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research

Writing & research

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Updated 10-15 21:09

About this skill

agentic-workflows-blueprint.workflow.mattpocock.research

Goal

Conduct evidence-driven technical research, dependency analysis, and isolated code spikes to eliminate technical risk and validate architectural choices.

Scope

  • Applies to: Integration spikes, third-party API research, library evaluations, performance bottlenecks, and unresolved technical risks.
  • Does not cover: Full production code implementation or user requirement discovery (use /wayfinder or /grilling).

Triggers

  • "/research"
  • "Run technical research spike"
  • "Investigate API and dependencies"
  • "Assess technical feasibility"

Inputs

  • technicalUnknowns: List of technical risks, API contracts, or unknown library behaviors.
  • domainModel: Draft domain model specification from Phase 3.
  • wayfinderBrief: Orientation brief from Phase 1.
  • techStack: Core technology stack details.

Invariants (Guardrails)

  1. Empirical Evidence First: Base findings strictly on concrete code inspection, test runs, or benchmark outputs; no guessing.
  2. Isolated Spike Execution: Run temporary research scripts or spikes in scratch/ directory without polluting production code.
  3. Security & Dependency Audit: Evaluate license, vulnerabilities, and maintenance status for any proposed third-party package.
  4. Research Report Output: Document findings, benchmarks, trade-offs, and clear implementation recommendations in research-spike.md.

Procedure

1) Extract Technical Questions & Risks

  1. Parse wayfinder.md, decision-log.md, and domain-model.md to identify unresolved technical questions, API contracts, or performance targets.
  2. Formulate explicit hypotheses to test during research.

2) Perform Code & Dependency Investigation

  1. Inspect package manifests (package.json, Gemfile, Cargo.toml, go.mod), source code, and official API documentation.
  2. Verify API rate limits, authentication protocols, payload constraints, and backward compatibility.

3) Execute Spike / Benchmarking

  1. Create isolated research scripts or spikes in <appDataDir>/scratch/ or workspace scratch/ directory (e.g. scratch/spike_api_test.ts or .py).
  2. Run benchmark tests or API integration probes to measure latency, throughput, memory footprint, or failure behavior.
  3. Capture exact output logs and performance metrics.

4) Formulate Implementation Recommendations

  1. Compare alternative approaches, detailing Pros, Cons, Performance Data, and Maintenance Overhead.
  2. Recommend the optimal technical path with code snippets and configuration flags.

5) Publish Research Spike Report

  1. Record findings, benchmark logs, and recommendations in research-spike.md or conversation context.

Outputs

  • research-spike.md: Technical research spike report detailing findings, benchmarks, API contracts, and implementation recommendations.
  • Temporary research scripts in scratch/.

Review gate

  • Technical research conducted using empirical code/log evidence.
  • Code spikes executed in scratch/ without uncommitted production churn.
  • Security and dependency risks evaluated.
  • Actionable implementation recommendation published in research-spike.md.

References

  • ../../../SKILL.md
  • ../domain-modeling/SKILL.md
  • ../prototype/SKILL.md
  • ../to-spec/SKILL.md
  • ../../embed-aihero-radioactive/SKILL.md