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 donald-ada/workinggenius --skill setup-working-geniusgit clone --depth 1 https://github.com/donald-ada/workinggeniusWrote 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/donald-ada/workinggenius/setup-working-genius)<a href="https://agentmods.dev/skills/donald-ada/workinggenius/setup-working-genius"><img src="https://agentmods.dev/badge/skills/donald-ada/workinggenius/setup-working-genius/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/donald-ada/workinggenius/setup-working-genius"><img src="https://agentmods.dev/badge/skills/donald-ada/workinggenius/setup-working-genius.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00043 | $0.01892 |
| Opus 5.5 | $0.00017 | $0.00757 |
| Sonnet 5.5 | $0.00009 | $0.00378 |
| Haiku 4.5 | $0.00004 | $0.00189 |
Grade A, and why
setup-working-genius 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 21d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Setup Working Genius
The workflow runs on defaults (.genius/ for work files, verify commands discovered per run, no issue mirror), so this skill is about the pointer as much as the settings: a fresh session knows none of it until an instruction file tells it. Run it once per repo, as a conversation: look first (existing CLAUDE.md/AGENTS.md, any .genius/ in flight, the task runner, a CONTEXT.md or DESIGN.md already alive), propose what you found, let the user correct each choice.
The concept: a setting no instruction file carries does not exist for the next session, so the pointer counts as much as the settings. Done when the work-file directory exists, any confirmed CONTEXT.md terms are written, and a fresh session reading only CLAUDE.md finds the ## Working Genius section through @AGENTS.md, with every setting and doc line in it confirmed by the user.
A — Work-file directory. Default .genius/ at the repo root. Recommend committing — done files are decision history every session can read. Create the directory now (a .gitkeep where it would be empty).
B — Verify commands. Propose the discovered typecheck / test / lint commands; the user corrects them. Pin each in its quiet form — pytest -q --tb=short, never -v — because a coordinator re-runs every criterion of every slice and carries each run's output in its context to the end of the work.
C — Issue tracking. Default off — the work file already carries the state. Pin Issue tracking: github when people watch progress through the tracker: Galvanizing then publishes each approved breakdown as one parent issue with a slice issue per slice, all wearing one shared working-genius label; Enablement closes slice issues as slices close, Tenacity closes the parent last. Issues are the work file's published mirror, never a second place to plan.
D — Seed the project docs. Work files are per-work memory; three homes are project memory, and setup is the one conversation guaranteed to happen before any of it. .genius/DECIDED.md needs no seeding — its lines are earned by the first decision a future stranger would re-fight (decision-record skill). The other two:
CONTEXT.md— the vocabulary. Propose the handful of terms a stranger would need decoded — domain words, never general programming ones — and let the user confirm or sharpen each; write the confirmed set in thedomain-glossaryskill's format. Where the project's records will be written in a language other than English, add the flow's own names — the glossary skill lists which — each with one rendering the user confirms. Confirmed terms only: an empty skeleton is premature documentation, and an existingCONTEXT.mdgets its## Languagesection appended, everything else left alone. A user with no patience for this declines in a word; the pointer still tells every session to grow the file as terms resolve.DESIGN.md— the visual language. Never scaffolded here: it is the output of the/designerconversation, and a template written without it is the model-default aesthetic it exists to prevent. Ask one question — does this project have an interface someone will see? — and the answer decides whether the pointer carries the DESIGN.md line.ARCHITECTURE.md— the committed architecture. The same rule: the output of/architect, never scaffolded. Where one exists, the pointer says to read it before designing across its boundaries; where the project is greenfield, the pointer routes to/architectfirst.
Three settings, and it stays three: configuration is for what a session must know and cannot infer from the repo. Preferences are said in plain words when they matter, not switches.
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.
- 21d ago Changed 5280bedaca32
- 25d ago Changed bb4a23748654
- 1mo ago Changed · +3 lines dad773d8839e
- 1mo ago Changed · -19 lines 4fc0a85149c8
- 1mo ago Changed · +20 lines 8122cfa5058c
- 1mo ago First seen · 72 lines · 43 tokens per session scan A 3bcc99cfe4ad
setup-working-genius is a skill published in the GitHub repository donald-ada/workinggenius (6 stars, last pushed 7d ago), licensed MIT. It adds 43 tokens to every session and 1,892 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
daily-working
End-to-end pipeline: pull a task from the project's task tracker by ID (Redmine or GitHub Issues today, more addable via a new adapter), sanity-check and impact-assess it against the codebase before touching anything, implement it with the Claude CLI, verify the result in a real browser via the Claude Chrome extension…
tasknotes
This skill should be used when the user asks to 'create a task', 'add a TODO', 'plan a sprint', 'organize tasks', 'track time', 'setup Kanban', 'create task from code', 'sync tasks with sprint', 'pomodoro session', 'start pomodoro', 'time estimate', or mentions TaskNotes, task management, or project planning in…
work
Dispatch a tracker ticket into an isolated worktree and drive it autonomously to an open pull request, then stop for review. Pass interactive to brainstorm the ticket with a human before planning it, or a sentence of editorial direction as a rider. Use when asked to work a ticket end-to-end.
batch
Dispatch a night's batch of Todo tickets — one worktree and one pull request each — and leave a morning summary. Run explicitly; never inferred.
prep
Spec a ticket before it is dispatched. Brainstorms it, decides everything it can recommend an answer for, asks a present human about the little that survives, and leaves the spec, the decisions and anything still open as one ticket comment. No worktree, no branch, no pull request, and the ticket stays where it was.
dw-prune
Walk .ai/backlog/ and decide every entry out loud: drop what is stale or already done, do what is cheap and unblocked, bundle what ships with a cousin, leave the rest. Explicit-invoke only.