An interactive agent skill for spiking ideas and proposing future development directions.

Install

mkdir -p .claude/skills/gsd-spike && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/13081" && unzip -o skill.zip -d .claude/skills/gsd-spike && rm skill.zip

Installs to .claude/skills/gsd-spike

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Spike an idea through experiential exploration, or propose what to spike next (frontier mode)
93 charsno explicit “when” trigger
Advanced

Key capabilities

  • Spike ideas through experiential exploration
  • Propose integration and frontier spikes
  • Map GSD workflow tasks to Codex agents
  • Execute parallel agent fan-out
  • Parse structured agent output markers

How it works

The skill translates GSD-style workflows into Codex-compatible agent tasks. It manages the lifecycle of spikes, including decomposition, research, and verification, while handling user input via interactive prompts.

Inputs & outputs

You give it
Idea description or spike proposal
You get back
Verified knowledge and spike findings

When to use gsd-spike

  • Prototype a new feature idea
  • Explore architectural alternatives
  • Propose next steps for development

About this skill

<codex_skill_adapter>

A. Skill Invocation

  • This skill is invoked by mentioning $gsd-spike.
  • Treat all user text after $gsd-spike as {{GSD_ARGS}}.
  • If no arguments are present, treat {{GSD_ARGS}} as empty.

B. AskUserQuestion → request_user_input Mapping

GSD workflows use AskUserQuestion (Claude Code syntax). Translate to Codex request_user_input:

Parameter mapping:

  • headerheader
  • questionquestion
  • Options formatted as "Label" — description{label: "Label", description: "description"}
  • Generate id from header: lowercase, replace spaces with underscores

Batched calls:

  • AskUserQuestion([q1, q2]) → single request_user_input with multiple entries in questions[]

Multi-select workaround:

  • Codex has no multiSelect. Use sequential single-selects, or present a numbered freeform list asking the user to enter comma-separated numbers.

Execute mode fallback:

  • When request_user_input is rejected or unavailable, activate TEXT_MODE: append --text to {{GSD_ARGS}} so the workflow's built-in text-mode branching takes over. Present every AskUserQuestion call as a plain-text numbered list, then stop and wait for the user's reply. Do NOT pick a default and continue (#3018 / #3808).
  • You may only proceed without a user answer when one of these is true: (a) the invocation included an explicit non-interactive flag (--auto or --all), (b) the user has explicitly approved a specific default for this question, or (c) the workflow's documented contract says defaults are safe (e.g. autonomous lifecycle paths).
  • Do NOT write workflow artifacts (CONTEXT.md, DISCUSSION-LOG.md, PLAN.md, checkpoint files) until the user has answered the plain-text questions or one of (a)-(c) above applies. Surfacing the questions and waiting is the correct response — silently defaulting and writing artifacts is the #3018 failure mode.

C. Task() → spawn_agent Mapping

GSD workflows use Task(...) (Claude Code syntax). Translate to Codex collaboration tools:

Direct mapping:

  • Task(subagent_type="X", prompt="Y")spawn_agent(agent_type="X", message="Y")
  • Agent(subagent_type="X", prompt="Y")spawn_agent(agent_type="X", message="Y")
  • Task(model="...") → omit. spawn_agent has no inline model parameter; GSD embeds the resolved per-agent model directly into each agent's .toml at install time so model_overrides from .planning/config.json and ~/.gsd/defaults.json are honored automatically by Codex's agent router.
  • Resolved reasoning_effort="low|medium|high|xhigh" (xhigh is a GSD/Codex tier, not a generic runtime enum) → pass reasoning_effort to spawn_agent when the runtime/tool supports it. Omit missing, empty, inherited, or unsupported values; do not invent one-off effort literals in workflow prose.
  • fork_context: false by default — GSD agents load their own context via <files_to_read> blocks
  • Task(isolation="worktree") / Agent(isolation="worktree") → no direct Codex mapping. Codex spawn_agent does not create or bind a git worktree automatically. Workflows that require this isolation must fail closed or use an explicit manual worktree protocol before spawning (#3360).

Spawn restriction:

  • Codex restricts spawn_agent to cases where the user has explicitly requested sub-agents. When automatic spawning is not permitted, do the work inline in the current agent rather than attempting to force a spawn.
  • In some Codex sessions, multi-agent tooling can be deferred. If spawn_agent is not currently visible, discover tools first via tool_search before defaulting to inline execution.

