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 agentmods add skills/glebis/humane-agentic-design/walkthroughnpx skills add glebis/humane-agentic-design --skill walkthroughgit clone --depth 1 https://github.com/glebis/humane-agentic-designWrote 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/glebis/humane-agentic-design/walkthrough)<a href="https://agentmods.dev/skills/glebis/humane-agentic-design/walkthrough"><img src="https://agentmods.dev/badge/skills/glebis/humane-agentic-design/walkthrough.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 | $0.00164 | $0.02691 |
| Opus 5 | $0.00082 | $0.01345 |
| Sonnet 5 | $0.00033 | $0.00538 |
| Haiku 4.5 | $0.00016 | $0.00269 |
Grade A, and why
walkthrough 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 6d 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 — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Walkthrough
Announce at start: "I'm using the humane:walkthrough skill to attempt this task as the person who has it."
Inspection asks whether an interface obeys principles. Reaction asks how words land. Neither asks the question that decides whether the product works: can this person, with this goal, get through?
A walkthrough answers that by attempting the task — one concrete task, one concrete person, one step at a time — and recording exactly where the attempt would break.
When to invoke — and when not
Reach for this when you have a task and something to attempt it on.
| You want | Skill |
|---|---|
| Can someone complete this task? | this skill |
| Does the interface violate usability principles? | nielsen-heuristics |
| How does this copy land with strangers? | respondent-panel |
| Would an expert stakeholder object to this document? | persona-review |
| Did the change actually improve things? | before-after (feed it these results) |
Do not run this on a spec or a description. A walkthrough needs something to
operate. If all you have is a document, nielsen-heuristics design-risk mode
is the honest substitute — say so and switch.
Step 1 — Derive the task from the corpus, not from the interface
The most common way this method fails is walking the flow the designer built instead of the job the person has. Guard against it by taking the task from outside the interface.
- Read
<corpus_root>/<slug>/jtbd.json—corpus_rootis thesetupsetting, default~/jtbd; read the configured value, not the default. Prefer a task derived from anodi.outcomes[]entry — those already carry astage(one of define/locate/prepare/confirm/execute/monitor/modify/conclude) and atouch(the surface it lives on). An underserved outcome (high importance, low satisfaction) is the highest-value thing you can walk. - Write the task as the person's goal, in their words, with no interface nouns in it. "Find out what I spent on models last week" — not "open the billing dashboard and apply a date filter." If your task statement names a button, you have already assumed the answer.
- Name the actor and their entry state. Who they are, what they already
know, what they have already done, where they arrive from, on what device.
Use the corpus
actorandjtbd.situation. First-time and returning users walk different paths through the same screens — pick one and say which. - State the success condition — the observable thing that is true when the job is done. This is what "task success" is measured against, and it must be decided before the walk.
What ships with it
2 files 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.
- 6d ago First seen · 228 lines · 164 tokens per session scan A 43f7686c0739
walkthrough is a skill published in the GitHub repository glebis/humane-agentic-design (27 stars, last pushed 4d ago), licensed MIT. It adds 164 tokens to every session and 2,691 once invoked, about $0.0008 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 2026-08-30.
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