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Get Started Free →Create a new proactive background mission for the Nexus agent. A mission is a scheduled, autonomous task that the agent executes on a cron schedule without user interaction. Use this skill when the user asks to monitor something automatically, run a recurring check, or set up a background task. Keywords: mission, create, schedule, monitor, background, recurring, automatic, proactive, cron, periodic.
.claude/skills/majiayu000-create-mission/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | -20% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 94% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -33% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -10% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 11% | 0% |
Creates a new proactive background mission that the Nexus agent will execute autonomously on a schedule.
Before applying this skill, verify:
Extract from the user's request:
yamltitle: Short human-readable title (max 80 chars) goal: Natural language description of what the agent should do schedule: Cron expression (default: "0 * * * *" = every hour) mcp_policy: "nexus" for safe missions, "nexus-proactive" for cluster access max_tool_calls: 1-50 (default: 20) notify_on: ["anomaly", "error"] — when to notify the user
Schedule reference: | Schedule | Cron Expression | |---|---| | Every 5 minutes | */5 * * * * | | Every 15 minutes | */15 * * * * | | Every 30 minutes | */30 * * * * | | Every hour | 0 * * * * | | Every 6 hours | 0 */6 * * * | | Twice daily | 0 9,21 * * * | | Daily at 9am | 0 9 * * * | | Weekly Monday | 0 9 * * 1 |
Policy reference:
nexus — memory, skills, fetch (safe, no cluster access)nexus-proactive — adds kubernetes, discord, temporal (for monitoring missions)notify_on options:
always — notify after every runanomaly — notify when the agent flags something unusualerror — notify when the mission failscompletion — notify when the mission completesnever — silent modeCall the create_mission_activity Temporal activity:
pythonresult = await temporal_client.execute_activity( "create_mission_activity", { "user_id": user_id, "title": title, "goal": goal, "schedule": schedule, "mcp_policy": mcp_policy, "max_tool_calls": max_tool_calls, "notify_on": notify_on, } )
Respond with a confirmation that includes:
The skill succeeds when:
If creation fails:
User request: "Monitor my cluster health every 30 minutes and alert me if anything is wrong"
Extracted parameters:
json{ "title": "Cluster health monitor", "goal": "Check the overall health of the Kubernetes cluster. Look for pods in CrashLoopBackOff or Error state, nodes with high resource usage, and any recent events that indicate problems. Flag anything that requires attention.", "schedule": "*/30 * * * *", "mcp_policy": "nexus-proactive", "max_tool_calls": 15, "notify_on": ["anomaly", "error"] }
Response to user:
✅ Mission created: **Cluster health monitor** (mission-abc123)
The agent will check your cluster health every 30 minutes, starting at 14:30.
You'll be notified if any anomalies are found or if the mission encounters an error.
To manage this mission:
- Pause: "pause mission mission-abc123"
- View history: "show mission history for mission-abc123"
- Delete: "delete mission mission-abc123"| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-09 | fail→pass | 13,118 | 5,451 | -58% | 1 | 1 | 0% | 2,440 | 1,944 | -20% | 0 | 0 | — |
case-02 | fail→pass | 6,856 | 5,586 | -19% | 1 | 1 | 0% | 1,028 | 1,995 | +94% | 0 | 0 | — |
case-01 | fail→fail | 16,373 | 4,860 | -70% | 1 | 1 | 0% | 3,138 | 1,824 | -42% | 0 | 0 | — |
case-03 | fail→fail | 17,617 | 5,273 | -70% | 1 | 1 | 0% | 3,178 | 1,922 | -40% | 0 | 0 | — |
case-04 | pass→pass | 5,740 | 3,295 | -43% | 1 | 1 | 0% | 879 | 1,524 | +73% | 0 | 0 | — |
case-05 | pass→pass | 4,479 | 10,420 | +133% | 1 | 1 | 0% | 735 | 2,549 | +247% | 0 | 0 | — |
case-06 | pass→pass | 9,949 | 5,797 | -42% | 1 | 1 | 0% | 1,683 | 2,167 | +29% | 0 | 0 | — |
case-07 | fail→fail | 14,539 | 7,846 | -46% | 1 | 1 | 0% | 2,569 | 2,381 | -7% | 0 | 0 | — |
case-08 | fail→fail | 7,224 | 8,186 | +13% | 1 | 1 | 0% | 1,345 | 2,402 | +79% | 0 | 0 | — |
case-10 | fail→pass | 15,148 | 5,240 | -65% | 1 | 1 | 0% | 2,815 | 1,897 | -33% | 0 | 0 | — |
case-11 | fail→pass | 11,775 | 5,137 | -56% | 1 | 1 | 0% | 2,103 | 1,902 | -10% | 0 | 0 | — |
case-12 | fail→pass | 11,847 | 7,583 | -36% | 1 | 1 | 0% | 1,997 | 2,219 | +11% | 0 | 0 | — |
case-13 | fail→pass | 12,208 | 5,013 | -59% | 1 | 1 | 0% | 688 | 1,849 | +169% | 0 | 0 | — |
case-14 | fail→fail | 4,567 | 4,715 | +3% | 1 | 1 | 0% | 835 | 1,861 | +123% | 0 | 0 | — |
case-15 | fail→fail | 11,385 | 6,876 | -40% | 1 | 1 | 0% | 2,032 | 2,173 | +7% | 0 | 0 | — |
case-16 | fail→pass | 17,956 | 3,994 | -78% | 1 | 1 | 0% | 1,730 | 1,747 | +1% | 0 | 0 | — |
case-17 | fail→fail | 17,108 | 4,716 | -72% | 1 | 1 | 0% | 2,942 | 1,840 | -37% | 0 | 0 | — |
case-18 | pass→pass | 16,978 | 3,601 | -79% | 1 | 1 | 0% | 3,014 | 1,599 | -47% | 0 | 0 | — |
case-19 | fail→pass | 11,585 | 6,131 | -47% | 1 | 1 | 0% | 2,025 | 2,024 | -0% | 0 | 0 | — |
case-20 | fail→pass | 13,281 | 4,022 | -70% | 1 | 1 | 0% | 2,310 | 1,722 | -25% | 0 | 0 | — |
case-21 | fail→pass | 16,892 | 4,230 | -75% | 1 | 1 | 0% | 3,180 | 1,699 | -47% | 0 | 0 | — |
case-22 | pass→pass | 15,972 | 6,397 | -60% | 1 | 1 | 0% | 2,707 | 2,000 | -26% | 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 21 counted toward the lift figure. The other 1 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 21 comparable cases. 1 case got worse with the skill loaded, and it is 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.