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Get Started Free →Use NTM as an optional pane adapter for
.claude/skills/boshu2-ntm/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | 108% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -2% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -18% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 153% | 0% |
| case-16 | ✗→✓ | ▲ Improved | -19% | 0% |
NTM hosts explicit agent roles in persistent panes. It is transport, not an AgentOps lifecycle controller. The caller chooses the panes, roles, commands, write scopes, and stopping point.
Robot surfaces work because they report what the pane is doing, not what was sent to it; a dispatch layer that only proved delivery would let every dead worker look busy.
Judge pane liveness by the truth-stack, strongest first: new artifacts on disk, then transcript growth, then robot state and attention flags, then bare process existence. A successful prompt send sits below all of these and proves nothing about work.
Named failure mode — kill-the-witness: restarting a stuck pane before capturing its state, destroying the only evidence of why it stalled.
Anti-pattern: restarting an unresponsive pane as the default remedy. Corrective: rescue before restart — snapshot robot state and transcript, attempt a nudge, and restart only when the truth-stack shows no liveness at any level.
ntm --help,ntm --robot-capabilities, and ntm --robot-snapshot before unfamiliar actions.
failed command, validate a candidate, integrate changes, or decide what runs next.
or delivery authority.
isolation the repository requires. NTM does not infer safe concurrency.
cannot change an RPI phase result or semantic verdict. The adapter cannot select AgentOps semantics, issue a binding verdict, or turn factory completion into delivery or validation proof.
caller's observation window ends.
For a software-factory layout, the caller may name producer, tester, validator, or integrator panes. The same identity rule still applies: a validator for a candidate must have a distinct context identity from its author. Merely placing two roles in different panes is a declared runtime fact, not proof of semantic independence. Mixed-model judgment panes follow the agent-native model-dispatch recipe (probe, disclose, never claude -p).
Return:
Terminal outcomes are explicit, never a silent hang:
ntm absent or the robot surface unreachable: report itand stop; do not fall back to blind key injection or assume the pane is idle.
and transcript reference; the window ending is a stop, not a failure verdict.
was and was not observed.
experiment; report which panes were created or left running so nothing is orphaned silently. NTM never retries or reaps on its own.
Prefer machine-readable robot surfaces for capability, snapshot, attention, tail, and pipeline observations. Use interactive key injection only when the caller explicitly requests an interactive action and no robot command provides the needed behavior.
External NTM documentation and examples remain the authority for command syntax; this skill owns only the AgentOps boundary above. NTM's CLI drifts across versions, so confirm the surface at runtime (ntm --version, ntm --robot-capabilities) rather than trusting a remembered command.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 7,165 | 6,874 | -4% | 1 | 1 | 0% | 371 | 1,341 | +261% | 0 | 0 | — |
case-02 | fail→fail | 7,839 | 6,013 | -23% | 1 | 1 | 0% | 409 | 1,284 | +214% | 0 | 0 | — |
case-03 | fail→fail | 5,393 | 42,973 | +697% | 1 | 1 | 0% | 227 | 1,618 | +613% | 0 | 0 | — |
case-04 | pass→pass | 10,288 | 5,717 | -44% | 1 | 1 | 0% | 1,606 | 1,911 | +19% | 0 | 0 | — |
case-05 | fail→pass | 6,206 | 5,524 | -11% | 1 | 1 | 0% | 909 | 1,888 | +108% | 0 | 0 | — |
case-06 | fail→pass | 10,729 | 4,358 | -59% | 1 | 1 | 0% | 1,663 | 1,632 | -2% | 0 | 0 | — |
case-07 | pass→pass | 8,746 | 3,429 | -61% | 1 | 1 | 0% | 1,307 | 1,457 | +11% | 0 | 0 | — |
case-08 | pass→pass | 9,511 | 3,837 | -60% | 1 | 1 | 0% | 1,557 | 1,584 | +2% | 0 | 0 | — |
case-09 | pass→pass | 10,116 | 3,770 | -63% | 1 | 1 | 0% | 1,602 | 1,536 | -4% | 0 | 0 | — |
case-10 | fail→pass | 10,537 | 2,829 | -73% | 1 | 1 | 0% | 1,692 | 1,395 | -18% | 0 | 0 | — |
case-11 | pass→pass | 11,751 | 3,171 | -73% | 1 | 1 | 0% | 1,757 | 1,513 | -14% | 0 | 0 | — |
case-12 | pass→pass | 12,534 | 5,439 | -57% | 1 | 1 | 0% | 1,951 | 1,867 | -4% | 0 | 0 | — |
case-13 | pass→pass | 12,202 | 3,655 | -70% | 1 | 1 | 0% | 1,886 | 1,431 | -24% | 0 | 0 | — |
case-14 | fail→pass | 3,992 | 3,522 | -12% | 1 | 1 | 0% | 590 | 1,492 | +153% | 0 | 0 | — |
case-15 | pass→pass | 11,798 | 3,468 | -71% | 1 | 1 | 0% | 1,805 | 1,415 | -22% | 0 | 0 | — |
case-16 | fail→pass | 11,642 | 2,829 | -76% | 1 | 1 | 0% | 1,729 | 1,401 | -19% | 0 | 0 | — |
case-17 | pass→pass | 13,540 | 5,494 | -59% | 1 | 1 | 0% | 2,267 | 1,867 | -18% | 0 | 0 | — |
case-18 | pass→pass | 13,254 | 3,890 | -71% | 1 | 1 | 0% | 1,972 | 1,543 | -22% | 0 | 0 | — |
case-19 | pass→pass | 9,793 | 4,179 | -57% | 1 | 1 | 0% | 1,431 | 1,623 | +13% | 0 | 0 | — |
case-20 | pass→pass | 7,966 | 5,709 | -28% | 1 | 1 | 0% | 1,326 | 1,902 | +43% | 0 | 0 | — |
case-21 | pass→pass | 4,000 | 4,154 | +4% | 1 | 1 | 0% | 629 | 1,507 | +140% | 0 | 0 | — |
case-22 | pass→pass | 4,997 | 2,760 | -45% | 1 | 1 | 0% | 879 | 1,396 | +59% | 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 +23 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.