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.
npx skills add aeonfun/aeon --skill spend-watchgit clone --depth 1 https://github.com/aeonfun/aeonWrote 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.
[](https://agentmods.dev/skills/aeonfun/aeon/spend-watch)<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>- NVIDIA SkillSpector warn
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.
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.
| Model | Per session | Once 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 |
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 Copies of this mod
1 near-identical copy found in the catalogue:
- spend-watch — 100% identical, 0 lines differ
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. Withoutarm:the skill is read-only: it recommends, never mutates.dry-runappended anywhere → build the digest but do not./notify(for testing).Runs unattended — treat
${var}as final, no confirmation step, except a delete/mutation always re-reads the target's current state before acting (see each adapter's arm rules).
This skill is a cost analyst, not a bill alarm. Each run answers, per platform:
- Attribution — what is consuming the most? Rank drivers by dollars (or the resource unit that maps to dollars), down to the specific object: service, branch, route, RPC method, workflow. Top-N with each line's % share.
- Root cause — why is that line expensive?
- Recommendation — a ranked action list, each carrying: the concrete lever, an effort/risk tag, whether it's armable now, and a saving in real dollars ONLY when the platform's billing API returns real dollars (Railway
currentUsage, Actions overage). Everywhere else there is no dollar figure — the line carries its real signal instead (idle-awake %, % over the included allowance, cache-miss rate, stale-preview count, failure rate). Never invent a$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 $), the biggest signal-ranked driver anywhere, and the top actions fleet-wide. No synthetic grand total.
The monitoring (deltas, real budgets, signal thresholds) is the trend context and the trigger; the deliverable is the ranked recommendations.
Shared setup (every run)
- Read
memory/MEMORY.mdfor context andmemory/spend-config.mdfor real-$ budgets, signal thresholds, and ignore-lists (see the config schema at the bottom). Ifspend-config.mdis missing, run with the built-in defaults and noteNO_CONFIGin the log — recommendations still work; they rank by signal regardless. - 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. - 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
- Run the matching adapter(s). Each adapter self-skips if its secret is absent — log
<platform>: SKIP no-secretand continue. Inallmode, run every adapter whose secret is present, then run the Synthesis section.
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.
- 3d ago First seen · 346 lines · 79 tokens per session scan A ce4530d9b615
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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