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.
git clone --depth 1 https://github.com/arrrrny/zuraffaWrote 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/commands/arrrrny/zuraffa/speckit.chore.assess)<a href="https://agentmods.dev/commands/arrrrny/zuraffa/speckit.chore.assess"><img src="https://agentmods.dev/badge/commands/arrrrny/zuraffa/speckit.chore.assess/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/commands/arrrrny/zuraffa/speckit.chore.assess"><img src="https://agentmods.dev/badge/commands/arrrrny/zuraffa/speckit.chore.assess.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 | $0.00017 | $0.03138 |
| Opus 5 | $0.00009 | $0.01569 |
| Sonnet 5 | $0.00003 | $0.00628 |
| Haiku 4.5 | $0.00002 | $0.00314 |
Grade C, and why
speckit.chore.assess scanned grade C with 1 finding 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.
Cloud metadata endpointhighServer-side request forgery
One request to 169.254.169.254 can return temporary IAM credentials.
- Cloud instance metadata endpoints: `169.254.169.254`, `metadata.google.internal`, `100.100.100.200`, `metadata.azure.com`. How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Assess Chore
Scope a maintenance chore against the current codebase and the project
constitution: understand what needs to change, locate the affected paths, judge
scope and risk, and propose an approach. The output is a single assessment file at
.specify/chores/<slug>/assessment.md that downstream commands
(__SPECKIT_COMMAND_CHORE_IMPLEMENT__, __SPECKIT_COMMAND_CHORE_PR__) consume.
A chore is work that is neither a bug (something broken) nor a feature (new user-facing capability): refactors, dependency bumps, asset swaps, config cleanups, tooling changes, logo/branding updates, and similar maintenance. It lives in the same Spec Kit ecosystem as features and bugs — it is scoped, tracked, and recorded — but it is classified on its own so triage and reporting stay honest.
User Input
$ARGUMENTS
The user input contains the chore description and (optionally) a slug. Treat it as one of:
- Pasted text — a copy of an issue, a chat message, a bullet, or a freeform description.
- A URL — a link to a GitHub/GitLab issue, a discussion, a design doc, or any web page describing the chore. Fetch and read the page content before proceeding.
- A mix — text plus a URL for additional context.
- An
issueflag —issue/--issue(orissue=true/issue=false). When present and truthy, this command also files a GitHub issue for the chore after writing the assessment (the "report" phase). See Optional — file the GitHub issue below.
If both a URL and text are present, fetch the URL and merge its content with the pasted text when forming the chore summary.
Slug Resolution
Each chore gets its own directory under .specify/chores/<slug>/. Resolve the slug in this order:
- User-provided slug: If the user explicitly passes a slug (e.g.,
slug=logo-swap,--slug logo-swap, or just an obvious slug-like token), use it verbatim after normalization (lowercase, hyphen-separated, no spaces, no special characters other than-and digits). Preserve the shape the user asked for — do not append timestamps or numbers. - Interactive mode (a human is driving): If no slug was provided, ask the user for one and wait for the answer before continuing. Suggest a 2–4 word kebab-case candidate derived from the chore summary as a default.
- Automated / non-interactive mode (no human to ask): Generate a concise slug yourself from the chore summary (2–4 kebab-case words, e.g.
logo-swap). The generated slug MUST produce a unique directory — if.specify/chores/<slug>/already exists, append the shortest disambiguating suffix needed (-2,-3, …) or a short ISO-style date (-20260605) to make it unique. Never overwrite an existing chore directory.
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 · 198 lines · 17 tokens per session scan C e7a7251151a3
speckit.chore.assess is a command published in the GitHub repository arrrrny/zuraffa (5 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 3,138 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 1 finding (cloud metadata endpoint). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
building-flutter-apps-skill-ac1294fc
Flutter Riverpod app architecture and Windows installer delivery. Use before changing a Riverpod Flutter app/package or its Windows desktop packaging/update pipeline; skip non-Riverpod stacks and pure-Dart work.
setup
Check whether the local Pi CLI is ready, show available models, and optionally toggle the stop-time review gate.
session-end
I'll summarize this coding session and update the memory system with our accomplishments.
scout-scan
Scan a path with Scout and walk through the security findings.
init
Birth or wake your homunculus.
checklist
Generate a custom checklist for the current feature based on user requirements.