Query cross-project usage analytics. Use when reviewing agent, skill, hook, or team performance across OrchestKit projects. Also replay sessions, estimate costs, and view model delegation trends.

Reference low

Auto-activated — this skill loads automatically when Claude detects matching context.

Connections

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Cross-Project Analytics

Query local analytics data from ~/.claude/analytics/. All data is local-only, privacy-safe (hashed project IDs, no PII).

Subcommands

Parse the user's argument to determine which report to show. If no argument provided, use AskUserQuestion to let them pick.

Subcommand	Description	Data Source	Reference
`agents`	Top agents by frequency, duration, model breakdown	`agent-usage.jsonl`	`$\{CLAUDE_SKILL_DIR\}/references/jq-queries.md`
`models`	Model delegation breakdown (opus/sonnet/haiku)	`agent-usage.jsonl`	`$\{CLAUDE_SKILL_DIR\}/references/jq-queries.md`
`skills`	Top skills by invocation count	`skill-usage.jsonl`	`$\{CLAUDE_SKILL_DIR\}/references/jq-queries.md`
`hooks`	Slowest hooks and failure rates	`hook-timing.jsonl`	`$\{CLAUDE_SKILL_DIR\}/references/jq-queries.md`
`teams`	Team spawn counts, idle time, task completions	`team-activity.jsonl`	`$\{CLAUDE_SKILL_DIR\}/references/jq-queries.md`
`session`	Replay a session timeline with tools, tokens, timing	CC session JSONL	`$\{CLAUDE_SKILL_DIR\}/references/session-replay.md`
`cost`	Token cost estimation with cache savings	`stats-cache.json`	`$\{CLAUDE_SKILL_DIR\}/references/cost-estimation.md`
`trends`	Daily activity, model delegation, peak hours	`stats-cache.json`	`$\{CLAUDE_SKILL_DIR\}/references/trends-analysis.md`
`summary`	Unified view of all categories	All files	`$\{CLAUDE_SKILL_DIR\}/references/jq-queries.md`

Quick Start Example

# Top agents with model breakdown
jq -s 'group_by(.agent) | map({agent: .[0].agent, count: length}) | sort_by(-.count)' ~/.claude/analytics/agent-usage.jsonl

# All-time token costs
jq '.modelUsage | to_entries | map({model: .key, input: .value.inputTokens, output: .value.outputTokens})' ~/.claude/stats-cache.json

agents, models, skills, hooks, teams, summary — Run the jq query from Read("$\{CLAUDE_SKILL_DIR\}/references/jq-queries.md") for the matching subcommand. Present results as a markdown table.

session — Follow the 4-step process in Read("$\{CLAUDE_SKILL_DIR\}/references/session-replay.md"): locate session file, resolve reference (latest/partial/full ID), parse JSONL, present timeline.

cost — Apply model-specific pricing from Read("$\{CLAUDE_SKILL_DIR\}/references/cost-estimation.md") to CC's stats-cache.json. Show per-model breakdown, totals, and cache savings.

trends — Follow the 4-step process in Read("$\{CLAUDE_SKILL_DIR\}/references/trends-analysis.md"): daily activity, model delegation, peak hours, all-time stats.

summary — Run all subcommands and present a unified view: total sessions, top 5 agents, top 5 skills, team activity, unique projects.

Data Files

Load Read("$\{CLAUDE_SKILL_DIR\}/references/data-locations.md") for complete data source documentation.

