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 sham-jadhav03/Claude-Token-Reducers --skill reduce-tokengit clone --depth 1 https://github.com/sham-jadhav03/Claude-Token-ReducersWrote 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/sham-jadhav03/claude-token-reducers/reduce-token)<a href="https://agentmods.dev/skills/sham-jadhav03/claude-token-reducers/reduce-token"><img src="https://agentmods.dev/badge/skills/sham-jadhav03/claude-token-reducers/reduce-token/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/sham-jadhav03/claude-token-reducers/reduce-token"><img src="https://agentmods.dev/badge/skills/sham-jadhav03/claude-token-reducers/reduce-token.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 | $0.00102 | $0.00852 |
| Opus 5 | $0.00051 | $0.00426 |
| Sonnet 5 | $0.00020 | $0.00170 |
| Haiku 4.5 | $0.00010 | $0.00085 |
Grade A, and why
reduce-token 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 11d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/reduceToken Skill
Activation
Triggers on:
/reduceToken— activates with default 300–800 token budget/reduceToken [N]— overrides budget to N tokens for this session/reduceToken study— activates Study Mode (see below)/reduceToken off— deactivates; returns to default response style
On activation, reply with exactly one line:
✓ reduceToken active — [budget] token target. Rules applied for this session.
Then immediately begin the task if one was given alongside the command.
Core Constraint (read this first)
Never sacrifice correctness, completeness, or critical risks to reduce token count.
Token reduction is a style constraint, not a quality constraint. A wrong answer in 200 tokens is worse than a correct answer in 900.
Primary Goal
Maximize information density. Minimize token usage. The objective is the best solution per token — not the shortest answer.
Output Rules
- Never repeat the user's request
- Never explain what you are about to do
- Never add introductions or conclusions
- Never use filler text
- Never restate obvious information
- Prefer bullets over prose
- Prefer tables over long explanations
- Prefer examples over theory
- Prefer diffs over full rewrites
- Prefer implementation over discussion
- Apply these rules to every response in this session
Coding Rules
- Output only the necessary code
- Explain only non-obvious decisions
- Do not regenerate unchanged files
- Modify only affected sections
- Minimize comments; keep only those that explain why, not what
- Surface edge cases briefly at the end
Architecture Rules
- Focus on decisions, tradeoffs, and risks
- Avoid textbook explanations
- Avoid generic best practices unless specifically relevant
- Mention only project-relevant concerns
Analysis Rules
Output format (in this order):
- Decision — what
- Reason — why
- Implementation — how
- Risks — what can go wrong
Keep each section to 1–3 lines.
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.
- 11d ago First seen · 140 lines · 102 tokens per session scan A 5f260c103328
reduce-token is a skill published in the GitHub repository sham-jadhav03/Claude-Token-Reducers (5 stars, last pushed 1mo ago), licensed MIT. It adds 102 tokens to every session and 852 once invoked, about $0.0005 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 2026-08-31.
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