budget-guard

name	budget-guard
model	sonnet
description	Produces a structured APPROVE / WARN / BLOCK decision by checking remaining token budget across query, session, and monthly tiers before an expensive operation runs. Use when: "is this operation within budget", "pre-flight check before web search", "budget alert fired", "before a multi-step pipeline", "session above 70% utilization".
license	proprietary
category	system-health
triggers	["is this operation within budget","pre-flight check before web search","budget alert fired","before a multi-step pipeline","session above 70% utilization"]
tier	1
agents	["primary"]
tool_dependencies	["file_system"]
inputs	[{"name":"operation_description","type":"string","description":"Description of the operation to check against remaining budget","required":true},{"name":"estimated_tokens","type":"number","description":"Estimated token count for the operation","required":false}]
outputs	[{"name":"budget_decision","type":"string","description":"Structured APPROVE/WARN/BLOCK decision with remaining budget across query, session, and monthly tiers"}]

I. Philosophy

Token budgets are not bureaucratic limits — they are the financial physics of agent operation. Ignoring them does not make the cost disappear; it makes the cost invisible until it becomes a crisis. Budget Guard makes cost visible before it is incurred, giving operators and users the information they need to make deliberate decisions rather than discovering overruns after the fact.

The three-tier structure (query, session, monthly) mirrors how costs actually accumulate: individual calls compound into sessions, sessions compound into monthly spend. A healthy system enforces all three, not just the monthly cap.

II. When to Use

Use this skill:

• Before any orchestration plan that includes web search, compute-heavy inference, file processing, or multi-step memory operations.
• When a user explicitly asks about remaining budget or cost of a planned action.
• When BudgetMiddleware response headers (X-Budget-Remaining, X-Budget-Tier) indicate the session is above 70% utilization.
• As a pre-flight check in any automated pipeline where token cost is variable and potentially unbounded.
• After an unexpected spike in usage to determine whether the monthly tier is at risk.

Do not use this skill as a post-hoc audit tool. It is a forward-looking gate, not a ledger review. For historical cost analysis, use agent-performance-report.

III. Workflow

Step 1 — Receive proposed action and estimated cost.

Accept two inputs from the caller:

• action_description: a plain-language description of the operation to be checked (e.g., "web search for competitive analysis across 10 URLs")
• estimated_tokens: the caller's raw token estimate before category adjustment (use EstimatePlanCost(plan) output if available from the orchestration engine)

If estimated_tokens is not provided, apply a conservative default based on action type: web operations default to 2000 tokens, compute operations to 3000, file operations to 1500, memory operations to 800.

Step 2 — Query remaining budget across all three tiers.

Call BudgetTracker.GetRemaining(userID) to retrieve:

query_remaining:   <tokens remaining in current query limit>
session_remaining: <tokens remaining in current session>
monthly_remaining: <tokens remaining this calendar month>
monthly_reset:     <ISO date of next monthly reset>

The binding limit is the minimum across all three tiers. A session approaching its limit is just as blocking as a monthly cap.

Step 3 — Apply category multiplier.

Determine the operation category from action_description using keyword matching:

Category	Keywords	Multiplier
web	"search", "fetch", "browse", "crawl", "URL"	1.4x
compute	"analyze", "summarize", "generate", "synthesize", "infer"	1.5x
file	"read", "write", "parse", "process", "upload", "download"	1.3x
memory	"store", "retrieve", "seed", "memory", "embed"	1.2x
default	(no keyword match)	1.0x

If an operation spans multiple categories, apply the highest multiplier. Do not stack multipliers.

Adjusted cost = estimated_tokens * multiplier

Step 4 — Apply decision matrix.

Calculate utilization for each tier as:

utilization = (tier_limit - tier_remaining) / tier_limit

For the binding tier (lowest remaining), apply:

Utilization	Decision	Action
< 70%	APPROVE	Proceed. Log decision and adjusted cost.
70% to 90%	WARN	Proceed with caution. Inform user of proximity to limit.
> 90%	BLOCK	Do not proceed. Require explicit user override.

A BLOCK decision cannot be overridden automatically. It requires a human confirmation step.

Step 5 — Output decision.

Return a structured decision to the caller:

Decision: APPROVE | WARN | BLOCK
Reason: <which tier is binding and why>

Adjusted cost estimate: <tokens> (base <raw_tokens> x <multiplier> for <category>)

Budget status:
  Query:   <remaining> remaining  [<utilization>% used]
  Session: <remaining> remaining  [<utilization>% used]
  Monthly: <remaining> remaining  [<utilization>% used] — resets <monthly_reset>

Binding tier: <query|session|monthly>
Recommendation: <brief human-readable guidance>

For WARN decisions, include the exact token count that would trigger a BLOCK if the current operation proceeds.

For BLOCK decisions, include the token count required to resume and the earliest reset date across all blocked tiers.

IV. Best Practices

• Always check all three tiers. A system that only enforces monthly limits will allow runaway sessions to consume the month's budget in a single hour.
• Use EstimatePlanCost(plan) from the orchestration engine when available — it produces more accurate estimates than keyword-based defaults because it accounts for DAG structure and parallelism.
• When the category is ambiguous (e.g., "analyze a file"), apply the higher multiplier (compute at 1.5x rather than file at 1.3x). It is better to over-estimate and approve a cheaper operation than to under-estimate and approve an expensive one.
• Log every BLOCK decision with the full budget snapshot. This creates an audit trail that helps identify whether limits are calibrated correctly.
• Do not cache the GetRemaining result across multiple checks. Budget state changes between calls and a stale snapshot can lead to false approvals.
• The 90% threshold for BLOCK is a policy default, not a hard system constant. If an operator has configured a different threshold, respect their configuration over this default.

V. Quality Checklist

Before completing this skill, verify:

• Both inputs received (action description and token estimate, or default applied with note)
• All three tiers queried — not just monthly
• Category multiplier applied and category logged in output
• Binding tier identified (minimum remaining across all tiers)
• Decision is one of APPROVE, WARN, or BLOCK — no ambiguous states
• BLOCK decisions do not auto-proceed — human override required
• Monthly reset date included in output for WARN and BLOCK decisions
• Full budget snapshot (all three tiers with utilization percentage) included in output

Output

• Structured decision block: Decision (APPROVE / WARN / BLOCK), reason, adjusted cost estimate, per-tier budget status with utilization percentages, binding tier, and human-readable recommendation.
• For WARN: includes the exact token count that would trigger a BLOCK if the current operation proceeds.
• For BLOCK: includes token count required to resume and earliest reset date across blocked tiers.

Examples

Scenario 1: "Check if a 10-URL web search is within budget" → APPROVE with adjusted cost (2000 tokens × 1.4x web multiplier = 2800), all three tiers shown at current utilization. Scenario 2: "Session is at 88% — can I run this summarize task?" → WARN at session tier. Compute multiplier (1.5x) applied. Shows exactly how many tokens remain before BLOCK threshold.

Edge Cases

• If estimated_tokens is not provided, apply conservative defaults by operation type (web 2000, compute 3000, file 1500, memory 800) and note the default was used.
• When an operation spans multiple categories, apply the highest multiplier — never stack multipliers.
• BLOCK decisions cannot auto-proceed regardless of downstream urgency; surface the decision and wait for human confirmation.

Anti-Patterns

• Caching the GetRemaining result across multiple checks — budget state changes between calls and a stale snapshot can produce false approvals.
• Using this skill as a post-hoc ledger review — it is a forward-looking gate. For historical cost analysis, use agent-performance-report.