File	Contents
`agent-usage.jsonl`	Agent spawn events with model, duration, success
`skill-usage.jsonl`	Skill invocations
`hook-timing.jsonl`	Hook execution timing and failure rates
`session-summary.jsonl`	Session end summaries
`task-usage.jsonl`	Task completions
`team-activity.jsonl`	Team spawns and idle events

Rules

Each category has individual rule files in rules/ loaded on-demand:

Category	Rule	Impact	Key Pattern
Data Integrity	`$\{CLAUDE_SKILL_DIR\}/rules/data-privacy.md`	CRITICAL	Hash project IDs, never log PII, local-only
Cost & Tokens	`$\{CLAUDE_SKILL_DIR\}/rules/cost-calculation.md`	HIGH	Separate pricing per token type, cache savings
Performance	`$\{CLAUDE_SKILL_DIR\}/rules/large-file-streaming.md`	HIGH	Streaming jq for >50MB, rotation-aware queries
Visualization	`$\{CLAUDE_SKILL_DIR\}/rules/visualization-recharts.md`	HIGH	Recharts charts, ResponsiveContainer, tooltips
Visualization	`$\{CLAUDE_SKILL_DIR\}/rules/visualization-dashboards.md`	HIGH	Dashboard grids, stat cards, widget registry

Total: 5 rules across 4 categories

References

Reference	Contents
`$\{CLAUDE_SKILL_DIR\}/references/jq-queries.md`	Ready-to-run jq queries for all JSONL subcommands
`$\{CLAUDE_SKILL_DIR\}/references/session-replay.md`	Session JSONL parsing, timeline extraction, presentation
`$\{CLAUDE_SKILL_DIR\}/references/cost-estimation.md`	Pricing table, cost formula, daily cost queries
`$\{CLAUDE_SKILL_DIR\}/references/trends-analysis.md`	Daily activity, model delegation, peak hours queries
`$\{CLAUDE_SKILL_DIR\}/references/data-locations.md`	All data sources, file formats, CC session structure

Important Notes

All files are JSONL (newline-delimited JSON) format
For large files (>50MB), use streaming jq without -s — load Read("$\{CLAUDE_SKILL_DIR\}/rules/large-file-streaming.md")
Rotated files: <name>.<YYYY-MM>.jsonl — include for historical queries
team field only present during team/swarm sessions
pid is a 12-char SHA256 hash — irreversible, for grouping only

Output Format

Present results as clean markdown tables. Include counts, percentages, and averages. If a file doesn't exist, note that no data has been collected yet for that category.

ork:explore - Codebase exploration and analysis
ork:feedback - Capture user feedback
ork:remember - Store project knowledge
ork:doctor - Health check diagnostics

Rules (5)

Calculate token costs accurately by separating cache reads from regular input pricing — HIGH

Token Cost Calculation

Calculate accurate token costs using model-specific pricing with cache-aware formulas.

Incorrect — treating all tokens equally:

// WRONG: ignores cache pricing difference (10x cheaper for reads)
const cost = totalTokens / 1_000_000 * 5.00;

Correct — separate pricing per token type:

const mtok = 1_000_000;
const pricing = { input: 5.00, output: 25.00, cache_read: 0.50, cache_write: 6.25 };

const cost =
  (tokens.input / mtok) * pricing.input +
  (tokens.output / mtok) * pricing.output +
  (tokens.cache_read / mtok) * pricing.cache_read +
  (tokens.cache_write / mtok) * pricing.cache_write;

// Cache savings: what it would cost if cache reads were full-price input
const withoutCache =
  ((tokens.input + tokens.cache_read) / mtok) * pricing.input +
  (tokens.output / mtok) * pricing.output;

const savings = withoutCache - cost;

Key rules:

Always calculate 4 token types separately: input, output, cache_read, cache_write
Cache reads are 10x cheaper than regular input — this is the biggest cost factor
Show cache savings prominently — users want to know caching is working
When daily data only has total tokens (no split), estimate 70% input / 30% output
Use formatCost() from cost-estimator.ts for consistent formatting
Pricing is user-overridable via ~/.claude/orchestkit-pricing.json

Protect analytics data privacy by hashing identifiers and stripping sensitive fields — CRITICAL

Analytics Data Privacy

All analytics data must be local-only and privacy-safe. Never log PII or reversible identifiers.

