本文へ移動
cccskills
無料GitHub で公開

token-budget

Token budget tracking and enforcement for Gastown convoy-level execution. Hard limits with pre-execution checking, per-convoy and per-agent tracking, structured stop reasons.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md6.4 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Token Budget Enforcement

Pre-execution budget gating for multi-agent convoy execution. Prevents token overspend by checking budgets BEFORE API calls, not after. Identified by the 12 Primitives analysis (Primitive 5) as the #1 actionable improvement.

Activation

This skill activates when:

  • A convoy execution starts (mayor creates a convoy)
  • Agents are spawned within a convoy
  • Any agent is about to make an API call during convoy execution
  • Budget reporting is requested during or after execution

Architecture

Budget Hierarchy

Convoy Budget (hard limit, default 500K tokens)
  |
  +-- Agent A budget (hard limit, default 100K tokens)
  +-- Agent B budget (hard limit, default 100K tokens)
  +-- Agent C budget (hard limit, default 100K tokens)

The convoy budget is the aggregate ceiling. Individual agent budgets prevent any single polecat from consuming a disproportionate share.

Check-Before-Execute Pattern

Every API call in a convoy MUST follow this sequence:

  1. Estimate the projected token cost for the call
  2. Check checkBudget(budget, agentId, projectedCost) — returns BudgetCheckResult
  3. If allowed: false — stop immediately, do NOT make the API call
  4. If reason: 'warning_threshold' — proceed but log the warning
  5. If reason: 'ok' — proceed normally
  6. After execution — recordUsage(budget, agentId, actualInput, actualOutput)
  7. Persist — saveBudget(budget, budgetDir) to survive crashes

Structured Stop Reasons

ReasonMeaningAction
okUnder budget, no concernsProceed
warning_thresholdPast warning % but under hard limitProceed, log warning
convoy_budget_exceededConvoy would exceed hard limitSTOP, do not call API
agent_budget_exceededAgent would exceed its limitSTOP, do not call API

Core API

Types

interface TokenBudget {
  convoyId: string;
  maxTokensPerConvoy: number;      // Hard limit for entire convoy
  maxTokensPerAgent: number;       // Hard limit per polecat
  warningThresholdPercent: number;  // Warn at this % (e.g., 80)
  currentUsage: BudgetUsage;
  createdAt: string;               // ISO 8601
  updatedAt: string;               // ISO 8601
}

interface BudgetCheckResult {
  allowed: boolean;
  reason: 'ok' | 'warning_threshold' | 'convoy_budget_exceeded' | 'agent_budget_exceeded';
  remainingTokens: number;
  usagePercent: number;
}

Functions

FunctionSignatureDescription
createBudget(convoyId, config?) => TokenBudgetInitialize a budget for a convoy
checkBudget(budget, agentId, projectedCost) => BudgetCheckResultPre-execution gate check
recordUsage(budget, agentId, input, output) => voidTrack actual usage after execution
getBudgetReport(budget) => BudgetReportSummary for logging/display
saveBudget(budget, budgetDir) => Promise<void>Persist to .chipset/state/budgets/
loadBudget(convoyId, budgetDir) => Promise<TokenBudget | null>Load from disk
deleteBudget(convoyId, budgetDir) => Promise<void>Remove budget file
listBudgets(budgetDir) => Promise<string[]>List all persisted convoy budget IDs

Default Values

ParameterDefaultRecalibration note
maxTokensPerConvoy500,000 tokensWas set conservatively pre-INLINE-SERIAL pattern. v1.49.621 actuals: ~50-300K per convoy under inline-serial Opus/Sonnet authoring (~10-15% of this default). Default retained for safety margin; missions may explicitly cap lower based on convoy shape.
maxTokensPerAgent100,000 tokensPre-recursive-spawn-block default; with INLINE SERIAL the agent IS the convoy, so per-agent ≈ per-convoy.
warningThresholdPercent80%Unchanged.

v1.49.621 retrospective lesson 3 — projection recalibration: Wave 1+2+3+4 fleet token spend came in at ~26% of projected ceiling under INLINE SERIAL authoring. Future missions should project Opus convoys at ~75-300K and Sonnet convoys at ~30-150K based on output volume × ~1.5K tokens/100-line-of-output heuristic. Reserve 3-5× headroom over the projection for safety; do not 10× as this skill historically did.

