ranbot-ai/analyze-project

/analyze-project — Root Cause Analyst Workflow

Analyze AI-assisted coding sessions in ~/.gemini/antigravity/brain/ and produce a report that explains not just what happened, but why it happened, who/what caused it, and what should change next time.

Goal

For each session, determine:

1. What changed from the initial ask to the final executed work
2. Whether the main cause was:
   • user/spec
   • agent
   • repo/codebase
   • validation/testing
   • legitimate task complexity
3. Whether the opening prompt was sufficient
4. Which files/subsystems repeatedly correlate with struggle
5. What changes would most improve future sessions

When to Use

• You need a postmortem on AI-assisted coding sessions, especially when scope drift or repeated rework occurred.
• You want root-cause analysis that separates user/spec issues from agent mistakes, repo friction, or validation gaps.
• You need evidence-backed recommendations for improving future prompts, repo health, or delivery workflows.

Global Rules

• Treat .resolved.N counts as iteration signals, not proof of failure
• Separate human-added scope, necessary discovered scope, and agent-introduced scope
• Separate agent error from repo friction
• Every diagnosis must include evidence and confidence
• Confidence levels:
  • High = direct artifact/timestamp evidence
  • Medium = multiple supporting signals
  • Low = plausible inference, not directly proven
• Evidence precedence:
  • artifact contents > timestamps > metadata summaries > inference
• If evidence is weak, say so

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Step 0.5: Session Intent Classification

Classify the primary session intent from objective + artifacts:

• DELIVERY
• DEBUGGING
• REFACTOR
• RESEARCH
• EXPLORATION
• AUDIT_ANALYSIS

Record:

• session_intent
• session_intent_confidence

Use intent to contextualize severity and rework shape. Do not judge exploratory or research sessions by the same standards as narrow delivery sessions.

---

Step 1: Discover Conversations

1. Read available conversation summaries from system context
2. List conversation folders in the user’s Antigravity brain/ directory
3. Build a conversation index with:
   • conversation_id
   • title
   • objective
   • created
   • last_modified
4. If the user supplied a keyword/path, filter to matching conversations; otherwise analyze all

Output: indexed list of conversations to analyze.

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Step 2: Extract Session Evidence

For each conversation, read if present:

Core artifacts

• task.md
• implementation_plan.md
• walkthrough.md

Metadata

• *.metadata.json

Version snapshots

• task.md.resolved.0 ... N
• implementation_plan.md.resolved.0 ... N
• walkthrough.md.resolved.0 ... N

Additional signals

• other .md artifacts
• timestamps across artifact updates
• file/folder/subsystem names mentioned in plans/walkthroughs
• validation/testing language
• explicit acceptance criteria, constraints, non-goals, and file targets

Record per conversation:

Lifecycle

• has_task
• has_plan
• has_walkthrough
• is_completed
• is_abandoned_candidate = task exists but no walkthrough

Revision / change volume

• task_versions
• plan_versions
• walkthrough_versions
• extra_artifacts

Scope

• task_items_initial
• task_items_final
• task_completed_pct
• scope_delta_raw
• scope_creep_pct_raw

Timing

• created_at
• completed_at
• duration_minutes

Content / quality

• objective_text
• initial_plan_summary
• final_plan_summary
• initial_task_excerpt
• final_task_excerpt
• walkthrough_summary
• mentioned_files_or_subsystems
• validation_requirements_present
• acceptance_criteria_present
• non_goals_present
• scope_boundaries_present
• file_targets_present
• constraints_present

---

Step 3: Prompt Sufficiency

Score the opening request on a 0–2 scale for:

• Clarity
• Boundedness
• Testability
• Architectural specificity
• Constraint awareness
• Dependency awareness

Create:

• prompt_sufficiency_score
• prompt_sufficiency_band = High / Medium / Low

Then note which missing prompt ingredients likely contributed to later friction.

Do not punish short prompts by default; a narrow, obvious task can still have high sufficiency.

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Step 4: Scope Change Classification

Classify scope change into:

• Human-added scope — new asks beyond the original task
• Necessary discovered scope — work required to complete the original task correctly
• Agent-introduced scope — likely unnecessary work introduced by the agent

Record:

• scope_change_type_primary
• scope_change_type_secondary (optional)
• scope_change_confidence
• evidence

Keep one short example in mind for calibration:

• Human-added: “also refactor nearby code while you’re here”
• Necessary discovered: hidden dependency must be fixed for original task to work
• Agent-in