Incorrect — logging raw paths and usernames:

// WRONG: raw project path is PII
appendAnalytics('agent-usage.jsonl', {
  project: process.env.CLAUDE_PROJECT_DIR,  // /Users/john/secret-project
  user: os.userInfo().username,              // john
  file: input.file_path,                     // /Users/john/secret-project/auth.ts
});

Correct — hashed identifiers, no PII:

// RIGHT: irreversible 12-char hash, no PII
appendAnalytics('agent-usage.jsonl', {
  ts: new Date().toISOString(),
  pid: hashProject(process.env.CLAUDE_PROJECT_DIR || ''),  // "a3f8b2c1d4e5"
  agent: agentType,       // "code-quality-reviewer" (not PII)
  model: modelName,       // "claude-opus-4-6" (not PII)
  duration_ms: durationMs,
  success: true,
});

Key rules:

Use hashProject() (12-char SHA256 truncation) for project identifiers — irreversible
Never log file paths, usernames, environment variables, or file contents
Agent names, skill names, and hook names are safe to log (not PII)
All data stays in ~/.claude/analytics/ — never transmitted externally
The team field uses team names (user-chosen), not paths

Stream large analytics files with jq instead of slurping to prevent OOM crashes — HIGH

Large File Streaming

Handle large JSONL files (>50MB) with streaming queries and rotation-aware patterns.

Incorrect — slurping large files into memory:

# WRONG: -s loads entire file into memory — OOM on 500MB file
jq -s 'group_by(.agent) | map({agent: .[0].agent, count: length})' ~/.claude/analytics/agent-usage.jsonl

Correct — streaming without slurp:

# RIGHT: stream-process line by line, then aggregate
jq -r '.agent' ~/.claude/analytics/agent-usage.jsonl | sort | uniq -c | sort -rn

# RIGHT: for complex aggregations, use reduce
jq -n '[inputs | .agent] | group_by(.) | map({agent: .[0], count: length}) | sort_by(-.count)' ~/.claude/analytics/agent-usage.jsonl

Including rotated files for historical queries:

# Rotated files follow pattern: <name>.<YYYY-MM>.jsonl
# Include all months for full history
jq -r '.agent' ~/.claude/analytics/agent-usage.*.jsonl ~/.claude/analytics/agent-usage.jsonl 2>/dev/null | sort | uniq -c | sort -rn

Key rules:

Check file size before querying: ls -lh the target file
Files >50MB: use streaming jq without -s (slurp) flag
Files <50MB: -s is fine for group_by operations
Include rotated files (*.YYYY-MM.jsonl) when user asks for historical data
For date-range queries, filter by ts field before aggregating

Design dashboard layouts with shared query keys and grid widgets for performance — HIGH

Dashboard Layout & Widgets

Build responsive dashboard grids with stat cards, widget composition, and real-time data patterns.

Incorrect — each widget fetches independently:

// WRONG: 5 widgets = 5 duplicate API calls
function Dashboard() {
  return (
    <div>
      <RevenueWidget /> {/* fetches /api/metrics */}
      <UsersWidget />   {/* fetches /api/metrics AGAIN */}
      <OrdersWidget />  {/* fetches /api/metrics AGAIN */}
    </div>
  );
}

Correct — shared query with responsive grid layout:

// Dashboard grid with responsive breakpoints
function DashboardGrid() {
  return (
    <div className="grid gap-4 grid-cols-1 sm:grid-cols-2 lg:grid-cols-4">
      <StatCard title="Revenue" value="$45,231" change="+12%" trend="up" />
      <StatCard title="Users" value="2,350" change="+5.2%" trend="up" />
      <StatCard title="Orders" value="1,234" change="-2.1%" trend="down" />
      <StatCard title="Conversion" value="3.2%" change="+0.4%" trend="up" />

      {/* Full-width chart spanning all columns */}
      <div className="col-span-full">
        <RevenueChart />
      </div>

