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 moonlight-lupin/agent-skills --skill input-token-overheadsgit clone --depth 1 https://github.com/moonlight-lupin/agent-skillsWrote 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/moonlight-lupin/agent-skills/input-token-overheads)<a href="https://agentmods.dev/skills/moonlight-lupin/agent-skills/input-token-overheads"><img src="https://agentmods.dev/badge/skills/moonlight-lupin/agent-skills/input-token-overheads/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/moonlight-lupin/agent-skills/input-token-overheads"><img src="https://agentmods.dev/badge/skills/moonlight-lupin/agent-skills/input-token-overheads.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Excessive Agency · line 194 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.
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 | $0.00027 | $0.02845 |
| Opus 5.5 | $0.00011 | $0.01138 |
| Sonnet 5.5 | $0.00005 | $0.00569 |
| Haiku 4.5 | $0.00003 | $0.00284 |
Grade A, and why
input-token-overheads scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Input Token Overheads
Audit every source of per-turn input token cost on a Hermes Agent instance. Measure each, rank by cost, act on the top consumers.
Run the consolidated audit with scripts/audit_overheads.py (procedure step 1, in one command): --json for structured output, --chart <png> for a shareable chart, plus a context-doctor-style check of every always-injected file — TRUNCATED (over injection cap, silently cut every turn) and MISSING statuses, exit 1 when any file needs attention. Ported from jzOcb/context-doctor (MIT; OpenClaw-only — concepts only, script is native).
When to Use
- User says "token overhead", "context too large", "why is input so expensive"
- Model output quality degrades from context dilution
- Cost optimization — fewer input tokens per turn means lower API spend
- After adding skills, plugins, or tools — verify the overhead delta
The Overhead Map
Every turn, Hermes injects these blocks into the system prompt before the user's message:
| Block | When loaded | Cost model |
|---|---|---|
| Skill descriptions | Every turn (skill-retrieval top-K) | ~200 chars per description, K per turn |
| Memory (personal notes) | Every turn | Static, grows with usage |
| User profile | Every turn | Static, grows as preferences accumulate |
| Memory provider context | Every turn (if memory plugin active) | Dynamic, 5 memories recalled by default |
| Tool schemas (direct) | Every turn | Full JSON schema per enabled tool |
| Deferred tool catalog | Every turn (if configured) | Name + description only |
| Mandatory skills | Every turn (if configured) | Full SKILL.md body |
| Platform formatting rules | Every turn | Fixed, platform-specific |
| Behavioral rules | Every turn | Fixed system prompt text |
| Full skill body | On-demand (skill_view) | Only when a skill is loaded |
| Compression summary | After threshold | Replaces older messages with a summary |
On-demand (not per-turn): full SKILL.md via skill_view, deferred tool schemas via tool_describe, reference files via skill_view(file_path=...).
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago Changed · +2 lines 6b7edd26e006
- 1mo ago First seen · 205 lines · 27 tokens per session scan A ea8bc3ee9128
input-token-overheads is a skill published in the GitHub repository moonlight-lupin/agent-skills (112 stars, last pushed 4d ago), licensed MIT. It adds 27 tokens to every session and 2,845 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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