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Get Started Free →Diagnose instruction defects and optionally submit Rosetta GitHub issue
.claude/skills/griddynamics-post-mortem/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 816% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 732% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 368% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 206% | 0% |
| case-21 | ✗→✓ | ▲ Improved | 1479% | 0% |
<post_mortem>
<core_concepts>
griddynamics/rosetta, offered ONLY when a defect is attributed to Rosetta instructions. ② NEVER starts without explicit opt-in.</core_concepts>
<process>
① Post-mortem (always), strictly in this order:
② GitHub issue (only if ≥1 defect attributed to Rosetta instructions; otherwise state "nothing Rosetta-attributable" and stop):
<project>, <file>. Keep: Rosetta component, release, IDE/agent, model.Submit the issue as drafted. Question → answer it, then RE-ASK. Comment/edit → revise → re-OUTPUT full draft → RE-ASK. Unclear → ask directly "Approve submission, or revise?". Loop until sanctioned or declined.gh issue create --repo griddynamics/rosetta --title <t> --body <b>. gh missing/unauthed → hand user the draft + https://github.com/griddynamics/rosetta/issues/new; NO other channels.</process>
<validation_checklist>
</validation_checklist>
<pitfalls>
</pitfalls>
<templates>
Post-mortem report:
md## Post-Mortem **Component:** {skill|agent|workflow|prompt|file} · **Release:** {r2|r3} · **Delivery:** {MCP|plugin} · **IDE/agent:** {name} · **Model:** {id} **Task:** {one line} · **Outcome:** {completed | partial | blocked} **Issues** (repeat per issue) P{0-3} — {title} - Layer: {prompt | workspace files | local config | Rosetta instruction | tooling} - Trigger: {minimal repro — request paraphrase + preconditions} - Source: {prompt phrase / file section / instruction line — quoted} - Expected vs actual: {intent vs what agent did} - Root cause: {ambiguity | gap | contradiction | missing gate | stale data — why} - Reasons: {terse: why AI concluded this is the root cause — verbatim proof quotes from prompt/instructions/files/output} - Frequency: {always | intermittent | once} · Workaround: {if any} **Recommended Changes** - Local (user applies): {exact file/section + proposed content + failure mode prevented} - Rosetta (issue candidate): {exact instruction file/section + proposed generalized rule + failure mode prevented} **Confidence:** {High|Medium|Low} — {why}
GitHub issue draft (Rosetta-attributed findings only):
mdTitle: [post-mortem] {component}: {root-cause summary} ## Environment {release} · {MCP|plugin} · {IDE/agent} · {model} ## Repro / trigger {sanitized minimal repro} ## Expected vs actual {quoted instruction line/section} vs {observed agent behavior} ## Root cause {ambiguity | gap | contradiction | missing gate — why} ## Reasons {terse: why this is the root cause — verbatim proof quotes from instructions/output, sanitized} ## Suggested change {exact file/section + proposed generalized rule + failure mode prevented} ## Severity P{0-3} · Frequency {always|intermittent|once} · Confidence {High|Medium|Low}
</templates>
</post_mortem>
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-08 | pass→pass | 8,586 | 13,309 | +55% | 1 | 1 | 0% | 1,347 | 4,050 | +201% | 0 | 0 | — |
case-01 | fail→pass | 13,788 | 9,383 | -32% | 1 | 1 | 0% | 407 | 3,730 | +816% | 0 | 0 | — |
case-02 | fail→fail | 21,051 | 3,553 | -83% | 1 | 1 | 0% | 3,530 | 2,271 | -36% | 0 | 0 | — |
case-03 | fail→fail | 24,805 | 4,031 | -84% | 1 | 1 | 0% | 4,054 | 2,344 | -42% | 0 | 0 | — |
case-04 | pass→fail | 8,413 | 14,378 | +71% | 1 | 1 | 0% | 1,535 | 4,343 | +183% | 0 | 0 | — |
case-05 | pass→fail | 10,473 | 12,894 | +23% | 1 | 1 | 0% | 1,831 | 4,446 | +143% | 0 | 0 | — |
case-06 | pass→fail | 8,911 | 10,544 | +18% | 1 | 1 | 0% | 1,617 | 3,928 | +143% | 0 | 0 | — |
case-07 | fail→pass | 2,865 | 9,637 | +236% | 1 | 1 | 0% | 409 | 3,402 | +732% | 0 | 0 | — |
case-09 | fail→fail | 8,057 | 7,440 | -8% | 1 | 1 | 0% | 1,158 | 3,406 | +194% | 0 | 0 | — |
case-10 | pass→pass | 10,967 | 21,711 | +98% | 1 | 1 | 0% | 1,599 | 6,016 | +276% | 0 | 0 | — |
case-11 | fail→pass | 25,837 | 18,562 | -28% | 1 | 1 | 0% | 1,141 | 5,339 | +368% | 0 | 0 | — |
case-12 | pass→pass | 10,743 | 23,861 | +122% | 1 | 1 | 0% | 1,210 | 6,273 | +418% | 0 | 0 | — |
case-18 | pass→pass | 3,922 | 10,437 | +166% | 1 | 1 | 0% | 634 | 3,981 | +528% | 0 | 0 | — |
case-13 | pass→fail | 19,752 | 5,595 | -72% | 1 | 1 | 0% | 2,935 | 2,554 | -13% | 0 | 0 | — |
case-14 | fail→fail | 7,633 | 9,210 | +21% | 1 | 1 | 0% | 1,198 | 3,578 | +199% | 0 | 0 | — |
case-15 | fail→pass | 6,322 | 8,272 | +31% | 1 | 1 | 0% | 1,093 | 3,341 | +206% | 0 | 0 | — |
case-16 | pass→pass | 4,673 | 9,789 | +109% | 1 | 1 | 0% | 875 | 3,890 | +345% | 0 | 0 | — |
case-17 | fail→fail | 4,314 | 3,608 | -16% | 1 | 1 | 0% | 711 | 2,624 | +269% | 0 | 0 | — |
case-19 | pass→pass | 7,699 | 3,249 | -58% | 1 | 1 | 0% | 1,276 | 2,530 | +98% | 0 | 0 | — |
case-20 | fail→fail | 10,734 | 6,774 | -37% | 1 | 1 | 0% | 1,454 | 2,804 | +93% | 0 | 0 | — |
case-21 | fail→pass | 2,900 | 13,425 | +363% | 1 | 1 | 0% | 284 | 4,483 | +1479% | 0 | 0 | — |
case-22 | fail→fail | 14,710 | 16,346 | +11% | 1 | 1 | 0% | 2,171 | 4,677 | +115% | 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 -14 percentage points is the difference between those two pass rates over the 17 comparable cases. 5 cases got worse with the skill loaded, and they are included in that figure.
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.