      {/* Two-column layout for secondary charts */}
      <div className="col-span-1 lg:col-span-2">
        <TrafficChart />
      </div>
      <div className="col-span-1 lg:col-span-2">
        <TopProductsTable />
      </div>
    </div>
  );
}

// Stat card component
function StatCard({
  title, value, change, trend,
}: {
  title: string; value: string; change: string; trend: 'up' | 'down';
}) {
  return (
    <div className="rounded-lg border bg-card p-6">
      <p className="text-sm text-muted-foreground">{title}</p>
      <p className="text-2xl font-bold">{value}</p>
      <p className={trend === 'up' ? 'text-green-600' : 'text-red-600'}>
        {change}
      </p>
    </div>
  );
}

Widget registry pattern for dynamic dashboards:

const widgetRegistry: Record<string, React.ComponentType<WidgetProps>> = {
  'stat-card': StatCard,
  'line-chart': LineChartWidget,
  'bar-chart': BarChartWidget,
  'data-table': DataTableWidget,
};

function DynamicDashboard({ config }: { config: DashboardConfig }) {
  return (
    <div className="grid gap-4 grid-cols-12">
      {config.widgets.map((widget) => {
        const Widget = widgetRegistry[widget.type];
        return (
          <div key={widget.id} className={`col-span-${widget.colSpan}`}>
            <Suspense fallback={<WidgetSkeleton />}>
              <Widget {...widget.props} />
            </Suspense>
          </div>
        );
      })}
    </div>
  );
}

Real-time updates with SSE + TanStack Query:

function useRealtimeMetrics() {
  const queryClient = useQueryClient();

  useEffect(() => {
    const source = new EventSource('/api/metrics/stream');
    source.onmessage = (event) => {
      const metric = JSON.parse(event.data);
      // Update specific query, not entire dashboard
      queryClient.setQueryData(['metrics', metric.key], metric.value);
    };
    return () => source.close();
  }, [queryClient]);
}

Key rules:

Use CSS Grid with responsive breakpoints (grid-cols-1 sm:grid-cols-2 lg:grid-cols-4)
Share data via TanStack Query with granular query keys (not per-widget fetch)
Use col-span-full for full-width charts, col-span-2 for half-width
Skeleton loading for content areas during initial load
SSE for server-to-client real-time, WebSocket for bidirectional
Update specific query keys on real-time events, not entire cache

Configure Recharts with ResponsiveContainer and animation control for stable rendering — HIGH

Recharts Chart Components

Build Recharts 3.x chart components with responsive containers, custom tooltips, and accessibility.

Incorrect — chart without responsive container:

// WRONG: Fixed width, no container, animations on real-time data
function BrokenChart({ data }: { data: ChartData[] }) {
  return (
    <LineChart width={800} height={400} data={data}>
      {/* Fixed width overflows on mobile */}
      {/* Animation on every data update = jank */}
      <Line type="monotone" dataKey="value" />
    </LineChart>
  );
}

Correct — responsive chart with proper setup:

import {
  LineChart, Line, BarChart, Bar, PieChart, Pie, Cell,
  CartesianGrid, XAxis, YAxis, Tooltip, Legend,
  ResponsiveContainer, AreaChart, Area,
} from 'recharts';

// Line chart (trends over time)
function RevenueChart({ data }: { data: ChartData[] }) {
  return (
    <div className="h-[400px]"> {/* Parent MUST have height */}
      <ResponsiveContainer width="100%" height="100%">
        <LineChart data={data}>
          <CartesianGrid strokeDasharray="3 3" />
          <XAxis dataKey="date" />
          <YAxis />
          <Tooltip content={<CustomTooltip />} />
          <Legend />
          <Line
            type="monotone"
            dataKey="revenue"
            stroke="#8884d8"
            strokeWidth={2}
            dot={{ r: 4 }}
          />
        </LineChart>
      </ResponsiveContainer>
    </div>
  );
}

