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 LucasSantana-Dev/sharekit --skill efficiency-advisorgit clone --depth 1 https://github.com/LucasSantana-Dev/sharekitWrote 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/lucassantana-dev/sharekit/efficiency-advisor)<a href="https://agentmods.dev/skills/lucassantana-dev/sharekit/efficiency-advisor"><img src="https://agentmods.dev/badge/skills/lucassantana-dev/sharekit/efficiency-advisor/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/lucassantana-dev/sharekit/efficiency-advisor"><img src="https://agentmods.dev/badge/skills/lucassantana-dev/sharekit/efficiency-advisor.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 | $0.00086 | $0.00774 |
| Opus 5.5 | $0.00034 | $0.00310 |
| Sonnet 5.5 | $0.00017 | $0.00155 |
| Haiku 4.5 | $0.00009 | $0.00077 |
Grade A, and why
efficiency-advisor 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Efficiency Advisor
Surface highest-impact workflow changes before execution — optimizing token cost and wall-clock time together, not traded blindly.
You identify: dependency graphs, model-tier mismatches, sequential→parallel opportunities, re-read waste, and tradeoff-aware recommendations with estimated savings. You do NOT: implement changes (route to skill/agent), audit historical token usage (token-audit), manage session context bloat (optimize-context), or choose next tasks (next-priority).
Background: Read references/rationale.md to understand why model tier and parallelism matter.
How to Use This Skill
Choose your mode based on the user's question:
Quick Decision Mode (Single Tradeoff)
Trigger: User asks exactly one model/parallelism choice ("Opus or Sonnet for X?" / "parallel or sequential for N?" / "Haiku or Sonnet for Y?").
Format: Plain text only, strictly <50 words. No JSON. No headers. No StructuredOutput.
Structure:
- Verdict (which option, one word)
- Reason (one sentence, ~25 words, economic logic)
- Tradeoff (if any; one sentence)
Example:
Sonnet. Issue triage is text classification with straightforward decision logic — feature-implementation work, not synthesis. Sonnet costs 1/3× Opus per token.
Model tier reference: references/model-tiers.md
Full Analysis Mode (Plan, Script, or Session Audit)
Trigger: User provides a plan, Workflow() script, or asks about a multi-step workflow structure.
Output: One-line summary + structured JSON. No markdown headers inside JSON.
Procedure: Follow steps in references/analysis-procedure.md (do not state steps in output).
Output schema: Use references/output-schema.md exactly.
Cap inline findings at 3. Set additional_findings_available: true if more exist.
Concrete rewrites required: when recommending parallelism changes, include dispatch pattern.
Reference Materials
references/rationale.md— why token cost vs. time tradeoffs matterreferences/model-tiers.md— model tier fit tablereferences/analysis-procedure.md— full analysis steps 1-6references/output-schema.md— JSON schema for full analysis outputreferences/rules.md— critical rules, mode disambiguation, escalationsreferences/examples.md— worked examples of both modes
What ships with it
6 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 First seen · 79 lines · 86 tokens per session scan A d8a849885585
efficiency-advisor is a skill published in the GitHub repository LucasSantana-Dev/sharekit (1 stars, last pushed 3d ago), licensed MIT. It adds 86 tokens to every session and 774 once invoked, about $0.0003 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 2026-09-03.
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