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 fabioc-aloha/Alex_Skill_Mall --skill token-waste-eliminationgit clone --depth 1 https://github.com/fabioc-aloha/Alex_Skill_MallWrote 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/fabioc-aloha/alex_skill_mall/token-waste-elimination)<a href="https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/token-waste-elimination"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/token-waste-elimination/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/fabioc-aloha/alex_skill_mall/token-waste-elimination"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/token-waste-elimination.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00025 | $0.01601 |
| Opus 5 | $0.00013 | $0.00800 |
| Sonnet 5 | $0.00005 | $0.00320 |
| Haiku 4.5 | $0.00003 | $0.00160 |
Grade A, and why
token-waste-elimination 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 9d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Token Waste Elimination
Memory files are LLM context, not human documentation. Every line costs tokens. Optimize for my consumption, not human readability.
Loading Tiers
| Tier | What Loads | When | Cost |
|---|---|---|---|
| Always-on | copilot-instructions.md body, AGENTS.md/CLAUDE.md (if present), ALL instruction descriptions, ALL skill name+descriptions, ALL agent descriptions |
Every request | Fixed |
| Match-loaded | Instruction bodies with applyTo |
File context matches glob | Predictable |
| Agent-loaded | Instruction bodies without applyTo |
I judge description relevant (not automatic) | Variable |
| On-demand | Skill bodies, prompt bodies | I load skill when needed; prompt on /command |
Near-zero |
Core Rules
Instructions thin, skills thick: Instructions = decision tables + routing. Skills = procedures + examples + references.
applyTo gates cost: Add applyTo to domain-specific instructions (file-type triggered). Omit for conversational/universal instructions.
No-applyTo != auto-load: Without applyTo, the description is always visible but I choose whether to load the body. Not deterministic.
Size Thresholds
| File Type | Has Matching Skill | Max | Over Threshold |
|---|---|---|---|
| Instruction | Yes | 50 lines | Trim to rules + routing pointer |
| Instruction | No | 200 lines | Review for splitting |
| Skill body | Any | 400 lines | Move detail to resource files |
| Prompt | Any | 60 lines | Slim to steps + skill reference |
Waste Patterns
| Pattern | Fix |
|---|---|
%%{init in Mermaid |
Delete line (rendering directive I can't use) |
Legacy ## Related Skills sections with separate JSON |
Delete section (connections now use frontmatter applyTo) |
Microsoft Entra ID |
Replace with Microsoft Entra ID |
Classification: / Activation: / Priority: in instruction body |
Delete (duplicates YAML frontmatter) |
| Instruction >50 lines with matching skill | Trim: keep decision tables, move procedures to skill |
| Code blocks >20 lines in instructions | Move to .github/muscles/ or scripts/ |
| Templates >15 lines inline | Move to resource file |
| Hardcoded counts (e.g., "150 skills") | Replace with "See catalog" references |
| Stale dates/versions | Update or remove |
| Human-tutorial prose in memory files | Compress to rule statements (LLM doesn't need tutorials) |
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.
- 9d ago First seen · 143 lines · 25 tokens per session scan A e4ec9cc2348f
token-waste-elimination is a skill published in the GitHub repository fabioc-aloha/Alex_Skill_Mall (4 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 1,601 once invoked, about $0.0001 per session on Opus 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 2026-09-03.
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