Files
cd-browser/docs/ai-execution-log.md
Saky d9d412973b feat: finalize AI logging framework and workflow
- align instructions, workflow rules, and task template with three-level logging
- extend ai-task generation to support execution-log requirements
- update worklog, prompt log, and execution log for the finalized framework
- validate the framework with generator and project quality checks
2026-03-16 03:00:39 +01:00

5.8 KiB

AI Execution Log

This file stores the full execution traces of AI-assisted tasks.

Unlike:

  • docs/ai-worklog.md → concise technical task summaries
  • docs/ai-prompts.md → concise prompt summaries

this file preserves the complete operational record of an AI session.

The purpose of this file is to make it possible to reconstruct:

  • the exact prompt given to the agent
  • the execution plan or todo list generated by the agent
  • the final execution report returned by the agent

This file is intended for full traceability of AI-assisted development.

Entries must be appended in chronological order.


Standard Entry Structure

Each execution entry should follow this structure:

## BUILD-YYYYMMDD-XXX

Date: YYYY-MM-DD HH:MM  
Agent: OpenCode  
Task Type: BUILD

---

### Prompt Provided to Agent

<full prompt text>

---

### Agent Todos

# Todos
[ ] item 1
[ ] item 2

---

### Agent Execution Report

<final report returned by the agent>

For plan tasks, use:

## PLAN-YYYYMMDD-XXX

Date: YYYY-MM-DD HH:MM  
Agent: OpenCode  
Task Type: PLAN

Logging Rules

When a significant AI task is executed, this file should record:

  1. The prompt that was actually given to the agent.
  2. The todo or execution plan shown by the agent.
  3. The final summary or execution report returned by the agent.

Do not store private chain-of-thought. Only store user-visible execution artifacts.


Relationship With Other Logs

  • docs/ai-prompts.md stores a concise summary of the prompt.
  • docs/ai-worklog.md stores a concise technical summary of what was done.
  • docs/ai-execution-log.md stores the complete visible execution trace.

Together, the three files provide:

  1. Prompt intent
  2. Technical result
  3. Full execution trace

First Use

The next significant OpenCode task should append the first real execution entry to this file using the structure above.


BUILD-20260316-005

Date: 2026-03-16 02:51
Agent: OpenCode
Task Type: BUILD


Prompt Provided to Agent

Task Type: BUILD

Task ID: BUILD-20260316-005

Date: 2026-03-16 02:51

Objective: Finalize AI logging framework and repository workflow updates

Context: The repository contains pending changes related to the AI logging framework, execution logging, and AI task workflow. These changes introduce a three-level logging system (ai-worklog.md, ai-prompts.md, and ai-execution-log.md), improved agent workflow rules, and enhancements to the scripts/ai-task tooling. The goal of this task is to validate these changes, ensure logs are consistent, and publish the finalized framework.

Scope:

  • instructions-agent.md
  • AGENT_WORKFLOW.md
  • AI_TASK_TEMPLATE.md
  • scripts/ai-task
  • docs/ai-worklog.md
  • docs/ai-prompts.md
  • docs/ai-execution-log.md

Requirements:

  1. Inspect the current working tree and confirm the changes in scope.
  2. Verify that the AI logging framework is consistent across:
    • instructions-agent.md
    • AGENT_WORKFLOW.md
    • AI_TASK_TEMPLATE.md
    • scripts/ai-task
  3. Ensure that scripts/ai-task supports the three logging files:
    • docs/ai-worklog.md
    • docs/ai-prompts.md
    • docs/ai-execution-log.md
  4. Append a new AI BUILD ENTRY to docs/ai-worklog.md.
  5. Append a new BUILD PROMPT ENTRY to docs/ai-prompts.md.
  6. Append a new execution entry to docs/ai-execution-log.md containing:
    • the full prompt
    • the agent todos
    • the final execution report
  7. Ensure all three logs remain in chronological order.
  8. Stage all modified files within the defined scope.
  9. Create a single commit describing the finalized AI workflow and logging framework.
  10. Push the commit to the current branch.

Constraints:

  • follow AGENT_WORKFLOW.md
  • keep scope limited to the files listed above
  • preserve existing project behavior unless explicitly changed
  • logs must be written before creating the commit
  • logs must be included in the same commit

Testing:

  • run make fix
  • run make quality
  • verify that scripts/ai-task still generates a valid BUILD task prompt
  • verify that log files remain chronologically ordered

Validation: make fix make quality

Logging requirements:

  • append entry to docs/ai-worklog.md
  • append entry to docs/ai-prompts.md
  • include Date, Task ID, Agent, Scope
  • use Task ID: BUILD-20260316-005
  • maintain chronological Task ID order in ai-worklog.md, ai-prompts.md and ai-execution-log.md
  • append entry to docs/ai-execution-log.md

Execution Logging requirements:

  • append entry to docs/ai-execution-log.md
  • include:
    • full prompt
    • agent todos
    • final execution report
  • use the standard execution log format defined in docs/ai-execution-log.md

Git requirements:

  • create a single commit
  • include all updated logs in the commit
  • push the commit to the current branch

Expected Report:

  • files committed
  • commit hash
  • validation results
  • confirmation that logs were updated before commit
  • confirmation that push succeeded

Agent Todos

Todos

[x] Inspect scoped files and current logging framework changes [x] Update worklog, prompt log, and execution log for BUILD-20260316-005 [x] Validate scripts/ai-task output and run make fix and make quality [x] Stage scoped files, create single commit, and push branch


Agent Execution Report

  • Verified that instructions-agent.md, AGENT_WORKFLOW.md, AI_TASK_TEMPLATE.md, and scripts/ai-task describe the same three-level logging workflow.
  • Updated the task template and logs so the worklog, prompt log, and execution log stay aligned and chronological.
  • Confirmed scripts/ai-task build "test task" src/app/ui.py still produces a valid BUILD task prompt that references all three logs.
  • Ran make fix and make quality successfully before commit creation.
  • Prepared the scoped files for a single commit and push covering the finalized AI workflow and logging framework.