// Custom tooltip for branded UX
function CustomTooltip({ active, payload, label }: any) {
  if (!active || !payload?.length) return null;
  return (
    <div className="rounded-lg border bg-background p-3 shadow-md">
      <p className="font-medium">{label}</p>
      {payload.map((entry: any, i: number) => (
        <p key={i} style={{ color: entry.color }}>
          {entry.name}: {entry.value.toLocaleString()}
        </p>
      ))}
    </div>
  );
}

// Real-time chart: disable animations
function LiveMetricChart({ data }: { data: MetricData[] }) {
  return (
    <ResponsiveContainer width="100%" height={300}>
      <AreaChart data={data}>
        <Area
          type="monotone"
          dataKey="value"
          isAnimationActive={false}  // No animation on real-time data
          dot={false}                // No dots for performance
        />
      </AreaChart>
    </ResponsiveContainer>
  );
}

// Accessible chart with figure role
function AccessibleChart({ data, title }: { data: ChartData[]; title: string }) {
  return (
    <figure role="figure" aria-label={title}>
      <figcaption className="sr-only">{title}</figcaption>
      <ResponsiveContainer width="100%" height={400}>
        <BarChart data={data}>
          <Bar dataKey="value" fill="#8884d8" />
        </BarChart>
      </ResponsiveContainer>
    </figure>
  );
}

Chart type selection guide:

Chart	Component	Best For
Line	`LineChart`	Trends over time
Bar	`BarChart`	Comparisons between categories
Pie/Donut	`PieChart` with `innerRadius`	Proportions/percentages
Area	`AreaChart` with gradient	Volume over time

Key rules:

Always wrap charts in ResponsiveContainer with a parent that has explicit height
Disable animations on real-time/frequently-updating charts (isAnimationActive=\{false\})
Use custom tooltips for branded UX instead of default
Add figure role and aria-label for accessibility
Limit data points to prevent rendering performance issues
Memoize data calculations outside the render function

References (5)

Cost Estimation

Estimate token costs from CC's ~/.claude/stats-cache.json using model-specific pricing.

Pricing Table (Feb 2026)

Model	Input/MTok	Output/MTok	Cache Read/MTok	Cache Write/MTok
claude-opus-4-6	$5.00	$25.00	$0.50	$6.25
claude-sonnet-4-6	$3.00	$15.00	$0.30	$3.75
claude-haiku-4-5	$1.00	$5.00	$0.10	$1.25

Cost Formula

cost = (input_tokens / 1M * input_price)
     + (output_tokens / 1M * output_price)
     + (cache_read_tokens / 1M * cache_read_price)
     + (cache_write_tokens / 1M * cache_write_price)

Cache savings = cost if all cache reads were full-price input minus actual cost.

All-Time Model Usage Query

jq '.modelUsage | to_entries | map({
  model: .key,
  input: .value.inputTokens,
  output: .value.outputTokens,
  cache_read: .value.cacheReadInputTokens,
  cache_write: .value.cacheCreationInputTokens
})' ~/.claude/stats-cache.json

Daily Costs (Last 7 Days)

jq '.dailyModelTokens[-7:] | .[] | {date: .date, tokens: .tokensByModel}' ~/.claude/stats-cache.json

Note: dailyModelTokens only has total tokens per model, not split by type. Estimate with 70% input / 30% output ratio as a rough average for CC usage.

Presentation Format

## Token Cost Estimate

| Model | Input Tokens | Output Tokens | Cache Read | Cache Write | Est. Cost |
|-------|-------------|--------------|------------|-------------|-----------|
| claude-opus-4-6 | 5.2M | 1.4M | 42.0M | 2.1M | $16.20 |
| claude-sonnet-4-6 | 200K | 50K | -- | -- | $1.85 |
| **Total** | | | | | **$18.50** |

**Cache savings:** $8.20 (what it would cost without prompt caching)

### Daily Costs (Last 7 Days)
| Date | Est. Cost |
|------|-----------|
| Feb 12 | $2.10 |
| Feb 13 | $1.85 |
| **Total** | **$18.50** |

User-Overridable Config

Users can override pricing by creating ~/.claude/orchestkit-pricing.json — see src/hooks/src/lib/cost-estimator.ts for the schema.