Parallel fan-out:

  • Spawn multiple agents → collect agent IDs → wait(ids) for all to complete

Result parsing:

  • Look for structured markers in agent output: CHECKPOINT, PLAN COMPLETE, SUMMARY, etc.
  • close_agent(id) after collecting results from each agent </codex_skill_adapter>
<objective> Spike an idea through experiential exploration — build focused experiments to feel the pieces of a future app, validate feasibility, and produce verified knowledge for the real build. Spikes live in `.planning/spikes/` and integrate with GSD commit patterns, state tracking, and handoff workflows.

Two modes:

  • Idea mode (default) — describe an idea to spike
  • Frontier mode (no argument or "frontier") — analyzes existing spike landscape and proposes integration and frontier spikes

Does not require prior new-project setup — auto-creates .planning/spikes/ if needed. </objective>

<execution_context> @C:/Users/hiday/WebstormProjects/zephyra-agenticai-library/.codex/gsd-core/workflows/spike.md @C:/Users/hiday/WebstormProjects/zephyra-agenticai-library/.codex/gsd-core/workflows/spike-wrap-up.md @C:/Users/hiday/WebstormProjects/zephyra-agenticai-library/.codex/gsd-core/references/ui-brand.md </execution_context>

<runtime_note> Copilot (VS Code): Use vscode_askquestions wherever this workflow calls AskUserQuestion. </runtime_note>

<context> Idea: {{GSD_ARGS}}

Available flags:

  • --quick — Skip decomposition/alignment, jump straight to building. Use when you already know what to spike.
  • --text — Use plain-text numbered lists instead of AskUserQuestion (for non-the agent runtimes).
  • --wrap-up — Package spike findings into a persistent project skill for future build conversations. Runs the spike-wrap-up workflow. </context>
<process> Parse the first token of {{GSD_ARGS}}: - If it is `--wrap-up`: strip the flag, execute the spike-wrap-up workflow - Otherwise: pass all of {{GSD_ARGS}} as the idea to the spike workflow end-to-end.

Preserve all workflow gates (prior spike check, decomposition, research, risk ordering, observability assessment, verification, MANIFEST updates, commit patterns). </process>

When not to use it

  • Writing artifacts before user input
  • Forcing sub-agent spawns without request

Limitations

  • Requires manual worktree protocol for isolation
  • No direct mapping for worktree isolation

How it compares

It provides a structured, experiential approach to prototyping that integrates with existing commit patterns and state tracking, rather than relying on ad-hoc exploration.

Compared to similar skills

gsd-spike side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
gsd-spike (this skill)02moNo flagsAdvanced
atlas-grounding028dNo flagsIntermediate
fable01moReviewAdvanced
deep-research359moReviewAdvanced

Try saying

Example prompts that trigger this skill in your AI assistant.

You might also like

atlas-grounding

ethosengine

Use when starting a planning or brainstorm pass on a feature or subsystem (or grounding external prior-art) and you need current ground-truth across the architecture's relevant seams before designing — "we're exploring X, ground my understanding", "map the seams for X", or as the grounding step befo

00

fable

tdimino

Summon Claude Fable 5 (Mythos-class, xenos daimon) for long-horizon tasks. Handles model override, cost awareness, and task routing. Availability probed at invocation.

00

deep-research

davidorex

Multi-agent parallel investigation for complex VCV Rack problems

35163

scientific-brainstorming

davila7

Research ideation partner. Generate hypotheses, explore interdisciplinary connections, challenge assumptions, develop methodologies, identify research gaps, for creative scientific problem-solving.

37155

nlm-skill

jacob-bd

Expert guide for the NotebookLM CLI (`nlm`) and MCP server - interfaces for Google NotebookLM. Use this skill when users want to interact with NotebookLM programmatically, including: creating/managing notebooks, adding sources (URLs, YouTube, text, Google Drive), generating content (podcasts, reports, quizzes, flashcards, mind maps, slides, infographics, videos, data tables), conducting research, chatting with sources, or automating NotebookLM workflows. Triggers on mentions of "nlm", "notebooklm", "notebook lm", "podcast generation", "audio overview", or any NotebookLM-related automation task.

895

gpt-researcher

assafelovic

GPT Researcher is an autonomous deep research agent that conducts web and local research, producing detailed reports with citations. Use this skill when helping developers understand, extend, debug, or integrate with GPT Researcher - including adding features, understanding the architecture, working with the API, customizing research workflows, adding new retrievers, integrating MCP data sources, or troubleshooting research pipelines.

1464

Search skills

Search the agent skills registry