What is analyze-project?

A forensic analysis workflow for Antigravity coding sessions, examining what changed during an AI-assisted development task. It separates causes such as the original request, the agent, the codebase, testing, or task complexity.

Use it to review past sessions, find scope changes and rework patterns, locate troublesome files or subsystems, and improve future prompts, repositories, or validation steps.

When a coding task drifts or needs repeated rework, the final result does not explain why. This workflow produces an evidence-based postmortem and identifies recurring problem areas.

analyze-project — Skill for Claude Code and Codex by humaisali

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add humaisali/Awesome-AI-Skills --skill analyze-project

Clone the repo

git clone --depth 1 https://github.com/humaisali/Awesome-AI-Skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/humaisali/awesome-ai-skills/analyze-project/github.svg)](https://agentmods.dev/skills/humaisali/awesome-ai-skills/analyze-project)

Your own site

<a href="https://agentmods.dev/skills/humaisali/awesome-ai-skills/analyze-project"><img src="https://agentmods.dev/badge/skills/humaisali/awesome-ai-skills/analyze-project/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/humaisali/awesome-ai-skills/analyze-project"><img src="https://agentmods.dev/badge/skills/humaisali/awesome-ai-skills/analyze-project.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,036 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin 88% copy Near-identical to another mod in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00039	$0.03036
Opus 5	$0.00019	$0.01518
Sonnet 5	$0.00008	$0.00607
Haiku 4.5	$0.00004	$0.00304

Measured 11d ago against content hash 4701d3744459, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

analyze-project scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

88% identical to analyze-project — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

AI-ML & Data Science Skills/Agents & LLMs/analyze-project/SKILL.md · 445 lines

How it starts

The opening of the file, as written. The whole thing — 445 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/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.

Read the full file on GitHub · 445 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

examples/sample_session_analysis_report.md 4.4 KB

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

11d ago First seen · 445 lines · 39 tokens per session scan A 4701d3744459

Subscribe to this mod's changes

analyze-project is a skill published in the GitHub repository humaisali/Awesome-AI-Skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 3,036 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to analyze-project, differing in 12 lines, and is treated as a copy.

Debugging analysis diagnostics meta project-health root-cause session-review

analyze-project

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

This is a copy

How it starts

/analyze-project — Root Cause Analyst Workflow

Goal

When to Use

Global Rules

Step 0.5: Session Intent Classification

What ships with it

What this file has done since we first saw it

Other skills, from other repositories

analyze-project

analyze-project

analyze-project

analyze-project

analyze-project

analyze-project