it help people do the task better than the alternative? | artificial |
| Case study in a real setting | Does it work in practice, and what happens around it? | naturalistic |
| Expert evaluation / walkthrough | Do practitioners judge it sound and useful? | either |
| Illustrative scenario | Can it handle the representative cases? (weak alone) | artificial |
| Analytical (complexity, formal properties) | Does it have the guaranteed properties claimed? | artificial |
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.
- 25d ago First seen · 60 lines · 112 tokens per session scan A e043d827e950
design-science-methodology is a skill published in the GitHub repository KhaledSaeed18/dotclaude (5 stars, last pushed 2d ago), licensed MIT. It adds 112 tokens to every session and 1,087 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-22.
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