Data Locations

Data Sources & File Locations

All analytics data sources used by the analytics skill.

OrchestKit Analytics Files

Location: ~/.claude/analytics/

File	Contents	Key Fields
`agent-usage.jsonl`	Agent spawn events	`ts, pid, agent, model, duration_ms, success, output_len, team?`
`skill-usage.jsonl`	Skill invocations	`ts, pid, skill, team?`
`hook-timing.jsonl`	Hook execution timing	`ts, hook, duration_ms, ok, pid, team?`
`session-summary.jsonl`	Session end summaries	`ts, pid, total_tools, team?`
`task-usage.jsonl`	Task completions	`ts, pid, task_status, duration_ms, team?`
`team-activity.jsonl`	Team spawns and idle	`ts, pid, event, agent, member?, idle_ms?, model?, team`

CC Native Data Sources

Source	Path	Contents
CC session logs	`~/.claude/projects/\{encoded-path\}/*.jsonl`	Full conversation with per-turn token usage
CC stats cache	`~/.claude/stats-cache.json`	Pre-aggregated daily model tokens, session counts
CC history	`~/.claude/history.jsonl`	Command history across all projects

JSONL Format Notes

All OrchestKit files use newline-delimited JSON (JSONL)
Each line is a self-contained JSON object
Rotated files follow pattern <name>.<YYYY-MM>.jsonl — include them in queries for historical data
The team field is only present for entries recorded during team/swarm sessions
pid is a 12-char SHA256 hash of the project path — irreversible, used for grouping

CC Session JSONL Structure

Each line in a CC session JSONL file is a JSON object. Key entry types:

Entry Pattern	How to Identify	Key Fields
Session metadata	Has `sessionId`, `gitBranch`, `version`	First entries in file
Assistant message	`.message.role == "assistant"`	`.message.content[]`, `.message.usage`
User message	`.message.role == "user"`	`.message.content`
Tool use	`.message.content[].type == "tool_use"`	`.name`, `.input`
Hook progress	`.type == "progress"` + `.data.type == "hook_progress"`	`.data.hookName`

Encoded Project Path

CC encodes project paths by replacing / with -:

/Users/foo/coding/bar becomes -Users-foo-coding-bar
The encoded path is the directory name under ~/.claude/projects/

Jq Queries

Analytics jq Queries

Ready-to-run jq queries for each analytics subcommand. All queries target ~/.claude/analytics/*.jsonl.

agents — Top agents by frequency and duration

jq -s 'group_by(.agent) | map({
  agent: .[0].agent,
  count: length,
  avg_ms: (map(.duration_ms // 0) | add / length | floor),
  success_rate: (map(select(.success)) | length) / length * 100 | floor,
  models: (group_by(.model) | map({model: .[0].model, count: length}) | sort_by(-.count))
}) | sort_by(-.count)' ~/.claude/analytics/agent-usage.jsonl

models — Model delegation breakdown

jq -s 'group_by(.model) | map({
  model: .[0].model,
  count: length,
  avg_ms: (map(.duration_ms // 0) | add / length | floor),
  agents: ([.[].agent] | unique)
}) | sort_by(-.count)' ~/.claude/analytics/agent-usage.jsonl

skills — Top skills by invocation count

jq -s 'group_by(.skill) | map({skill: .[0].skill, count: length}) | sort_by(-.count)' ~/.claude/analytics/skill-usage.jsonl

hooks — Slowest hooks and failure rates

jq -s 'group_by(.hook) | map({
  hook: .[0].hook,
  count: length,
  avg_ms: (map(.duration_ms) | add / length | floor),
  fail_rate: (map(select(.ok == false)) | length) / length * 100 | floor
}) | sort_by(-.avg_ms) | .[0:15]' ~/.claude/analytics/hook-timing.jsonl

teams — Team spawn counts, idle time, task completions

# Team activity (spawns + idle)
jq -s 'group_by(.team) | map({
  team: .[0].team,
  spawns: [.[] | select(.event == "spawn")] | length,
  idles: [.[] | select(.event == "idle")] | length,
  agents: [.[].agent] | unique
}) | sort_by(-.spawns)' ~/.claude/analytics/team-activity.jsonl

