{"id":"c5ffc0bf-fb7d-4660-8fb0-3ef8bf31d06d","shortId":"rakUxV","kind":"skill","title":"analyze-project","tagline":"Forensic root cause analyzer for Antigravity sessions. Classifies scope deltas, rework patterns, root causes, hotspots, and auto-improves prompts/health.","description":"# /analyze-project — Root Cause Analyst Workflow\n\nAnalyze 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**.\n\n## Goal\n\nFor each session, determine:\n\n1. What changed from the initial ask to the final executed work\n2. Whether the main cause was:\n   - user/spec\n   - agent\n   - repo/codebase\n   - validation/testing\n   - legitimate task complexity\n3. Whether the opening prompt was sufficient\n4. Which files/subsystems repeatedly correlate with struggle\n5. What changes would most improve future sessions\n\n## When to Use\n- You need a postmortem on AI-assisted coding sessions, especially when scope drift or repeated rework occurred.\n- You want root-cause analysis that separates user/spec issues from agent mistakes, repo friction, or validation gaps.\n- You need evidence-backed recommendations for improving future prompts, repo health, or delivery workflows.\n\n## Global Rules\n\n- Treat `.resolved.N` counts as **iteration signals**, not proof of failure\n- Separate **human-added scope**, **necessary discovered scope**, and **agent-introduced scope**\n- Separate **agent error** from **repo friction**\n- Every diagnosis must include **evidence** and **confidence**\n- Confidence levels:\n  - **High** = direct artifact/timestamp evidence\n  - **Medium** = multiple supporting signals\n  - **Low** = plausible inference, not directly proven\n- Evidence precedence:\n  - artifact contents > timestamps > metadata summaries > inference\n- If evidence is weak, say so\n\n---\n\n## Step 0.5: Session Intent Classification\n\nClassify the primary session intent from objective + artifacts:\n\n- `DELIVERY`\n- `DEBUGGING`\n- `REFACTOR`\n- `RESEARCH`\n- `EXPLORATION`\n- `AUDIT_ANALYSIS`\n\nRecord:\n- `session_intent`\n- `session_intent_confidence`\n\nUse intent to contextualize severity and rework shape.\nDo not judge exploratory or research sessions by the same standards as narrow delivery sessions.\n\n---\n\n## Step 1: Discover Conversations\n\n1. Read available conversation summaries from system context\n2. List conversation folders in the user’s Antigravity `brain/` directory\n3. Build a conversation index with:\n   - `conversation_id`\n   - `title`\n   - `objective`\n   - `created`\n   - `last_modified`\n4. If the user supplied a keyword/path, filter to matching conversations; otherwise analyze all\n\nOutput: indexed list of conversations to analyze.\n\n---\n\n## Step 2: Extract Session Evidence\n\nFor each conversation, read if present:\n\n### Core artifacts\n- `task.md`\n- `implementation_plan.md`\n- `walkthrough.md`\n\n### Metadata\n- `*.metadata.json`\n\n### Version snapshots\n- `task.md.resolved.0 ... N`\n- `implementation_plan.md.resolved.0 ... N`\n- `walkthrough.md.resolved.0 ... N`\n\n### Additional signals\n- other `.md` artifacts\n- timestamps across artifact updates\n- file/folder/subsystem names mentioned in plans/walkthroughs\n- validation/testing language\n- explicit acceptance criteria, constraints, non-goals, and file targets\n\nRecord per conversation:\n\n#### Lifecycle\n- `has_task`\n- `has_plan`\n- `has_walkthrough`\n- `is_completed`\n- `is_abandoned_candidate` = task exists but no walkthrough\n\n#### Revision / change volume\n- `task_versions`\n- `plan_versions`\n- `walkthrough_versions`\n- `extra_artifacts`\n\n#### Scope\n- `task_items_initial`\n- `task_items_final`\n- `task_completed_pct`\n- `scope_delta_raw`\n- `scope_creep_pct_raw`\n\n#### Timing\n- `created_at`\n- `completed_at`\n- `duration_minutes`\n\n#### Content / quality\n- `objective_text`\n- `initial_plan_summary`\n- `final_plan_summary`\n- `initial_task_excerpt`\n- `final_task_excerpt`\n- `walkthrough_summary`\n- `mentioned_files_or_subsystems`\n- `validation_requirements_present`\n- `acceptance_criteria_present`\n- `non_goals_present`\n- `scope_boundaries_present`\n- `file_targets_present`\n- `constraints_present`\n\n---\n\n## Step 3: Prompt Sufficiency\n\nScore the opening request on a 0–2 scale for:\n\n- **Clarity**\n- **Boundedness**\n- **Testability**\n- **Architectural specificity**\n- **Constraint awareness**\n- **Dependency awareness**\n\nCreate:\n- `prompt_sufficiency_score`\n- `prompt_sufficiency_band` = High / Medium / Low\n\nThen note which missing prompt ingredients likely contributed to later friction.\n\nDo not punish short prompts by default; a narrow, obvious task can still have high sufficiency.\n\n---\n\n## Step 4: Scope Change Classification\n\nClassify scope change into:\n\n- **Human-added scope** — new asks beyond the original task\n- **Necessary discovered scope** — work required to complete the original task correctly\n- **Agent-introduced scope** — likely unnecessary work introduced by the agent\n\nRecord:\n- `scope_change_type_primary`\n- `scope_change_type_secondary` (optional)\n- `scope_change_confidence`\n- evidence\n\nKeep one short example in mind for calibration:\n- Human-added: “also refactor nearby code while you’re here”\n- Necessary discovered: hidden dependency must be fixed for original task to work\n- Agent-introduced: extra cleanup or redesign not requested and not required\n\n---\n\n## Step 5: Rework Shape\n\nClassify each session into one primary pattern:\n\n- **Clean execution**\n- **Early replan then stable finish**\n- **Progressive scope expansion**\n- **Reopen/reclose churn**\n- **Late-stage verification churn**\n- **Abandoned mid-flight**\n- **Exploratory / research session**\n\nRecord:\n- `rework_shape`\n- `rework_shape_confidence`\n- evidence\n\n---\n\n## Step 6: Root Cause Analysis\n\nFor every non-clean session, assign:\n\n### Primary root cause\nOne of:\n- `SPEC_AMBIGUITY`\n- `HUMAN_SCOPE_CHANGE`\n- `REPO_FRAGILITY`\n- `AGENT_ARCHITECTURAL_ERROR`\n- `VERIFICATION_CHURN`\n- `LEGITIMATE_TASK_COMPLEXITY`\n\n### Secondary root cause\nOptional if materially relevant\n\n### Root-cause guidance\n- **SPEC_AMBIGUITY**: opening ask lacked boundaries, targets, criteria, or constraints\n- **HUMAN_SCOPE_CHANGE**: scope expanded because the user broadened the task\n- **REPO_FRAGILITY**: hidden coupling, brittle files, unclear architecture, or environment issues forced extra work\n- **AGENT_ARCHITECTURAL_ERROR**: wrong files, wrong assumptions, wrong approach, hallucinated structure\n- **VERIFICATION_CHURN**: implementation mostly worked, but testing/validation caused loops\n- **LEGITIMATE_TASK_COMPLEXITY**: revisions were expected for the difficulty and not clearly avoidable\n\nEvery root-cause assignment must include:\n- evidence\n- why stronger alternative causes were rejected\n- confidence\n\n---\n\n## Step 6.5: Session Severity Scoring (0–100)\n\nAssign each session a severity score to prioritize attention.\n\nComponents (sum, clamp 0–100):\n- **Completion failure**: 0–25 (`abandoned = 25`)\n- **Replanning intensity**: 0–15\n- **Scope instability**: 0–15\n- **Rework shape severity**: 0–15\n- **Prompt sufficiency deficit**: 0–10 (`low = 10`)\n- **Root cause impact**: 0–10 (`REPO_FRAGILITY` / `AGENT_ARCHITECTURAL_ERROR` highest)\n- **Hotspot recurrence**: 0–10\n\nBands:\n- **0–19 Low**\n- **20–39 Moderate**\n- **40–59 Significant**\n- **60–79 High**\n- **80–100 Critical**\n\nRecord:\n- `session_severity_score`\n- `severity_band`\n- `severity_drivers` = top 2–4 contributors\n- `severity_confidence`\n\nUse severity as a prioritization signal, not a verdict. Always explain the drivers.\nContextualize severity using session intent so research/exploration sessions are not over-penalized.