Skip to content
cccskills
FreePublished on GitHub

cost-accrual-tracker

Track real-time API cost accrual during LLM execution. Activate on 'cost tracking', 'token usage', 'API costs', 'budget monitoring', 'usage metrics'. NOT for cost estimation, pricing tiers, or billing systems.

The English summary is being prepared. Showing the original description.

See how to install

Files included (2)

  • SKILL.md9.4 KB
  • references/claude-api-pricing.md5.0 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Cost Accrual Tracker

Real-time tracking of API costs during LLM execution with support for partial costs on abort.

When to Use

✅ Use for:

  • Implementing real-time cost tracking during execution
  • Capturing partial costs when executions are aborted
  • Building cost display widgets for execution UIs
  • Integrating token counting into execution pipelines
  • Adding budget thresholds with auto-stop

❌ NOT for:

  • Cost estimation before execution (use pricing calculators)
  • Billing system design (use billing-system skill)
  • Price tier management or discounts
  • Historical cost analytics dashboards

Core Patterns

1. Token-Based Cost Calculation

interface TokenUsage {
  inputTokens: number;
  outputTokens: number;
  cacheReadTokens?: number;   // Prompt caching hits
  cacheWriteTokens?: number;  // Prompt caching misses
}

interface CostCalculation {
  inputCostUsd: number;
  outputCostUsd: number;
  cacheSavingsUsd?: number;
  totalCostUsd: number;
}

function calculateCost(usage: TokenUsage, model: string): CostCalculation {
  const pricing = MODEL_PRICING[model];

  const inputCostUsd = (usage.inputTokens / 1_000_000) * pricing.inputPerMTok;
  const outputCostUsd = (usage.outputTokens / 1_000_000) * pricing.outputPerMTok;

  return {
    inputCostUsd,
    outputCostUsd,
    totalCostUsd: inputCostUsd + outputCostUsd,
  };
}

2. Incremental Accrual Pattern

Track costs as they accrue, not just at completion:

class CostAccrualTracker {
  private totalInputTokens = 0;
  private totalOutputTokens = 0;
  private accruedCostUsd = 0;
  private readonly model: string;

  constructor(model: string) {
    this.model = model;
  }

  /**
   * Called after each API response (streaming or complete)
   */
  recordUsage(usage: TokenUsage): void {
    this.totalInputTokens += usage.inputTokens;
    this.totalOutputTokens += usage.outputTokens;

    const cost = calculateCost(usage, this.model);
    this.accruedCostUsd += cost.totalCostUsd;
  }

  /**
   * Get current accrued cost (for real-time display)
   */
  getCurrentCost(): number {
    return this.accruedCostUsd;
  }

  /**
   * Finalize on completion or abort
   */
  finalize(reason: 'completed' | 'aborted' | 'failed'): CostReport {
    return {
      totalInputTokens: this.totalInputTokens,
      totalOutputTokens: this.totalOutputTokens,
      totalCostUsd: this.accruedCostUsd,
      completionReason: reason,
      finalizedAt: Date.now(),
    };
  }
}

3. Abort-Aware Cost Capture

Critical: Always capture partial costs on abort:

// In execution handler
const tracker = new CostAccrualTracker(model);

try {
  for await (const chunk of executeStream(request)) {
    if (abortSignal.aborted) {
      // CRITICAL: Capture cost BEFORE throwing
      const partialCost = tracker.finalize('aborted');
      onCostUpdate(partialCost);
      throw new AbortError('Execution aborted');
    }

    tracker.recordUsage(chunk.usage);
    onCostUpdate(tracker.getCurrentCost());
  }

  return tracker.finalize('completed');
} catch (error) {
  if (error instanceof AbortError) {
    throw error; // Already handled
  }
  return tracker.finalize('failed');
}

4. Budget Threshold Pattern

Auto-stop execution when budget is exceeded:

interface BudgetConfig {
  maxCostUsd: number;
  warnAtPercentage: number;  // e.g., 0.8 for 80%
  onWarn?: (current: number, max: number) => void;
  onExceed?: (current: number, max: number) => void;
}

function createBudgetGuard(config: BudgetConfig) {
  return {
    check(currentCostUsd: number): 'ok' | 'warn' | 'exceed' {
      const percentage = currentCostUsd / config.maxCostUsd;

      if (percentage >= 1.0) {
        config.onExceed?.(currentCostUsd, config.maxCostUsd);
        return 'exceed';
      }

      if (percentage >= config.warnAtPercentage) {
        config.onWarn?.(currentCostUsd, config.maxCostUsd);
        return 'warn';
      }

      return 'ok';
    }
  };
}

Anti-Patterns

Lost Costs on Abort

Novice thinking: "Just throw an error when aborted"

Reality: If you don't capture costs before aborting, you lose:

  • Token usage data for partial execution
  • Accurate cost reporting for billing
  • Audit trail for debugging

Timeline: Always been an issue, but became critical with expensive models (GPT-4, Claude Opus)

Correct approach: Always call finalize() with partial data BEFORE throwing abort errors.

