spark

A design-first conversation mode for turning a requested feature or behavior change into a clear implementation plan.

In plain words
What is it for?
Use it to inspect an existing project, explore possible solutions, compare design choices, and prepare a formal design for approval.
Why use it?
It reduces the risk of building the wrong thing by clarifying intent, requirements, project structure, and design before code is written.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/devnomad-byte/techneering/spark
Any agent
npx skills add devnomad-byte/techneering --skill spark
Clone the repo
git clone --depth 1 https://github.com/devnomad-byte/techneering

Made for: Claude Code, Codex.

Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,613 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00035 $0.01613
Opus 5 $0.00017 $0.00807
Sonnet 5 $0.00007 $0.00323
Haiku 4.5 $0.00003 $0.00161

Measured 2d ago against content hash 5a871d9a1a01, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

spark 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 2d 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.

skills/spark/SKILL.md · 157 lines

How it starts

The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Sparking Ideas Into Designs

Help turn ideas into fully formed designs through natural collaborative dialogue. Explore intent, refine requirements, compare approaches — then hand off to draft for formal documentation.

Core Principle

NO IMPLEMENTATION WITHOUT A DESIGN.
Present a design and get user approval before writing any code.

Steps

Step 1: Explore Project Context

Check files, docs, recent commits. Understand the current state before asking questions.

For existing projects (code already exists):

1a. Project Structure Analysis
  • Scan directory structure, identify layering pattern (MVC / layered / feature-based / modular / monorepo)
  • Identify file/directory naming convention (kebab-case / PascalCase / camelCase / snake_case)
1b. Code Pattern Analysis

Sample 3-5 source files and identify:

  • Naming conventions: files, classes, functions, constants, variables (camelCase / snake_case / PascalCase / UPPER_SNAKE)
  • Error handling: exceptions / Result type / error codes / try-catch
  • Dependency injection: constructor / annotations / manual / module system
  • API style: REST / GraphQL / gRPC (if applicable)
  • Data access: ORM / query-builder / raw-sql / repository pattern
  • Import style: alias (@/) / relative / absolute / package-based
1c. Formatting Style Analysis
  • Check for config files: .editorconfig, .prettierrc, .eslintrc, biome.json
  • If no config found, infer from code: indent (tab/space, 2/4), quotes (single/double), semicolons, trailing commas
1d. Summarize Conventions

Produce a coding_conventions summary as part of spark output. This will be written to techneering/config.yaml by tn:draft.

1e. Tech Stack Detection & Quality Standards Recommendation

Detect project tech stack from build files and recommend matching quality standards:

Detected File Tech Stack Recommended Source
pom.xml / build.gradle Java ali-taishan-java
requirements.txt / pyproject.toml Python ali-taishan-python + pep8
go.mod Go gofmt
package.json + vue Vue vue-official
package.json + react React react-airbnb
package.json + typescript TypeScript google-js
package.json (other) JavaScript prompt user to choose

Read the full file on GitHub · 157 lines

Changes

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.

  1. 2d ago First seen · 157 lines · 35 tokens per session scan A 5a871d9a1a01

Subscribe to this mod's changes

spark is a skill published in the GitHub repository devnomad-byte/techneering (13 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 1,613 once invoked, about $0.0002 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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