State Persistence

Path: .chipset/state/budgets/{convoyId}.json

Follows the same durability contract as beads-state:

  • Atomic writes (write temp -> fsync -> rename)
  • JSON with sorted keys for git-friendly diffs
  • Filesystem-only, no database dependencies
  • Crash-recoverable (partial writes leave only temp files)

Integration Points

Mayor Coordinator

When the mayor creates a convoy, it should also create a token budget:

const convoy = await stateManager.createConvoy('Sprint 1', beadIds);
const budget = createBudget(convoy.id, {
  maxTokensPerConvoy: 500_000,
  maxTokensPerAgent: 100_000,
});
await saveBudget(budget, '.chipset/state/budgets');

Polecat Worker

Before each API call in GUPP autonomous mode:

const budget = await loadBudget(convoyId, '.chipset/state/budgets');
const check = checkBudget(budget!, agentId, estimatedTokens);
if (!check.allowed) {
  // Structured stop — include reason in termination message
  return { stopped: true, reason: check.reason, remaining: check.remainingTokens };
}
// ... make API call ...
recordUsage(budget!, agentId, actualInput, actualOutput);
await saveBudget(budget!, '.chipset/state/budgets');

Witness Observer

The witness can periodically check budget health:

const budget = await loadBudget(convoyId, '.chipset/state/budgets');
const report = getBudgetReport(budget!);
if (report.warningActive) {
  // Alert: convoy approaching budget limit
}

Module Location

  • Implementation: src/chipset/gastown/token-budget.ts
  • Tests: src/chipset/gastown/token-budget.test.ts
  • Barrel export: src/chipset/gastown/index.ts

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Provides web accessibility best practices for semantic HTML, ARIA, keyboard navigation, color contrast, and screen reader patterns. Use when building UI components, reviewing accessibility, or when user mentions 'a11y', 'accessibility', 'ARIA', 'screen reader', 'keyboard navigation', 'WCAG'.

日本語の概要は準備中です。原文の説明を表示しています。

Tibsfox/gsd-skill-creator712026年7月20日 更新

Active listening techniques for effective communication. Covers attending behaviors, paraphrasing, reflective listening, clarifying questions, empathic response, barriers to listening, listening in conflict, and cross-cultural listening. Use when building listening skills, improving understanding in conversation, mediating disputes, or analyzing communication breakdowns.

日本語の概要は準備中です。原文の説明を表示しています。

Tibsfox/gsd-skill-creator712026年7月20日 更新

Adversarial spec-compliance PR review — cross-references diffs against approved specs, verifies runtime claims against source, detects competing PRs, audits scope/convention compliance. Use before merging.

日本語の概要は準備中です。原文の説明を表示しています。

Tibsfox/gsd-skill-creator712026年7月20日 更新

Provides best practices for AI agent orchestration including MCP servers, A2A protocol, multi-agent coordination, and swarm architectures. Use when designing agent systems, configuring MCP servers, setting up agent teams, or when user mentions 'MCP', 'A2A', 'agent orchestration', 'multi-agent', 'swarm', 'agent team', 'LangGraph', 'CrewAI', 'AutoGen'.

日本語の概要は準備中です。原文の説明を表示しています。

Tibsfox/gsd-skill-creator712026年7月20日 更新

Symbolic manipulation, equation solving, and algebraic structures for mathematical reasoning. Covers distributive law, factoring, completing the square, linear through polynomial equation solving, systems of equations (substitution, elimination, Gaussian elimination, matrix methods), algebraic structures (groups, rings, fields), modular arithmetic, polynomial theory, and inequalities. Use when solving equations, simplifying expressions, working with algebraic structures, or performing symbolic manipulation.

日本語の概要は準備中です。原文の説明を表示しています。

Tibsfox/gsd-skill-creator712026年7月20日 更新

Understanding how algorithmic systems shape what users see, know, and do -- from recommendation feeds to search ranking to credit scoring to hiring software. Covers the mechanics of recommendation systems, algorithmic bias and its sources, personalization's effects on information diets, opacity and accountability, AI limitations (hallucination, confident wrongness), and the human-in-the-loop question. Use when a learner needs to think critically about why particular content reached them.

日本語の概要は準備中です。原文の説明を表示しています。

Tibsfox/gsd-skill-creator712026年7月20日 更新

Tibsfox のスキルをすべて見る

このスキルの問題を報告する