Polling Without Debounce

Novice thinking: "Poll cost endpoint every 100ms for real-time updates"

Reality:

  • Wastes bandwidth and CPU
  • Cost updates only happen after API responses
  • Polling faster than response rate is pointless

Correct approach: Poll at 1-2 second intervals, or use event-driven updates from the execution stream.

Ignoring Prompt Caching

Novice thinking: "Just multiply tokens by price per token"

Reality: Claude's prompt caching changes the cost model:

  • Cache reads are 90% cheaper
  • Cache writes cost extra on first use
  • Ignoring caching leads to inaccurate costs

Timeline:

  • Pre-2024: No caching, simple calculation
  • 2024+: Claude prompt caching requires separate tracking

Correct approach: Track cache_read_input_tokens and cache_creation_input_tokens separately.

Per-Request Cost Objects

Novice thinking: "Create new tracker for each request"

Reality: For DAG execution with multiple nodes:

  • Need aggregate cost across all nodes
  • Need to attribute costs to specific nodes
  • Need rollup for parent execution

Correct approach: Hierarchical tracking - per-node trackers that roll up to execution-level.

State Flow

                    ┌─────────────────────────────────────────┐
                    │           CostAccrualTracker            │
                    └─────────────────────────────────────────┘
                                        │
              ┌─────────────────────────┼─────────────────────────┐
              │                         │                         │
              ▼                         ▼                         ▼
    ┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
    │  recordUsage()  │     │ getCurrentCost()│     │   finalize()    │
    │                 │     │                 │     │                 │
    │ After each API  │     │ For real-time   │     │ On completion,  │
    │ response        │     │ display         │     │ abort, or fail  │
    └─────────────────┘     └─────────────────┘     └─────────────────┘
              │                         │                         │
              │                         │                         │
              ▼                         ▼                         ▼
    ┌─────────────────────────────────────────────────────────────────┐
    │                        CostReport                               │
    │  { inputTokens, outputTokens, totalCostUsd, completionReason }  │
    └─────────────────────────────────────────────────────────────────┘

UI Display Pattern

For real-time cost display in execution UIs:

// Poll every 2 seconds while executing
useEffect(() => {
  if (status !== 'running') return;

  const interval = setInterval(async () => {
    const response = await fetch(`/api/execute/${executionId}`);
    const data = await response.json();
    setAccruedCost(data.cost.accruedUsd);
    setTokens({
      input: data.cost.inputTokens,
      output: data.cost.outputTokens,
    });
  }, 2000);

  return () => clearInterval(interval);
}, [executionId, status]);

// Display format
<div className="cost-display">
  <span className="cost-amount">${accruedCost.toFixed(4)}</span>
  <span className="token-count">
    {tokens.input.toLocaleString()} in / {tokens.output.toLocaleString()} out
  </span>
</div>

Integration Points

ComponentResponsibility
CostAccrualTrackerPer-execution token counting and cost calculation
ExecutionManagerAggregates costs across DAG executions
BudgetGuardThreshold monitoring and auto-stop
/api/execute/:idExposes current cost via polling
Cost Display WidgetReal-time UI rendering

References

See /references/claude-api-pricing.md for current Claude API pricing.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.

The English summary is being prepared. Showing the original description.

curiositech/port-daddy3Updated Oct 19, 2026

Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

The English summary is being prepared. Showing the original description.

curiositech/port-daddy3Updated Oct 19, 2026

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

The English summary is being prepared. Showing the original description.

curiositech/port-daddy3Updated Oct 19, 2026

Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

The English summary is being prepared. Showing the original description.

curiositech/port-daddy3Updated Oct 19, 2026

Use for insight, reframing, contradiction, impasse, and anomaly-driven problem solving when execution effort no longer helps. NOT for routine optimization, error correction, or well-specified tasks with known solution paths.

The English summary is being prepared. Showing the original description.

curiositech/port-daddy3Updated Oct 19, 2026

Apply cognitive task analysis to expert work that depends on perceptual cues, branching judgment, and recurring monitoring loops. Use when decomposing expert capability into agent structure, simulation design, or validation interviews. NOT for ordinary step-by-step SOP capture or simple pipelines with no tacit cue layer.

The English summary is being prepared. Showing the original description.

curiositech/port-daddy3Updated Oct 19, 2026

See all skills by curiositech

Report a problem with this skill