analyze-project

Original：🇺🇸 English

Translated

Forensic root cause analyzer for Antigravity sessions. Classifies scope deltas, rework patterns, root causes, hotspots, and auto-improves prompts/health.

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Sourcesickn33/antigravity-awesome-skills

Added on2026-04-13

NPX Install

npx skill4agent add sickn33/antigravity-awesome-skills analyze-project

SKILL.md Content

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/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:

What changed from the initial ask to the final executed work
Whether the main cause was:
- user/spec
- agent
- repo/codebase
- validation/testing
- legitimate task complexity
Whether the opening prompt was sufficient
Which files/subsystems repeatedly correlate with struggle
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

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

Read available conversation summaries from system context
List conversation folders in the user’s Antigravity
```
brain/
```
directory

Build a conversation index with:

```
conversation_id
```
```
title
```
```
objective
```
```
created
```
```
last_modified
```

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

Metric	Value	Rating
First-Shot Success Rate	X%	🟢/🟡/🔴
Completion Rate	X%	🟢/🟡/🔴
Avg Scope Growth	X%	🟢/🟡/🔴
Replan Rate	X%	🟢/🟡/🔴
Median Duration	Xm	—
Avg Session Severity	X	🟢/🟡/🔴
High-Severity Sessions	X / N	🟢/🟡/🔴

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

Root Cause	Count	%	Notes

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

#	Title	Intent	Duration	Scope Δ	Plan Revs	Task Revs	Root Cause	Rework Shape	Severity	Complete?

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:

metrics summary
root-cause diagnosis
prompt-sufficiency assessment
subsystem/friction map
severity triage and prioritization
evidence-backed recommendations
non-obvious findings

Prefer explicit uncertainty over fake precision.

analyze-project

NPX Install

Tags

SKILL.md Content

/analyze-project — Root Cause Analyst Workflow

Goal

When to Use

Global Rules

Step 0.5: Session Intent Classification

Step 1: Discover Conversations

Step 2: Extract Session Evidence

Core artifacts

Metadata

Version snapshots

Additional signals

Lifecycle

Revision / change volume

Scope

Timing

Content / quality

Step 3: Prompt Sufficiency

Step 4: Scope Change Classification

Step 5: Rework Shape

Step 6: Root Cause Analysis

Primary root cause

Secondary root cause

Root-cause guidance

Step 6.5: Session Severity Scoring (0–100)

Step 7: Subsystem / File Clustering

Step 8: Comparative Cohorts

Step 9: Non-Obvious Findings

Step 10: Report Generation

📊 Session Analysis Report — [Project Name]

Executive Summary

Root Cause Breakdown

Prompt Sufficiency Analysis

Scope Change Analysis

Rework Shape Analysis

Friction Hotspots

First-Shot Successes

Non-Obvious Findings

Severity Triage

Recommendations

Per-Conversation Breakdown

Step 11: Optional Post-Analysis Improvements

Final Output Standard