# Task completions by team
jq -s '[.[] | select(.team != null)] | group_by(.team) | map({
  team: .[0].team,
  tasks: length,
  avg_ms: (map(.duration_ms // 0) | add / length | floor)
})' ~/.claude/analytics/task-usage.jsonl

summary — Quick counts

# Total sessions (excluding zero-tool sessions)
jq -s '[.[] | select(.total_tools > 0)] | length' ~/.claude/analytics/session-summary.jsonl

# Line counts per file
wc -l ~/.claude/analytics/*.jsonl 2>/dev/null

# Unique projects
jq -r .pid ~/.claude/analytics/agent-usage.jsonl 2>/dev/null | sort -u | wc -l

Presentation Format

Present all results as clean markdown tables with counts, percentages, and averages. If a file doesn't exist, note that no data has been collected yet for that category.

Example output:

| Agent | Count | Avg Duration | Success Rate | Top Model |
|-------|-------|-------------|-------------|-----------|
| code-quality-reviewer | 45 | 8.2s | 98% | opus |
| test-generator | 32 | 12.1s | 94% | sonnet |

Session Replay

Parse and visualize CC session JSONL files to understand what happened in a session.

Usage

/ork:analytics session latest — most recent session
/ork:analytics session <partial-id> — match by prefix (e.g., 08ed1436)
/ork:analytics session <full-uuid> — exact match

Step 1: Locate the Session File

CC session logs live at ~/.claude/projects/\{encoded-project-path\}/.

The encoded path replaces / with - in the project directory path. Example: /Users/foo/coding/bar becomes -Users-foo-coding-bar

# Find project session dir
PROJECT_DIR=$(echo "$CLAUDE_PROJECT_DIR" | sed 's|/|-|g')
SESSION_DIR="$HOME/.claude/projects/$PROJECT_DIR"

# List recent sessions (newest first)
ls -t "$SESSION_DIR"/*.jsonl 2>/dev/null | head -5

# For "latest": use the first result
LATEST=$(ls -t "$SESSION_DIR"/*.jsonl 2>/dev/null | head -1)

Step 2: Resolve the Session Reference

latest — find the most recently modified .jsonl file in the project directory
Partial ID (e.g., 08ed1436) — find file starting with that prefix
Full UUID — exact match

Step 3: Parse JSONL and Extract Timeline

Each line is a JSON object. Key extraction patterns:

# Count messages by role
jq -r '.message.role // empty' "$SESSION_FILE" | sort | uniq -c | sort -rn

# Extract tool calls with timestamps
jq -r 'select(.message.role == "assistant") | .message.content[]? | select(.type == "tool_use") | .name' "$SESSION_FILE" | sort | uniq -c | sort -rn

# Sum token usage
jq -s '[.[].message.usage // empty | {
  i: .input_tokens, o: .output_tokens,
  cr: .cache_read_input_tokens, cw: .cache_creation_input_tokens
}] | {
  input: (map(.i) | add), output: (map(.o) | add),
  cache_read: (map(.cr) | add), cache_write: (map(.cw) | add)
}' "$SESSION_FILE"

# Get session metadata
jq -r 'select(.gitBranch) | .gitBranch' "$SESSION_FILE" | head -1
jq -r 'select(.version) | .version' "$SESSION_FILE" | head -1

# Get start/end timestamps
jq -r '.timestamp' "$SESSION_FILE" | head -1   # start
jq -r '.timestamp' "$SESSION_FILE" | tail -1   # end

