spec
About
The `spec` skill is the exclusive mutator for a project's SPEC.md file, handling creation, amendments, and backpropagation of bugs. It triggers on commands like writing a new spec, amending specific sections (§G, §C, etc.), distilling a spec from code, or logging a bug. It follows the formatting rules defined in FORMAT.md for all writes.
Quick Install
Claude Code
Recommendednpx skills add JuliusBrussee/blueprint -a claude-code/plugin add https://github.com/JuliusBrussee/blueprintgit clone https://github.com/JuliusBrussee/blueprint.git ~/.claude/skills/specCopy and paste this command in Claude Code to install this skill
Documentation
spec — spec mutator
Read FORMAT.md at repo root if not already loaded. Caveman skill applies to all writes here.
DISPATCH
Inspect user request and project state:
- No
SPEC.mdat repo root AND args describe idea → NEW - No
SPEC.mdANDfrom-codein args → DISTILL SPEC.mdexists AND args startbug:→ BACKPROPSPEC.mdexists AND args startamend→ AMENDSPEC.mdexists, no args → ask user which mode
NEW — idea → spec
Input: user idea.
Steps:
- Extract goal (1 line, caveman). → §G.
- List constraints user stated or implied. → §C.
- List external surfaces user named. → §I.
- Propose initial invariants. → §V (numbered V1…).
- Break goal into ordered tasks. → §T pipe table, all status
., ids T1… - §B section with header row only (
id|date|cause|fix).
Write to SPEC.md. Show user full file. Ask: "spec OK? suggest edits or invoke build."
DISTILL — code → spec
Walk repo. Produce §G (infer from README/package.json/main entry), §C (infer from stack), §I (enumerate public APIs/CLIs/configs), §V (derive from tests and assertions), §T (one task per known TODO or missing test), §B (empty).
Caveman everywhere. Flag uncertain items with ? in text so user can confirm.
BACKPROP — bug → §B + §V
Input: bug: <description>.
Steps:
- Parse bug description.
- Find root cause (read relevant code).
- Decide: would a new invariant catch recurrence? If yes → draft
V<next>. - Append §B row:
B<next>|<date>|<cause>|V<N>. - Append new invariant to §V.
- If fix also changes behavior → add/update §T rows.
- Show diff. Apply only on user OK.
Rule: every bug gets a §B entry. Invariant optional but preferred.
AMEND — targeted edit
Input: amend §V.3 or amend §T etc.
Read that section. Show current. Ask user what changes. Write. Show diff.
Never silently rewrite sections user did not name.
OUTPUT RULES
- Caveman format per
FORMAT.md. - Preserve identifiers, paths, code verbatim.
- Numbering monotonic — never reuse §V.N or §B.N.
- §T row
citescolumn ! list §V/§I deps:T5|.|impl auth mw|V2,I.api.
NON-GOALS
- No sub-agents. Main thread writes.
- No dashboards, no logs, no state files beyond SPEC.md itself.
- No auto-build after spec. User invokes build explicitly.
GitHub Repository
Related Skills
content-collections
MetaThis skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.
polymarket
MetaThis skill enables developers to build applications with the Polymarket prediction markets platform, including API integration for trading and market data. It also provides real-time data streaming via WebSocket to monitor live trades and market activity. Use it for implementing trading strategies or creating tools that process live market updates.
creating-opencode-plugins
MetaThis skill helps developers create OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It provides the plugin structure, event API specifications, and implementation patterns for JavaScript/TypeScript modules. Use it when you need to intercept, monitor, or extend the OpenCode AI assistant's lifecycle with custom event-driven logic.
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
