Develop Spike Summary
Documents the results of a time-boxed technical or design exploration (spike). Use after completing a spike to capture learnings, findings, and recommendations for the team.
MCP get_skill({ skillId: "spike-summary-f7ac82c8" })Use this skill with your agent
Create a free account and connect via MCP
<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 --> # Spike Summary A spike summary documents the results of a time-boxed exploration - a focused investigation to reduce uncertainty before committing to implementation. Spikes answer specific questions like "Can we integrate with this API?" or "Is this technology viable for our use case?" The summary captures findings so the team can make informed decisions without the spike participants needing to repeat explanations. ## When to Use - After completing a time-boxed technical exploration - When evaluating technology choices or vendor options - After proof-of-concept work that needs to inform team decisions - When investigating feasibility of a proposed solution - Before committing engineering resources to a new approach ## When NOT to Use - You are recording the resulting architecture decision itself -> use `develop-adr`; the spike informs, the ADR decides - The exploration was user research, not technical or design feasibility -> use `discover-interview-synthesis` - You want to propose the solution the spike pointed to -> use `develop-solution-brief` - The spike has not happened yet: this skill documents results; time-box and run the exploration first ## Instructions When asked to document a spike, follow these steps: 1. **State the Question Clearly** Articulate the specific question the spike was designed to answer. Good spike questions are focused and answerable with the time-box available. If the question evolved during the spike, document both the original and final versions. 2. **Define the Time-Box** Document the time allocated (e.g., 3 days) and actual time spent. If the spike exceeded its time-box, explain why and note any remaining work. 3. **Describe the Approach** Explain what was tried, in what order, and why. This helps future readers understand the methodology and whether alternative approaches were considered. 4. **Present Findings with Evidence** Document what was learned, supported by concrete evidence - code samples, performance benchmarks, screenshots, or API responses. Distinguish between verified findings and hypotheses that need more testing. 5. **Make a Clear Recommendation** Answer the original question directly: proceed, do not proceed, or proceed with conditions. Avoid hedging - the team needs actionable guidance. 6. **Document Artifacts** Link to any code, prototypes, diagrams, or documentation created during the spike. These artifacts often have ongoing value beyond the summary. 7. **Capture Open Questions** Note what the spike didn't answer and what additional investigation might be needed. ## Output Format Use the template in `references/TEMPLATE.md` to structure the output. A complete spike summary fills every template section: Overview; Background; Approach; Findings; Recommendation; Artifacts; Open Questions; and Follow-up Items. ## Quality Checklist Before finalizing, verify: - [ ] Original question is clearly stated - [ ] Time-box is documented (allocated vs. actual) - [ ] Findings are supported by evidence, not just opinions - [ ] Recommendation directly answers the question - [ ] Artifacts (code, diagrams) are linked or attached - [ ] Open questions identify remaining unknowns ## Examples See `references/EXAMPLE.md` for a completed example.
Related Skills
More skills in Software Engineering
Accessibility Standards
Comprehensive web accessibility standards based on WCAG 2.2 AA, with 38+ anti-patterns, legal enforcement context (EAA, ADA Title II), WAI-ARIA patterns, and framework-specific fixes for modern web frameworks and libraries.
Accord
Authoring unified specification packages across Business/Development/Design teams via staged elaboration (L0 Vision → L1 Requirements → L2 Team Detail → L3 Acceptance Criteria). No code. Use when authoring cross-team specs, building L0-L3 packages, or aligning Biz/Dev/Design on a single source of truth.
Acquire Codebase Knowledge
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
Acreadiness Assess
Run the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.
Acreadiness Generate Instructions
Generate tailored AI agent instruction files via AgentRC instructions command. Produces .github/copilot-instructions.md (default, recommended for Copilot in VS Code) plus optional per-area .instructions.md files with applyTo globs for monorepos. Use after running /acreadiness-assess to close gaps in the AI Tooling pillar.
Acreadiness Policy
Help the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation.
Explore Other Categories
Skills from other categories with shared topics
Define Hypothesis
Defines a testable hypothesis with clear success metrics and a validation approach. Use when forming assumptions to test or aligning a team on what success looks like, before any experiment is designed. To design the A/B test or experiment that will validate the hypothesis, use measure-experiment-design.
Define Jtbd Canvas
Creates a Jobs to be Done canvas capturing the functional, emotional, and social dimensions of a customer job. Use when deeply understanding customer motivations, designing for jobs, or reframing product positioning.
Define Opportunity Tree
Creates an opportunity solution tree mapping desired outcomes to opportunities and potential solutions. Use for outcome-driven product discovery, prioritization, or communicating product strategy.