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Produces an aggregated markdown performance report from stored trace data — tool call metrics, token usage, cost, bottlenecks, and numbered recommendations. Use when: "session cost report", "what ran this week", "why did this spike", "end-of-sprint performance review", "budget alert fired".
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决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
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npx skills add https://github.com/DojoGenesis/gateway --skill agent-performance-report命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Produces a dispatch plan — isolation model, agent count, sequencing, and model assignments — for multi-agent parallel work. Use when: "dispatch agents in parallel", "run multiple tracks simultaneously", "plan parallel agent strategy", "should this be foreground or background".
Produces a structured background-dispatch handoff — task description, output contract, success criteria, and timeout — so long-running work completes without blocking the user. Use when: "run this in the background", "dispatch agent for long task", "I need this to run async", "parallel agent dispatch".
Routes health-audit findings to the correct execution path — main thread, foreground fix, or parallel agent waves. Use when health-audit findings need to be sorted by effort and dispatched as agent waves.
基于 SOC 职业分类
正在显示 SKILL.md
| name | agent-performance-report |
| model | sonnet |
| description | Produces an aggregated markdown performance report from stored trace data — tool call metrics, token usage, cost, bottlenecks, and numbered recommendations. Use when: "session cost report", "what ran this week", "why did this spike", "end-of-sprint performance review", "budget alert fired". |
| license | proprietary |
| category | system-health |
| triggers | ["session cost report","what ran this week","why did this spike","end-of-sprint performance review","budget alert fired"] |
| tier | 1 |
| agents | ["primary"] |
| tool_dependencies | ["file_system"] |
| inputs | [{"name":"time_period","type":"string","description":"Time period for the report (e.g. this week, this sprint, today)","required":false}] |
| outputs | [{"name":"performance_report","type":"string","description":"Aggregated markdown performance report with tool call metrics, token usage, cost, bottlenecks, and numbered recommendations"}] |
A performance report is an accountability artifact. It converts the raw trace data accumulated during agent operation into a human-readable narrative that answers three questions: what ran, how well did it run, and what should change.
The value of this report is not comprehensiveness — it is actionability. A report that surfaces five specific recommendations is more useful than a report that lists every span in the trace store. Structure the output so a non-technical reviewer can understand cost trends and a technical reviewer can locate bottlenecks without scrolling.
Use this skill:
Do not use this skill for real-time monitoring during an active session. For
live tool call visibility, use tool-intercept-logger. This skill operates on
stored, completed trace data.
Step 1 — Accept time range and filters.
Accept the following inputs:
time_range: required. ISO 8601 start/end pair (e.g., 2026-04-01T00:00:00Z
to 2026-04-05T23:59:59Z). If only a start is given, end defaults to now.
Convenience shortcuts: "today", "last_7_days", "this_month".agent_filter: optional. Restrict to spans generated by a specific agent ID.user_filter: optional. Restrict to spans for a specific user ID.tool_namespace: optional. Restrict tool-level metrics to a namespace prefix
(e.g., mcp_by_dojo:*).If no filters are provided, report covers all agents and users for the time range.
Step 2 — Query stored spans.
Query TraceStorage for all spans within the time range, applying any filters.
Each span contains: SpanID, TraceID, ParentID, Name, StartTime,
, , , , .
EndTimeInputsOutputsMetadataStatusAlso query for orchestration lifecycle events during the same range:
orchestration_plan_created — provides plan_id, node_count,
estimated_costorchestration_node_end — provides duration_ms, state per nodeorchestration_complete — provides total_nodes, success_nodes,
failed_nodes per planQuery BudgetTracker for usage snapshots at the start and end of the time
range to calculate net consumption per tier (query, session, monthly).
Step 3 — Aggregate tool call metrics.
