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Agent Skill · 研究检索

savethetokens(savethetokens 搜索)

savethetokens 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

savethetokens 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 39,288。

总安装 1,435周安装 61GitHub Stars 5下载 503核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:savethetokens(savethetokens 搜索)
来源仓库:https://github.com/redclawww/savethetokens
仓库路径:skills/savethetokens
安装命令:
npx skills add https://github.com/redclawww/savethetokens --skill savethetokens
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/redclawww/savethetokens --skill savethetokens

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,支持基于关键词或任务场景快速定位结果。适合在需要根据线索快速获取候选信息时使用。可结合来源仓库与原始 README 进一步验证具体功能与使用方式。安装通过 GitHub,适用于 Codex、Claude、Cursor、Gemini CLI 等宿主环境。

使用前应确认权限范围、维护状态,避免触发联网、命令执行或文件读写操作。

来源仓库内路径:skills/savethetokens

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/redclawww/savethetokens --skill savethetokens;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

savethetokens 怎么安装?

在终端执行 npx skills add https://github.com/redclawww/savethetokens --skill savethetokens。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

savethetokens 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

savethetokens 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/redclawww/savethetokens --skill savethetokens 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

savethetokens 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

savethetokens 用的人多吗?

公开数据是累计安装 1,435 次,近一周安装 61 次,下载 503 次,GitHub 5 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

savethetokens 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
savethetokens

savethetokens 搜索 · 本页

1,4355执行命令未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Context Governor

Optimize context usage with practical, high-impact workflows and scripts.

Non-Negotiable Guardrails

  1. Keep scope locked to the user request. Do not add extra features, pages, or telemetry unless asked.
  2. Treat token optimization as a constraint, not the goal. Correctness and security win over token reduction.
  3. Never claim token savings without before/after measurement on comparable tasks.
  4. If context-saving actions risk quality loss, keep the extra context and state the tradeoff.
  5. Never reduce code thoroughness to save tokens. Tests, strict config, safety checks, and error handling are non-negotiable. Save tokens from message verbosity — never from output completeness.

Operating Modes

  • Lean Mode (default): Use lightweight context hygiene only; do not create new benchmark artifacts.
  • Measurement Mode: Use launch-readiness or A/B telemetry scripts only when user asks for proof/percentages.

Claude Code Message Budget (required)

  1. Keep progress updates short and phase-based. Do not narrate every file write.
  2. Do not paste long command output unless user asks. Summarize only key signals.
  3. Do not repeat the same command without a code/input change; if retried, state the reason once.
  4. If /context shows message growth is unusually high, switch to stricter concise mode:

- fewer updates - shorter summaries - batch related edits before reporting

  1. Prefer one concise final summary over long running commentary.
  2. For benchmark runs, enforce matched behavior on both variants:

- same stop criteria - same compact policy - same output style (no extra giant report in one variant only)

Operating Playbook

  1. Confirm objective and lock scope in one sentence.
  2. Keep one chat session per task. Start a new session for unrelated work.
  3. Use ! <command> for direct shell commands when no reasoning is required.
  4. Run /context periodically. Compact around 50% usage instead of waiting for hard limits.
  5. Before /compact or /clear, create a checkpoint file with next steps and touched files.
  6. Keep top-level docs lean; move deep details to linked docs/*.md.
  7. Before final output on code tasks, run the quality gates in docs/QUALITY_GATES.md.
  8. For token-savings claims, run matched A/B using docs/BENCHMARK_PROTOCOL.md.
  9. For Claude benchmark runs, use docs/STRICT_BENCHMARK_PROMPT.md as the session starter.

Quick Commands

# Generate execution plan
python ~/.claude/skills/savethetokens/scripts/govern.py --budget 8000

# Generate checkpoint before compact/clear
python ~/.claude/skills/savethetokens/scripts/session_checkpoint.py \
  --task "..." \
  --done "..." \
  --next "..." \
  --context-percent 52 \
  --message-count 36

# Create session hook (Claude Code)
python ~/.claude/skills/savethetokens/scripts/session_hook_generator.py --project .

# Optimize CLAUDE.md
python ~/.claude/skills/savethetokens/scripts/claude_md_optimizer.py --analyze

# Calculate cost savings
python ~/.claude/skills/savethetokens/scripts/cost_calculator.py --developers 5

# Run launch-readiness benchmark with section-wise savings
python ~/.claude/skills/savethetokens/scripts/launch_readiness.py

