spend-watch

spend-watch is a skill for Claude Code, Codex from aeonfun/aeon. It costs 79 tokens per session (6,609 once invoked), scanned A, original, MIT.

A cloud-cost analysis workflow for Neon, Vercel, Railway, and GitHub Actions. It attributes usage to specific resources, investigates likely causes, and recommends actions; it is read-only unless an action is explicitly armed.

In plain words
What is it for?
Use it to rank expensive services, branches, routes, RPC methods, or workflows; find causes such as idle usage or failures; and prepare platform-specific cost actions.
Why use it?
It helps explain what is driving cloud spending and separates real billing data from estimates, so cost changes can be based on identifiable usage rather than guesswork.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Use it to rank expensive services, branches, routes, RPC methods, or workflows; find causes such as idle usage or failures; and prepare platform-specific cost actions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aeonfun/aeon/spend-watch
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add aeonfun/aeon --skill spend-watch
Clone the repo
git clone --depth 1 https://github.com/aeonfun/aeon

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for spend-watch

README.md
[![agentmods](https://agentmods.dev/badge/skills/aeonfun/aeon/spend-watch.svg)](https://agentmods.dev/skills/aeonfun/aeon/spend-watch)
Your own site
<a href="https://agentmods.dev/skills/aeonfun/aeon/spend-watch"><img src="https://agentmods.dev/badge/skills/aeonfun/aeon/spend-watch.svg" alt="Measured on agentmods" height="20"></a>
Per session 79 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,609 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 7 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Tool Misuse · line 156
    Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).
    Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
  • high Tool Misuse · line 197
    Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).
    Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
  • high Tool Misuse · line 279
    Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).
    Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
  • medium Excessive Agency · line 31
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
  • medium Data Exfiltration · line 215
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 260
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 262
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00079 $0.06609
Opus 5 $0.00039 $0.03305
Sonnet 5 $0.00016 $0.01322
Haiku 4.5 $0.00008 $0.00661

Measured 3d ago against content hash ce4530d9b615, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

spend-watch scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

- **actions** adapter uses the `gh` CLI / `gh api`, authenticated by the workflow's `GH_TOKEN` (`GH_GLOBAL`); it works in-run with no curl fallback. Do **not** route it through `./secretcurl` — `GH_GLOBAL` ends in `_GLOB
Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/spend-watch/SKILL.md · 346 lines

How it starts

The opening of the file, as written. The whole thing — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Today is ${today}.

${var} — scope selector + optional arm flag.

  • empty / all → sweep every platform whose secret is present, emit one combined digest.
  • neon | vercel | railway | actions → run one adapter only.
  • prepend arm: (e.g. arm:neon, arm:actions) → authorize the adapter's safe write levers for this run. Without arm: the skill is read-only: it recommends, never mutates.
  • dry-run appended anywhere → build the digest but do not ./notify (for testing).

Runs unattended — treat ${var} as final, no confirmation step, except a delete/mutation always re-reads the target's current state before acting (see each adapter's arm rules).

This skill is a cost analyst, not a bill alarm. Each run answers, per platform:

  1. Attribution — what is consuming the most? Rank drivers by dollars (or the resource unit that maps to dollars), down to the specific object: service, branch, route, RPC method, workflow. Top-N with each line's % share.
  2. Root causewhy is that line expensive?
  3. Recommendation — a ranked action list, each carrying: the concrete lever, an effort/risk tag, whether it's armable now, and a saving in real dollars ONLY when the platform's billing API returns real dollars (Railway currentUsage, Actions overage). Everywhere else there is no dollar figure — the line carries its real signal instead (idle-awake %, % over the included allowance, cache-miss rate, stale-preview count, failure rate). Never invent a $X/mo.

Dollars-only-when-real is the core rule. A fabricated "$5/mo" is worse than the true signal "idle-awake 71%". Rank each recommendation by: real-$ saving first (when known), then signal magnitude × how actionable it is (armable > 1-click > code-change > investigate). The recommendation is the deliverable; the signal justifies it; the dollar is a bonus only when the API hands it over.

Across platforms (all): a roll-up — the real spend where billing exposes it (Railway $, Actions $), the biggest signal-ranked driver anywhere, and the top actions fleet-wide. No synthetic grand total.

The monitoring (deltas, real budgets, signal thresholds) is the trend context and the trigger; the deliverable is the ranked recommendations.


Shared setup (every run)

  1. Read memory/MEMORY.md for context and memory/spend-config.md for real-$ budgets, signal thresholds, and ignore-lists (see the config schema at the bottom). If spend-config.md is missing, run with the built-in defaults and note NO_CONFIG in the log — recommendations still work; they rank by signal regardless.
  2. Read the last 7 days of memory/logs/ — used to detect newly expensive drivers vs ongoing, and to avoid repeat-nagging a recommendation already sent.
  3. Parse ${var}:
RAW="$(printf '%s' "${var}" | tr '[:upper:]' '[:lower:]' | sed -e 's/^[[:space:]]*//' -e 's/[[:space:]]*$//')"
ARM=0;   case "$RAW" in arm:*) ARM=1; RAW="${RAW#arm:}";; esac
DRYRUN=0; case "$RAW" in *dry-run*) DRYRUN=1; RAW="$(printf '%s' "$RAW" | sed 's/dry-run//g' | tr -s ' ')";; esac
RAW="$(printf '%s' "$RAW" | sed -e 's/^[[:space:]]*//' -e 's/[[:space:]]*$//')"
case "$RAW" in
  ""|all) SCOPE=all ;;
  neon|vercel|railway|actions) SCOPE="$RAW" ;;
  *) SCOPE=all ;;   # unrecognized -> full sweep, note it in the log
esac
  1. Run the matching adapter(s). Each adapter self-skips if its secret is absent — log <platform>: SKIP no-secret and continue. In all mode, run every adapter whose secret is present, then run the Synthesis section.

Read the full file on GitHub · 346 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 346 lines · 79 tokens per session scan A ce4530d9b615

Subscribe to this mod's changes

spend-watch is a skill published in the GitHub repository aeonfun/aeon (716 stars, last pushed yesterday), licensed MIT. It adds 79 tokens to every session and 6,609 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.

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