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Get Started Free →Read and render the output of a local Output SDK workflow trace file as clean readable markdown. Use when the user wants to view what a recent workflow produced, see the result from a local trace file, or render trace output as a document.
.claude/skills/growthxai-output-workflow-trace-file/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-13 | ✗→✓ | ▲ Improved | -14% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -57% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -44% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 38% | 0% |
Show just the final output of an Output.ai workflow trace — the actual result, rendered as readable markdown.
The argument the user provided is either a workflow name (e.g. context_competitors) or a workflow run ID. If no argument is provided, use the most recent trace across all workflows.
logs/runs/<workflow_name>/ as JSON files<timestamp>_<workflow_id>.json.json file in logs/runs/<workflow_name>/logs/runs/*/ folders for a file containing that ID in its filename.json file across all logs/runs/*/ foldersoutput from the JSON root — skip children and input.Strategy for large files (trace files can be 10k+ lines):
jq '.output' <file> to extract directly — this is the fastest pathjq is not available: read the last 500 lines of the file (the output field is at the root level, near the end of the JSON). Work backwards in chunks if neededchildren array with step details can be thousands of lines and you don't need any of ittmp/trace_result_<workflow_name>_<id>.md (create the tmp/ directory if it doesn't exist) with:### Header (brief)
### Result Render output as clean, readable markdown:
The goal is a document you'd want to READ, not debug. Make it look good.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 11,130 | 5,195 | -53% | 1 | 1 | 0% | 1,552 | 838 | -46% | 0 | 0 | — |
case-02 | fail→fail | 3,121 | 5,478 | +76% | 1 | 1 | 0% | 501 | 913 | +82% | 0 | 0 | — |
case-03 | fail→fail | 3,308 | 5,350 | +62% | 1 | 1 | 0% | 481 | 851 | +77% | 0 | 0 | — |
case-04 | fail→fail | 7,523 | 7,502 | -0% | 1 | 1 | 0% | 1,276 | 1,137 | -11% | 0 | 0 | — |
case-13 | fail→pass | 8,335 | 3,220 | -61% | 1 | 1 | 0% | 1,212 | 1,039 | -14% | 0 | 0 | — |
case-05 | fail→fail | 9,473 | 21,088 | +123% | 1 | 1 | 0% | 1,557 | 4,381 | +181% | 0 | 0 | — |
case-06 | fail→fail | 11,352 | 8,763 | -23% | 1 | 1 | 0% | 2,023 | 2,102 | +4% | 0 | 0 | — |
case-07 | fail→pass | 12,410 | 1,990 | -84% | 1 | 1 | 0% | 2,145 | 917 | -57% | 0 | 0 | — |
case-08 | fail→pass | 10,446 | 5,519 | -47% | 1 | 1 | 0% | 1,785 | 1,651 | -8% | 0 | 0 | — |
case-09 | pass→pass | 6,962 | 3,330 | -52% | 1 | 1 | 0% | 1,083 | 1,134 | +5% | 0 | 0 | — |
case-10 | fail→pass | 11,297 | 2,922 | -74% | 1 | 1 | 0% | 1,913 | 1,073 | -44% | 0 | 0 | — |
case-11 | pass→pass | 7,708 | 2,135 | -72% | 1 | 1 | 0% | 1,153 | 934 | -19% | 0 | 0 | — |
case-12 | fail→pass | 9,173 | 13,085 | +43% | 1 | 1 | 0% | 1,516 | 2,088 | +38% | 0 | 0 | — |
case-14 | fail→pass | 14,047 | 2,760 | -80% | 1 | 1 | 0% | 2,239 | 958 | -57% | 0 | 0 | — |
case-15 | fail→fail | 3,446 | 2,532 | -27% | 1 | 1 | 0% | 533 | 1,009 | +89% | 0 | 0 | — |
case-16 | pass→pass | 10,146 | 3,049 | -70% | 1 | 1 | 0% | 1,595 | 1,069 | -33% | 0 | 0 | — |
case-17 | pass→pass | 9,925 | 2,058 | -79% | 1 | 1 | 0% | 1,501 | 866 | -42% | 0 | 0 | — |
case-18 | fail→fail | 4,503 | 3,967 | -12% | 1 | 1 | 0% | 750 | 1,280 | +71% | 0 | 0 | — |
case-19 | fail→pass | 17,318 | 2,005 | -88% | 1 | 1 | 0% | 765 | 899 | +18% | 0 | 0 | — |
case-20 | pass→pass | 7,116 | 8,453 | +19% | 1 | 1 | 0% | 1,146 | 2,047 | +79% | 0 | 0 | — |
case-21 | fail→fail | 8,249 | 15,516 | +88% | 1 | 1 | 0% | 1,226 | 2,645 | +116% | 0 | 0 | — |
case-22 | pass→pass | 7,142 | 9,849 | +38% | 1 | 1 | 0% | 1,110 | 2,307 | +108% | 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 17 counted toward the lift figure. The other 5 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 +32 percentage points is the difference between those two pass rates over the 17 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.