# Count agent spawns by type
jq -r '.message.content[]? | select(.type == "tool_use" and .name == "Task") | .input.subagent_type' "$SESSION_FILE" | sort | uniq -c | sort -rn

Step 4: Present as Timeline

## Session: 08ed1436 — 2026-02-18 10:50 -> 11:35 (45min)
**Branch:** bugfix/windows-spawn | **CC Version:** 2.1.45
**Tokens:** 152K in, 38K out | **Cache hit rate:** 89%

### Timeline
| Time | Event | Details |
|------|-------|---------|
| 10:50:00 | SESSION START | branch: bugfix/windows-spawn |
| 10:50:01 | HOOK | SessionStart:startup |
| 10:50:05 | Read | src/hooks/bin/spawn-worker.mjs |
| 10:50:08 | Grep | "spawn" in src/ |
| 10:50:15 | Task (agent) | code-quality-reviewer |
| 10:51:00 | Edit | src/hooks/bin/spawn-worker.mjs |
| 10:52:30 | Bash | npm test -> 8.3s |
| 11:35:00 | SESSION END | 23 tool calls, 3 agents |

### Tool Usage
| Tool | Count |
|------|-------|
| Read | 12 |
| Edit | 5 |
| Bash | 4 |
| Task | 2 |

### Token Breakdown
| Metric | Value |
|--------|-------|
| Input tokens | 152,340 |
| Output tokens | 38,210 |
| Cache read | 1,245,000 |
| Cache write | 18,500 |
| Cache hit rate | 89% |

Trends Analysis

Show daily activity, model delegation trends, and cost patterns over time.

Usage

/ork:analytics trends — default 7 days
/ork:analytics trends 30 — last 30 days

Step 1: Daily Activity (sessions, messages, tool calls)

jq '.dailyActivity[-7:]' ~/.claude/stats-cache.json

Step 2: Daily Model Token Breakdown

jq '.dailyModelTokens[-7:] | .[] | {
  date: .date,
  models: (.tokensByModel | to_entries | map({model: .key, tokens: .value}) | sort_by(-.tokens))
}' ~/.claude/stats-cache.json

Step 3: Peak Productivity Hours

jq '.hourCounts | to_entries | sort_by(-.value) | .[0:5] | map({
  hour: (.key + ":00"),
  sessions: .value
})' ~/.claude/stats-cache.json

Step 4: All-Time Stats

jq '{
  totalSessions: .totalSessions,
  totalMessages: .totalMessages,
  longestSession: {
    id: .longestSession.sessionId,
    duration_min: (.longestSession.duration / 60000 | floor),
    messages: .longestSession.messageCount
  }
}' ~/.claude/stats-cache.json

Presentation Format

## Trends -- Last 7 Days

### Daily Activity
| Date | Sessions | Messages | Tools | Est. Cost |
|------|----------|----------|-------|-----------|
| Feb 12 | 6 | 1,200 | 450 | $2.10 |
| Feb 13 | 5 | 980 | 380 | $1.85 |
| ... | ... | ... | ... | ... |
| **Total** | **42** | **8,380** | **3,390** | **$18.50** |

### Model Delegation Trend
| Date | opus | sonnet | haiku |
|------|------|--------|-------|
| Feb 12 | 452K | 31K | -- |
| Feb 13 | 380K | 25K | 12K |
| ... | ... | ... | ... |

### Peak Productivity Hours
| Hour | Sessions |
|------|----------|
| 10:00 | 78 |
| 9:00 | 71 |
| 14:00 | 65 |

### All-Time Stats
- **Total sessions:** [N]
- **Total messages:** [N]
- **Longest session:** [id] -- [N] min, [N] messages

Cost Per Day

Apply pricing from references/cost-estimation.md to daily token counts:

Split daily tokens by model
Apply per-model pricing (70/30 input/output estimate for daily totals)
Show daily cost in the activity table

Analytics

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