For all spans where Name matches "node.<toolName>":
total_calls: <count>
success_rate: <success_nodes / total_nodes as %>
durations: <sorted list of duration_ms values>
avg_duration_ms: <mean>
p50_duration_ms: <median>
p95_duration_ms: <95th percentile>
calls_by_tool: <map of tool name -> call count>
Compute duration statistics per tool as well as globally.
Step 4 — Aggregate token and cost metrics.
From span Inputs, Outputs, and Metadata (which carries OTEL attributes
from the TraceLogger):
token_usage_by_tool: <map of tool name -> {input_tokens, output_tokens}>
token_usage_by_model: <map of model ID -> {input_tokens, output_tokens}>
total_input_tokens: <sum>
total_output_tokens: <sum>
cost_by_operation_type: <map of {web|compute|file|memory} -> estimated_cost>
total_cost: <sum of all estimated_cost values>
For cost breakdown, infer operation type from tool name using the same keyword
mapping as budget-guard: web/compute/file/memory.
Step 5 — Aggregate DAG execution stats.
From orchestration_plan_created and orchestration_complete events:
plans_created: <count>
avg_nodes_per_plan: <mean node_count across all plans>
total_successful_plans: <plans where failed_nodes = 0>
total_failed_plans: <plans where failed_nodes > 0>
parallel_ratio: <nodes with siblings at same DAG depth / total nodes>
sequential_ratio: <1 - parallel_ratio>
avg_plan_cost_estimate: <mean estimated_cost from plan_created events>
parallel_ratio requires reconstructing DAG structure from ParentID
relationships in span data. If span data is insufficient to reconstruct the
DAG, report this metric as "unavailable (insufficient span data)" rather
than approximating.
Step 6 — Identify performance issues.
Apply four detection rules:
Slow tools: tools where p95 duration > 2x the median p95 duration across all tools. List each with its p95, median, and percent above threshold.
Expensive operations: top 5 operations by estimated cost. Include tool name, call count, total cost, and cost per call.
Failed nodes: for each plan with failed_nodes > 0, list the failing
span names, their Status values, and the error_message from span
Metadata. Group repeated errors by pattern rather than listing each instance.
Budget utilization trend: compare BudgetTracker usage at start vs. end of the time range for each tier. Flag any tier where utilization increased by more than 20 percentage points within the range.
Step 7 — Generate markdown report.
Structure the report as follows:
# Agent Performance Report
Period: <time_range>
Filters: <agent|user|namespace if applied, else "none">
Generated: <ISO 8601 timestamp>
## Summary
<3-5 sentence narrative: total activity, overall cost, success rate, notable
issues. Written for a non-technical reader.>
## Tool Call Metrics
<table: tool name | calls | success rate | avg ms | p50 ms | p95 ms>
## Token and Cost Breakdown
<table: tool name | input tokens | output tokens | estimated cost>
<table: operation type | tokens | cost | % of total>
## DAG Execution
<table: metric | value>
## Performance Issues
### Slow Tools
<list or "none detected">
### Most Expensive Operations
<top 5 table>
### Failed Nodes
<grouped error patterns or "none detected">
### Budget Utilization
<per-tier trend or "within normal range">
## Recommendations
<numbered list of 3-5 specific, actionable recommendations based on findings>
Step 8 — Output.
Return the formatted markdown report. If the report exceeds 300 lines, offer a condensed version (summary + top issues + recommendations only) alongside the full version.
web_search with a cached lookup for
the 3 queries called more than 10 times this week" is a recommendation."connection timeout on mcp_by_dojo:github_search" is one pattern, not five
findings.Before completing this skill, verify:
Scenario 1: "Give me a performance report for last week" → Full markdown report covering all agents for the prior 7-day window, with tool latency tables, cost breakdown by operation type, and 5 actionable recommendations. Scenario 2: "Why did costs spike on April 3rd?" → Report filtered to 2026-04-03, identifying the top 5 expensive operations, any failed nodes that triggered retries, and the binding budget tier.
tool-intercept-logger instead, which captures live per-call data.