\n\n---\n\n## Step 7: Subsystem / File Clustering\n\nAcross all conversations, cluster repeated struggle by file, folder, or subsystem.\n\nFor each cluster, calculate:\n- number of conversations touching it\n- average revisions\n- completion rate\n- abandonment rate\n- common root causes\n- average severity\n\nGoal: identify whether friction is mostly prompt-driven, agent-driven, or concentrated in specific repo areas.\n\n---\n\n## Step 8: Comparative Cohorts\n\nCompare:\n- first-shot successes vs re-planned sessions\n- completed vs abandoned\n- high prompt sufficiency vs low prompt sufficiency\n- narrow-scope vs high-scope-growth\n- short sessions vs long sessions\n- low-friction subsystems vs high-friction subsystems\n\nFor each comparison, identify:\n- what differs materially\n- which prompt traits correlate with smoother execution\n- which repo traits correlate with repeated struggle\n\nDo not just restate averages; extract cautious evidence-backed patterns.\n\n---\n\n## Step 9: Non-Obvious Findings\n\nGenerate 3–7 findings that are not simple metric restatements.\n\nEach finding must include:\n- observation\n- why it matters\n- evidence\n- confidence\n\nExamples of strong findings:\n- replans cluster around weak file targeting rather than weak acceptance criteria\n- scope growth often begins after initial success, suggesting post-success human expansion\n- auth-related struggle is driven more by repo fragility than agent hallucination\n\n---\n\n## Step 10: Report Generation\n\nCreate `session_analysis_report.md` with this structure:\n\n# 📊 Session Analysis Report — [Project Name]\n\n**Generated**: [timestamp]  \n**Conversations Analyzed**: [N]  \n**Date Range**: [earliest] → [latest]\n\n## Executive Summary\n\n| Metric | Value | Rating |\n|:---|:---|:---|\n| First-Shot Success Rate | X% | 🟢/🟡/🔴 |\n| Completion Rate | X% | 🟢/🟡/🔴 |\n| Avg Scope Growth | X% | 🟢/🟡/🔴 |\n| Replan Rate | X% | 🟢/🟡/🔴 |\n| Median Duration | Xm | — |\n| Avg Session Severity | X | 🟢/🟡/🔴 |\n| High-Severity Sessions | X / N | 🟢/🟡/🔴 |\n\nThresholds:\n- First-shot: 🟢 >70 / 🟡 40–70 / 🔴 <40\n- Scope growth: 🟢 <15 / 🟡 15–40 / 🔴 >40\n- Replan rate: 🟢 <20 / 🟡 20–50 / 🔴 >50\n\nAvg severity guidance:\n- 🟢 <25\n- 🟡 25–50\n- 🔴 >50\n\nNote: avg severity is an aggregate health signal, not the same as per-session severity bands.\n\nThen 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.\n\n## Root Cause Breakdown\n\n| Root Cause | Count | % | Notes |\n|:---|:---|:---|:---|\n\n## Prompt Sufficiency Analysis\n- common traits of high-sufficiency prompts\n- common missing inputs in low-sufficiency prompts\n- which missing prompt ingredients correlate most with replanning or abandonment\n\n## Scope Change Analysis\nSeparate:\n- Human-added scope\n- Necessary discovered scope\n- Agent-introduced scope\n\n## Rework Shape Analysis\nSummarize the main failure patterns across sessions.\n\n## Friction Hotspots\nShow the files/folders/subsystems most associated with replanning, abandonment, verification churn, and high severity.\n\n## First-Shot Successes\nList the cleanest sessions and extract what made them work.\n\n## Non-Obvious Findings\nList 3–7 evidence-backed findings with confidence.\n\n## Severity Triage\nList the highest-severity sessions and say whether the best intervention is:\n- prompt improvement\n- scope discipline\n- targeted skill/workflow\n- repo refactor / architecture cleanup\n- validation/test harness improvement\n\n## Recommendations\nFor each recommendation, use:\n- **Observed pattern**\n- **Likely cause**\n- **Evidence**\n- **Change to make**\n- **Expected benefit**\n- **Confidence**\n\n## Per-Conversation Breakdown\n\n| # | Title | Intent | Duration | Scope Δ | Plan Revs | Task Revs | Root Cause | Rework Shape | Severity | Complete? |\n|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|\n\n---\n\n## Step 11: Optional Post-Analysis Improvements\n\nIf appropriate, also:\n- update any local project-health or memory artifact (if present) with recurring failure modes and fragile subsystems\n- generate `prompt_improvement_tips.md` from high-sufficiency / first-shot-success sessions\n- suggest missing skills or workflows when the same subsystem or task sequence repeatedly causes struggle\n\nOnly recommend workflows/skills when the pattern appears repeatedly.\n\n---\n\n## Final Output Standard\n\nThe workflow must produce:\n1. metrics summary\n2. root-cause diagnosis\n3. prompt-sufficiency assessment\n4. subsystem/friction map\n5. severity triage and prioritization\n6. evidence-backed recommendations\n7. non-obvious findings\n\nPrefer explicit uncertainty over fake precision.\n\n## Limitations\n- Use this skill only when the task clearly matches the scope described above.\n- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.\n- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.","tags":["analyze","project","antigravity","awesome","skills","sickn33","agent-skills","agentic-skills","ai-agent-skills","ai-agents","ai-coding","ai-workflows"],"capabilities":["skill","source-sickn33","skill-analyze-project","topic-agent-skills","topic-agentic-skills","topic-ai-agent-skills","topic-ai-agents","topic-ai-coding","topic-ai-workflows","topic-antigravity","topic-antigravity-skills","topic-claude-code","topic-claude-code-skills","topic-codex-cli","topic-codex-skills"],"categories":["antigravity-awesome-skills"],"synonyms":[],"warnings":[],"endpointUrl":"https://skills.sh/sickn33/antigravity-awesome-skills/analyze-project","protocol":"skill","transport":"skills-sh","auth":{"type":"none","details":{"cli":"npx skills add sickn33/antigravity-awesome-skills","source_repo":"https://github.com/sickn33/antigravity-awesome-skills","install_from":"skills.sh"}},"qualityScore":"0.700","qualityRationale":"deterministic score 0.70 from registry signals: · indexed on github topic:agent-skills · 34964 github stars · SKILL.md body (12,286 chars)","verified":false,"liveness":"unknown","lastLivenessCheck":null,"agentReviews":{"count":0,"score_avg":null,"cost_usd_avg":null,"success_rate":null,"latency_p50_ms":null,"narrative_summary":null,"summary_updated_at":null},"enrichmentModel":"deterministic:skill-github:v1","enrichmentVersion":1,"enrichedAt":"2026-04-25T00:50:25.715Z","embedding":null,"createdAt":"2026-04-18T21:30:54.926Z","updatedAt":"2026-04-25T00:50:25.715Z","lastSeenAt":"2026-04-25T00:50:25.715Z","tsv":"'/.gemini/antigravity/brain':36 '/analyze-project':24 '0':361,364,367,499,820,834,838,844,848,853,858,865,875,878 '0.5':235 '1':65,284,287,1497 '10':859,861,866,876,1133 '100':821,835,891 '11':1429 '15':845,849,854,1199,1200 '19':879 '2':77,295,341,500,902,1500 '20':881,1205,1206 '25':839,841,1212,1213 '3':90,306,490,1072,1357,1505 '39':882 '4':97,319,550,903,1510 '40':884,1194,1196,1201,1202 '5':104,648,1513 '50':1207,1208,1214,1215 '59':885 '6':690,1518 '6.5':816 '60':887 '7':934,1073,1358,1523 '70':1193,1195 '79':888 '8':988 '80':890 '9':1066 'abandon':408,675,840,962,1003,1297,1332 'accept':386,475,1104 'across':375,938,1321 'ad':181,560,614,1304 'add':1234 'addit':369 'agent':84,144,188,192,580,589,636,713,767,869,979,1130,1310 'agent-driven':978 'agent-introduc':187,579,635,1309 'aggreg':1221 'ai':31,121 'ai-assist':30,120 'also':615,1437 'altern':810 'alway':916 'ambigu':707,733 'analysi':138,253,693,1142,1272,1300,1315,1433 'analyst':27 'analyz':2,7,29,331,339,1149 'analyze-project':1 'antigrav':9,303 'appear':1488 'approach':775 'appropri':1436 'architectur':506,714,760,768,870,1388 'area':986 'around':1097 'artifact':222,246,352,373,376,425,1446 'artifact/timestamp':208 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'comparison':1035 'complet':406,434,446,574,836,960,1001,1166,1427 'complex':89,720,789 'compon':831 'concentr':982 