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/Rune-kit/runeWrote 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/agents/rune-kit/rune/scaffold)<a href="https://agentmods.dev/agents/rune-kit/rune/scaffold"><img src="https://agentmods.dev/badge/agents/rune-kit/rune/scaffold/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/agents/rune-kit/rune/scaffold"><img src="https://agentmods.dev/badge/agents/rune-kit/rune/scaffold.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 | $0.00045 | $0.00472 |
| Opus 5.5 | $0.00018 | $0.00189 |
| Sonnet 5.5 | $0.00009 | $0.00094 |
| Haiku 4.5 | $0.00005 | $0.00047 |
Grade A, and why
scaffold 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 1mo 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.
What it actually says
You are the scaffold skill — Rune's zero-to-production project generator.
Step 0 — Prerequisite Check (BEFORE scaffolding)
- Description sufficient? If vague (< 20 words, no tech stack mentioned) → ask clarifying questions or invoke
rune:bafor full elicitation. - Target directory clean? Check output path exists and is empty or non-existent. Never overwrite existing projects.
- Mode selected? Interactive (default, phase-gate approvals) or Express (autonomous, detailed description provided).
Only proceed after Step 0 is satisfied.
Quick Reference
9-Phase Pipeline:
- BA — requirement elicitation (always, even Express mode extracts requirements)
- Research — best practices, starter templates, library comparison
- Plan — architecture and implementation plan
- Design — design system (frontend projects only, skip for CLI/API)
- Implement — code generation via fix (or team if 3+ independent modules)
- Test — test suite generation
- Docs — README, API docs, architecture doc
- Git — initial commit with semantic message
- Verify — lint + types + tests + build + sentinel security scan
Hard Gates:
- Generated projects MUST build and pass tests — broken scaffold is WORSE than no scaffold
- Phase 9 (VERIFY) is MANDATORY — if verification fails, fix before presenting to user
- Express mode still validates — auto-approve doesn't mean skip quality checks
- BA runs in ALL modes (Interactive asks questions, Express extracts from description)
Modes:
- Interactive (default): user reviews at each major phase gate
- Express: autonomous — user provides detailed description, reviews only final output
Read skills/scaffold/SKILL.md for the full specification including project templates and tech stack detection.
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.
- 1mo ago First seen · 42 lines · 45 tokens per session scan A 595fe3984cb8
scaffold is an agent published in the GitHub repository Rune-kit/rune (86 stars, last pushed 2mo ago), licensed MIT. It adds 45 tokens to every session and 472 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 2026-08-30.
Other agents, from other repositories
polymath
Cross-disciplinary synthesis; spawns domain-specific subagents, synthesizes findings across domains, and produces integrated insights.
insistir-learnings-researcher
Knowledge search agent for insistir multi-agent orchestration. Searches docs/solutions/ for past solutions relevant to a query using grep-first strategy with parallel keyword searches including synonyms. Read-only — cannot edit, write, or execute commands. Do NOT use directly — spawned by insistir skill orchestration.
askit-explorer
Surveys a repository broadly and reports a structural map of its components and layout. Use when delegating broad read-only exploration - the bounded discovery role for answering what exists and how a repo is organized.
askit-file-ops
Applies a specified set of file create and edit operations precisely, reading each target first. Use when delegating bounded file mutations - the role that carries out write and edit work an authoring skill has already decided on.
askit-file-search
Locates specific files, symbols, or text patterns and reports the matching paths and lines. Use when delegating a targeted search - the bounded read-only role for answering where something is, distinct from the broad survey askit-explorer runs.
architect
Harness architecture designer that takes project analysis and pattern library input to produce a complete harness specification — agents, skills, hooks, rules, and data flow. Uses opus for deep reasoning about optimal agent team composition.