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Get Started Free →Audit local AI coding-agent sessions with agenttrace. Use when the user asks to inspect Claude Code, Codex CLI, Gemini CLI, Qwen Code, Cline, Aider, Cursor exports, Hermes Agent, OpenCode, OpenClaw, Pi, Oh My Pi, Kimi CLI, Copilot-style logs, or generic JSON/JSONL traces for cost, tokens, tool failures, latency, anomalies, health, diffs, or CI gates.
.claude/skills/hashgraph-online-agenttrace-session-audit/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -40% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -40% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -57% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -71% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -48% | 0% |
Use this skill when session logs need an operational read: spend, token burn, cache use, tool failures, retry loops, latency, health, anomalies, and CI gate readiness.
agenttrace binary when it is available on PATH.luoyuctl/agenttrace repository, use go run ./cmd/agenttrace.bashagenttrace --doctor agenttrace --overview
bashagenttrace --overview -f markdown -o agenttrace-overview.md
bashagenttrace --overview -f json -o agenttrace-overview.json agenttrace --overview --fail-under-health 80 --fail-on-critical --max-tool-fail-rate 15
bashagenttrace --latest agenttrace --latest -f json
bashagenttrace path/to/session-or-export.json agenttrace --overview -d path/to/session-dir
agenttrace command and threshold.agenttrace --doctor and report the detected agent directories and next step.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 7,957 | 10,569 | +33% | 1 | 1 | 0% | 422 | 753 | +78% | 0 | 0 | — |
case-02 | fail→fail | 7,494 | 10,552 | +41% | 1 | 1 | 0% | 1,001 | 780 | -22% | 0 | 0 | — |
case-03 | fail→fail | 15,007 | 11,047 | -26% | 1 | 1 | 0% | 268 | 663 | +147% | 0 | 0 | — |
case-04 | fail→pass | 14,635 | 3,141 | -79% | 1 | 1 | 0% | 1,517 | 916 | -40% | 0 | 0 | — |
case-05 | fail→pass | 10,954 | 3,485 | -68% | 1 | 1 | 0% | 1,519 | 905 | -40% | 0 | 0 | — |
case-06 | fail→pass | 15,512 | 7,797 | -50% | 1 | 1 | 0% | 2,122 | 907 | -57% | 0 | 0 | — |
case-07 | fail→pass | 27,670 | 8,800 | -68% | 1 | 1 | 0% | 3,205 | 943 | -71% | 0 | 0 | — |
case-08 | fail→pass | 11,467 | 7,805 | -32% | 1 | 1 | 0% | 1,616 | 842 | -48% | 0 | 0 | — |
case-09 | fail→pass | 15,462 | 4,430 | -71% | 1 | 1 | 0% | 1,438 | 1,262 | -12% | 0 | 0 | — |
case-10 | fail→pass | 15,110 | 11,250 | -26% | 1 | 1 | 0% | 2,783 | 1,373 | -51% | 0 | 0 | — |
case-11 | fail→pass | 16,503 | 9,440 | -43% | 1 | 1 | 0% | 1,448 | 1,008 | -30% | 0 | 0 | — |
case-12 | pass→pass | 12,393 | 10,578 | -15% | 1 | 1 | 0% | 1,941 | 1,135 | -42% | 0 | 0 | — |
case-13 | fail→fail | 18,100 | 14,339 | -21% | 1 | 1 | 0% | 1,834 | 1,902 | +4% | 0 | 0 | — |
case-14 | pass→pass | 16,794 | 8,917 | -47% | 1 | 1 | 0% | 935 | 956 | +2% | 0 | 0 | — |
case-15 | fail→pass | 20,246 | 11,139 | -45% | 1 | 1 | 0% | 2,021 | 1,493 | -26% | 0 | 0 | — |
case-16 | fail→fail | 23,580 | 19,396 | -18% | 1 | 1 | 0% | 3,030 | 2,803 | -7% | 0 | 0 | — |
case-17 | fail→pass | 29,538 | 4,687 | -84% | 1 | 1 | 0% | 2,026 | 1,074 | -47% | 0 | 0 | — |
case-18 | pass→pass | 10,092 | 6,948 | -31% | 1 | 1 | 0% | 1,261 | 1,350 | +7% | 0 | 0 | — |
case-19 | pass→pass | 20,613 | 31,713 | +54% | 1 | 1 | 0% | 4,161 | 4,344 | +4% | 0 | 0 | — |
case-20 | pass→pass | 12,664 | 17,343 | +37% | 1 | 1 | 0% | 1,988 | 2,066 | +4% | 0 | 0 | — |
case-21 | pass→pass | 19,289 | 17,101 | -11% | 1 | 1 | 0% | 2,226 | 2,222 | -0% | 0 | 0 | — |
case-22 | pass→pass | 11,928 | 9,309 | -22% | 1 | 1 | 0% | 1,922 | 1,985 | +3% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 19 counted toward the lift figure. The other 3 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +45 percentage points is the difference between those two pass rates over the 19 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.