'confid':203,204,259,602,687,814,906,1090,1364,1408 'constraint':388,487,508,741 'content':223,450 'context':294 'contextu':263,920 'contribut':529 'contributor':904 'convers':286,290,297,309,312,329,337,347,397,940,955,1148,1411 'core':351 'correct':578 'correl':101,1043,1050,1292 'count':170,1268 'coupl':756 'creat':316,444,512,1136 'creep':440 'criteria':387,476,739,1105,1578 'critic':892 'date':1151 'debug':248 'default':539 'deficit':857 'deliveri':164,247,281 'delta':13,437 'depend':510,626 'describ':1546 'determin':64 'diagnosi':198,1504 'differ':1038 'difficulti':795 'direct':207,218 'directori':305 'disciplin':1259,1383 'discov':184,285,569,624,1307 'drift':128 'driven':977,980,1124 'driver':900,919 'durat':448,1177,1415 'earli':660 'earliest':1153 'environ':762,1558 'environment-specif':1557 'error':193,715,769,871 'especi':125 'everi':197,695,800 'evid':154,201,209,220,229,344,603,688,807,1062,1089,1360,1402,1520 'evidence-back':153,1061,1359,1519 'exampl':607,1091 'excerpt':462,465 'execut':75,659,1046,1155 'exist':411 'expand':746 'expans':667,1118 'expect':792,1406 'expert':1563 'explain':42,917 'explicit':385,1529 'explor':251 'exploratori':271,679 'extra':424,638,765 'extract':342,1059,1347 'failur':177,837,1319,1451 'fake':1532 'file':393,469,484,758,771,936,945,1099 'file/folder/subsystem':378 'files/folders/subsystems':1327 'files/subsystems':99 'filter':326 'final':74,432,457,463,1490 'find':1070,1074,1082,1094,1355,1362,1527 'finish':664 'first':993,1161,1191,1339,1463 'first-shot':992,1160,1190,1338 'first-shot-success':1462 'fix':629 'flight':678 'folder':298,946 'forc':764 'forens':4 'fragil':712,754,868,1128,1257,1454 'friction':147,196,532,972,1026,1031,1323 'futur':110,159 'gap':150 'generat':1071,1135,1146,1456 'global':166 'go':1242 'goal':60,391,479,969 'growth':1018,1107,1171,1198 'guidanc':731,1211 'hallucin':776,1131 'happen':46,50 'har':1391 'health':162,1222,1443 'hidden':625,755 'high':206,519,547,889,1004,1016,1030,1184,1277,1336,1460 'high-frict':1029 'high-scope-growth':1015 'high-sever':1183 'high-suffici':1276,1459 'highest':872,1370 'highest-sever':1369 'hotspot':18,873,1324 'human':180,559,613,708,742,1117,1303 'human-ad':179,558,612,1302 'id':313 'identifi':970,1036 'impact':864 'implement':780 'implementation_plan.md':354 'implementation_plan.md.resolved':363 'improv':22,109,158,1381,1392,1434 'includ':200,806,1084 'index':310,334 'infer':216,227 'ingredi':527,1291 'initi':70,429,454,460,1111 'input':1282,1572 'instabl':847 'intens':843 'intent':237,243,256,258,261,924,1414 'intervent':1378 'introduc':189,581,586,637,1311 'issu':142,763,1252 'item':428,431 'iter':172 'judg':270 'keep':604 'keyword/path':325 'lack':736 'languag':384 'last':317 'late':671 'late-stag':670 'later':531 'latest':1154 'legitim':87,718,787 'level':205 'lifecycl':398 'like':528,583,1400 'limit':1534 'list':296,335,1342,1356,1367 'local':1440 'long':1022 'loop':786 'low':214,521,860,880,1008,1025,1285 'low-frict':1024 'low-suffici':1284 'made':1349 'main':80,1251,1318 'make':1405 'map':1512 'match':328,1543 'materi':726,1039 'matter':1088 'md':372 'median':1176 'medium':210,520 'memori':1445 'mention':380,468 'metadata':225,356 'metadata.json':357 'metric':1079,1157,1498 'mid':677 'mid-flight':676 'mind':609 'minut':449 'miss':525,1281,1289,1468,1580 'mistak':145 'mode':1452 'moder':883 'modifi':318 'most':781,974 'multipl':211 'must':199,627,805,1083,1495 'n':362,365,368,1150,1188 'name':379,1145 'narrat':1237 'narrow':280,541,1012 'narrow-scop':1011 'nearbi':617 'necessari':183,568,623,1306 'need':116,152 'new':562 'next':58 'non':390,478,697,1068,1353,1525 'non-clean':696 'non-goal':389 'non-obvi':1067,1352,1524 'note':523,1216,1269 'number':953 'object':245,315,452 'observ':1085,1398 'obvious':542,1069,1354,1526 'occur':132 'often':1108 'one':605,655,704 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