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

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

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.

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.

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-introduced: extra cleanup or redesign not requested and not required

Step 5: Rework Shape

Classify each session into one primary pattern:

• Clean execution
• Early replan then stable finish
• Progressive scope expansion
• Reopen/reclose churn
• Late-stage verification churn
• Abandoned mid-flight
• Exploratory / research session

Record:

• rework_shape
• rework_shape_confidence
• evidence

Step 6: Root Cause Analysis

For every non-clean session, assign:

Primary root cause

One of:

• SPEC_AMBIGUITY
• HUMAN_SCOPE_CHANGE
• REPO_FRAGILITY
• AGENT_ARCHITECTURAL_ERROR
• VERIFICATION_CHURN
• LEGITIMATE_TASK_COMPLEXITY

Secondary root cause

Optional if materially relevant

Root-cause guidance

• SPEC_AMBIGUITY: opening ask lacked boundaries, targets, criteria, or constraints
• HUMAN_SCOPE_CHANGE: scope expanded because the user broadened the task
• REPO_FRAGILITY: hidden coupling, brittle files, unclear architecture, or environment issues forced extra work
• AGENT_ARCHITECTURAL_ERROR: wrong files, wrong assumptions, wrong approach, hallucinated structure
• VERIFICATION_CHURN: implementation mostly worked, but testing/validation caused loops
• LEGITIMATE_TASK_COMPLEXITY: revisions were expected for the difficulty and not clearly avoidable

Every root-cause assignment must include:

• evidence
• why stronger alternative causes were rejected
• confidence

Step 6.5: Session Severity Scoring (0–100)

Assign each session a severity score to prioritize attention.

Components (sum, clamp 0–100):

• Completion failure: 0–25 (abandoned = 25)
• Replanning intensity: 0–15
• Scope instability: 0–15
• Rework shape severity: 0–15
• Prompt sufficiency deficit: 0–10 (low = 10)
• Root cause impact: 0–10 (REPO_FRAGILITY / AGENT_ARCHITECTURAL_ERROR highest)
• Hotspot recurrence: 0–10

Bands:

• 0–19 Low
• 20–39 Moderate
• 40–59 Significant
• 60–79 High
• 80–100 Critical

Record:

• session_severity_score
• severity_band
• severity_drivers = top 2–4 contributors
• severity_confidence

Use severity as a prioritization signal, not a verdict. Always explain the drivers. Contextualize severity using session intent so research/exploration sessions are not over-penalized.

Step 7: Subsystem / File Clustering

Across all conversations, cluster repeated struggle by file, folder, or subsystem.

For each cluster, calculate:

• number of conversations touching it
• average revisions
• completion rate
• abandonment rate
• common root causes
• average severity

Goal: identify whether friction is mostly prompt-driven, agent-driven, or concentrated in specific repo areas.

Step 8: Comparative Cohorts

Compare:

• first-shot successes vs re-planned sessions
• completed vs abandoned
• high prompt sufficiency vs low prompt sufficiency
• narrow-scope vs high-scope-growth
• short sessions vs long sessions
• low-friction subsystems vs high-friction subsystems

For each comparison, identify:

• what differs materially
• which prompt traits correlate with smoother execution
• which repo traits correlate with repeated struggle

Do not just restate averages; extract cautious evidence-backed patterns.

Step 9: Non-Obvious Findings

Generate 3–7 findings that are not simple metric restatements.

Each finding must include:

• observation
• why it matters
• evidence
• confidence

Examples of strong findings:

• replans cluster around weak file targeting rather than weak acceptance criteria
• scope growth often begins after initial success, suggesting post-success human expansion
• auth-related struggle is driven more by repo fragility than agent hallucination

Step 10: Report Generation

Create session_analysis_report.md with this structure:

📊 Session Analysis Report — [Project Name]

Generated: [timestamp]
Conversations Analyzed: [N]
Date Range: [earliest] → [latest]

Executive Summary

Thresholds:

• First-shot: 🟢 >70 / 🟡 40–70 / 🔴 <40
• Scope growth: 🟢 <15 / 🟡 15–40 / 🔴 >40
• Replan rate: 🟢 <20 / 🟡 20–50 / 🔴 >50

Avg severity guidance:

• 🟢 <25
• 🟡 25–50
• 🔴 >50

Note: avg severity is an aggregate health signal, not the same as per-session severity bands.

Then add a short narrative summary of what is going well, what is breaking down, and whether the main issue is prompt quality, repo fragility, workflow discipline, or validation churn.

Root Cause Breakdown

Prompt Sufficiency Analysis

• common traits of high-sufficiency prompts
• common missing inputs in low-sufficiency prompts
• which missing prompt ingredients correlate most with replanning or abandonment

Scope Change Analysis

Separate:

• Human-added scope
• Necessary discovered scope
• Agent-introduced scope

Rework Shape Analysis

Summarize the main failure patterns across sessions.

Friction Hotspots

Show the files/folders/subsystems most associated with replanning, abandonment, verification churn, and high severity.

First-Shot Successes

List the cleanest sessions and extract what made them work.

Non-Obvious Findings

List 3–7 evidence-backed findings with confidence.

Severity Triage

List the highest-severity sessions and say whether the best intervention is:

• prompt improvement
• scope discipline
• targeted skill/workflow
• repo refactor / architecture cleanup
• validation/test harness improvement

Recommendations

For each recommendation, use:

• Observed pattern
• Likely cause
• Evidence
• Change to make
• Expected benefit
• Confidence

Per-Conversation Breakdown

Step 11: Optional Post-Analysis Improvements

If appropriate, also:

• update any local project-health or memory artifact (if present) with recurring failure modes and fragile subsystems
• generate prompt_improvement_tips.md from high-sufficiency / first-shot-success sessions
• suggest missing skills or workflows when the same subsystem or task sequence repeatedly causes struggle

Only recommend workflows/skills when the pattern appears repeatedly.

Final Output Standard

The workflow must produce:

1. metrics summary
2. root-cause diagnosis
3. prompt-sufficiency assessment
4. subsystem/friction map
5. severity triage and prioritization
6. evidence-backed recommendations
7. non-obvious findings

Prefer explicit uncertainty over fake precision.

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \\
  --content "Description of what was decided/solved" \\
  --type decision \\
  --tags analyze-project <relevant-tags>

Agent Team Collaboration

• This skill can be invoked by the orchestrator agent via intelligent routing.
• In Agent Teams mode, results are shared via Qdrant shared memory for cross-agent context.
• In Subagent mode, this skill runs in isolation with its own memory namespace.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns