hackathon-code-implementer

Goal

Provide structured implementation guidance for a hackathon project task, including code patterns, integration strategies, and shortcuts appropriate for prototype speed.

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Trigger Conditions

Use this skill when:

• A specific task from hackathon-task-planner is about to be implemented
• Implementation guidance, code scaffolds, or shortcut patterns are needed
• A developer is blocked and needs a concrete starting point
• The task budget needs to be assessed for [HIGH-RISK] overrun potential
• Invoked once per task; iterate through the full task list from hackathon-task-planner

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Inputs

| Input | Type | Required | Description | |---|---|---|---| | task_title | string | Yes | Task being implemented (from hackathon-task-planner) | | task_description | string | Yes | Detailed description of what the task must achieve | | tech_stack | string[] | Yes | Technologies in use | | mvp_demo_flow | object[] | Yes | Demo flow steps from hackathon-scope-cutter | | time_budget_hours | number | Yes | Hours allocated to this task | | existing_code_context | string | No | Relevant existing code snippets or file structure | | fake_ok | boolean | No | Whether hardcoded/simulated data is acceptable (default: false) |

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Outputs

| Output | Description | |---|---| | implementation_plan | Ordered list of sub-steps to complete the task | | code_scaffolds | Key code snippets, patterns, or stubs to start from | | integration_points | Where this task connects to other components | | shortcuts | Hackathon-appropriate shortcuts (mocks, hardcoding, libraries) | | gotchas | Common failure modes to avoid | | done_criteria | Conditions that signal the task is complete |

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Rules

1. Prioritize working code over clean code; note tech debt explicitly.
2. Recommend existing libraries over custom implementations whenever possible.
3. If fake_ok is true, provide mock/stub patterns alongside real implementations.
4. Keep code_scaffolds minimal — entry points only, not full implementations.
5. done_criteria must be observable and verifiable within time_budget_hours.
6. Flag any sub-step that risks taking longer than 50% of time_budget_hours as [HIGH-RISK].
7. Do not generate production-quality architecture; optimize for demo completeness.

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Output Format

implementation_plan:
  - step: <number>
    action: "<what to do>"
    risk: "<[HIGH-RISK]|normal>"

code_scaffolds:
  - label: "<purpose>"
    language: "<language>"
    snippet: |
      <code>

integration_points:
  - component: "<name>"
    connection: "<how this task connects>"

shortcuts:
  - shortcut: "<description>"
    trade_off: "<what is sacrificed>"

gotchas:
  - "<pitfall>"

done_criteria:
  - "<verifiable condition>"

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Example

Input:

task_title: "Implement session memory read/write (T-03)"
task_description: "Store and retrieve a rolling summary of the user's emotional state across chat sessions using Redis."
tech_stack: ["Python", "FastAPI", "Redis", "OpenAI API"]
mvp_demo_flow:
  - step: 2
    action: "User describes recurring work stress"
    outcome: "AI references last week's similar conversation from memory"
time_budget_hours: 3
fake_ok: false

Output:

implementation_plan:
  - step: 1
    action: "Install redis-py; add Redis client singleton to app startup"
    risk: "normal"
  - step: 2
    action: "After each GPT-4 response, call GPT-4 to generate a 2-sentence session summary and write to Redis key user:{id}:summary"
    risk: "normal"
  - step: 3
    action: "On each new conversation, prepend the stored summary to the system prompt"
    risk: "[HIGH-RISK] — summary injection may push context window near limit; keep summary ≤100 tokens"
  - step: 4
    action: "Test by running two sessions and verifying AI references prior context"
    risk: "normal"

code_scaffolds:
  - label: "Redis memory read/write"
    language: "python"
    snippet: |
      import redis
      r = redis.Redis(host="localhost", port=6379, decode_responses=True)

      def get_memory(user_id: str) -> str:
          return r.get(f"user:{user_id}:summary") or ""

      def save_memory(user_id: str, summary: str):
          r.set(f"user:{user_id}:summary", summary, ex=86400)

integration_points:
  - component: "OpenAI chat endpoint (T-02)"
    connection: "Prepend get_memory() result to system prompt on every request"

shortcuts:
  - shortcut: "Use a single hardcoded user_id='demo' for the hackathon"
    trade_off: "No real multi-user support; acceptable for single-demo session"

gotchas:
  - "Redis not running on demo machine → add a startup health check with a clear error message"
  - "Summary growing too long → hard-cap at 150 tokens before injection"

done_criteria:
  - "Starting a new chat session shows AI referencing content from the previous session"
  - "Redis key exists and contains non-empty summary after first session ends"

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Context Files

Knowledge Base

• knowledge/hackathon-reference-architecture.md
• knowledge/hackathon-tools.md
• knowledge/hackathon-mvp-strategy.md
• knowledge/hackathon-common-failures.md

Playbooks

• playbooks/hackathon-workflow.md