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 agentmods add skills/brunob54/superpowers-orchestrator/token-efficiencynpx skills add brunob54/superpowers-orchestrator --skill token-efficiencygit clone --depth 1 https://github.com/brunob54/superpowers-orchestratorWrote 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/brunob54/superpowers-orchestrator/token-efficiency)<a href="https://agentmods.dev/skills/brunob54/superpowers-orchestrator/token-efficiency"><img src="https://agentmods.dev/badge/skills/brunob54/superpowers-orchestrator/token-efficiency.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.02025 |
| Opus 5 | $0.00019 | $0.01012 |
| Sonnet 5 | $0.00008 | $0.00405 |
| Haiku 4.5 | $0.00004 | $0.00202 |
Grade A, and why
token-efficiency 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 6d 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.
2. **WebFetch returns AI-generated summaries, not raw text.** For verbatim content from URLs (e.g., GitHub raw files, config files, source code), use `curl -sf <url>` via Bash instead. How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Token Efficiency
Core operating standard for all sessions. Apply permanently from activation.
Response Rules
- Lead with the answer — no preambles, no restating the question
- Use bullet points and code over prose narration
- Never explain what you are about to do — just do it
- Omit filler phrases ("Certainly!", "Great question!", "Now let me...", "As you can see...")
- One question per clarification turn — collect all unknowns and ask them together, not one at a time
- Prefer structured output (JSON/YAML) when the result feeds a downstream step
Tool Execution Rules
- Batch all independent tool calls in a single response — never serialize calls that can run in parallel
- Do not re-read a file already read this session unless it was modified since
- Match read scope to task type: use Grep to locate specific known content (a function, a config value, an error handler); read complete files when the task requires understanding what a file covers (scope assessment, gap analysis, systemic recommendations). Partial reads cannot prove absence.
- Use Glob instead of Bash
lsorfind - Do not verify existence of a path already confirmed earlier in the session
- The Read tool returns a maximum of 2,000 lines per call. For files you have reason to believe exceed 2,000 lines, use
offsetandlimitparameters to read in sequential chunks. Never assume a single read covered the complete file.
Agent & External Content Rules
- Agent results are compressed. When a subagent returns to the parent session, its full context (all file reads, web fetches, reasoning) is reduced to a summary. Never dispatch agents to "fetch and return" raw content — the content lives in the agent's context and only a compressed summary survives the return. This applies to local file reads AND web-fetched content.
- WebFetch returns AI-generated summaries, not raw text. For verbatim content from URLs (e.g., GitHub raw files, config files, source code), use
curl -sf <url>via Bash instead. - Use agents for conclusions, not data relay. Good: "Analyze the test failures in X and recommend fixes." Bad: "Fetch files A, B, C and return their contents." If you need raw content in your context, fetch it yourself with direct tool calls.
- For local files: Read directly. Do not dispatch an agent to read project files and report back. You lose the actual content and waste tokens on the round trip. Use the Read tool.
- project-map.md is orientation, not understanding. The map tells you what exists and where — directory purposes, key file roles, constraints. It does NOT contain the logic inside each file. When you need to understand a file's actual implementation (for modification, comparison, or debugging), read it directly. The map saves you from re-discovering project structure; it does not replace reading the files you need to work with.
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.
- 6d ago First seen · 146 lines · 39 tokens per session scan A 8e9a44901524
token-efficiency is a skill published in the GitHub repository brunob54/superpowers-orchestrator (3 stars, last pushed 4d ago), licensed MIT. It adds 39 tokens to every session and 2,025 once invoked, about $0.0002 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 2026-08-31.
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