# Run live A/B telemetry session (auto split control/optimized)
python ~/.claude/skills/savethetokens/scripts/govern.py \
  --input context.json \
  --budget 8000 \
  --experiment-id claude-launch-v1 \
  --variant auto \
  --assignment-key TICKET-123

# Generate measured A/B report from live sessions
python ~/.claude/skills/savethetokens/scripts/ab_telemetry.py \
  --experiment-id claude-launch-v1 \
  --days 14 \
  --required-intents code_generation,debugging,planning,review \
  --min-samples-per-intent 5

# Strict mode: exit 2 if claim gates fail (CI-friendly)
python ~/.claude/skills/savethetokens/scripts/ab_telemetry.py \
  --experiment-id claude-launch-v1 \
  --strict-claim-mode

# Print report JSON to stdout (pipe to jq, etc.)
python ~/.claude/skills/savethetokens/scripts/ab_telemetry.py \
  --experiment-id claude-launch-v1 \
  --json-stdout

# Code-task quality gate checklist (required before final answer)
cat ~/.claude/skills/savethetokens/docs/QUALITY_GATES.md

# Compare two /context snapshots (control vs optimized)
python ~/.claude/skills/savethetokens/scripts/context_snapshot_diff.py \
  --before-file before.txt \
  --after-file after.txt \
  --strict

# Compact watchdog (advisory, safe defaults)
python ~/.claude/skills/savethetokens/scripts/compact_watchdog.py \
  --context-file context_snapshot.txt \
  --require-checkpoint

# Dynamic tool filtering (fail-open recommended)
python ~/.claude/skills/savethetokens/scripts/tool_filter.py \
  --input tools.json \
  --query "..." \
  --fail-open

# Semantic skill selection (recommendation only)
python ~/.claude/skills/savethetokens/scripts/skill_selector.py \
  --query "..."

# External memory store (bounded retrieval)
python ~/.claude/skills/savethetokens/scripts/memory_store.py search \
  --query "..." \
  --for-prompt \
  --top-k 5 \
  --max-chars 1200

# Print lean session prompt template
cat ~/.claude/skills/savethetokens/docs/LEAN_SESSION_PROMPT.md

# Print strict benchmark harness prompt
cat ~/.claude/skills/savethetokens/docs/STRICT_BENCHMARK_PROMPT.md

Scripts

ScriptPurpose
govern.pyMain entry - execution plans
analyze.pyContext analysis
prune.pyPrune to budget (max 40%)
session_hook_generator.pySession-start hooks
session_checkpoint.pySave compact-ready session checkpoints
claude_md_optimizer.pyOptimize CLAUDE.md
quick_ref_generator.pyGenerate QUICK_REF.md
tiered_context.py3-tier context classification
relevance_scorer.pyScore context relevance
cost_calculator.pyROI tracking
launch_readiness.pyLaunch benchmark + section-wise savings report
ab_telemetry.pyLive A/B telemetry report with confidence checks
context_snapshot_diff.pyDetect token regressions from /context snapshots
compact_watchdog.pySafe advisory for /compact and /clear decisions
tool_filter.pyDynamic tool filtering with fail-open safeguards
skill_selector.pySemantic skill ranking with confidence gating
memory_store.pyExternal memory store with bounded retrieval
path_filter.pyFilter package dirs

Quality Rules

  • NEVER prune system prompts, errors, recent messages
  • Max pruning: 40% (keeps quality)
  • When uncertain → KEEP content
  • Will exceed budget rather than harm quality
  • Keep solution minimal and request-aligned; avoid speculative architecture
  • Run relevant tests/checks for touched areas, or explicitly state what could not be run

Completeness Checklist (never skip under token pressure)

Token savings come from shorter messages and smarter context — never from cutting corners on output quality. Before finalizing any code task, verify:

  1. Strict config — Enable strictest compiler/linter settings available (e.g. "strict": true in tsconfig). Zero any types, zero @ts-ignore, zero @ts-nocheck.
  2. Tests for touched code — Every changed function/module has corresponding tests. Minimum: one happy path, one error path per public function.
  3. Safety limits — Runtime code that loops, recurses, or processes unbounded input must have explicit guards (max iterations, call depth, step limits, timeouts).
  4. Error handling with context — Errors include location info (file, line, span) and actionable messages. No bare catch(e) {} or except Exception: pass.
  5. Input validation at boundaries — Validate user input, API responses, and file I/O. Internal code can trust internal types.
  6. Security basics — No command injection, no unsanitized template interpolation, no hardcoded secrets. Parameterize queries.
  7. Build passes — Run type-check/compile/build before declaring done. If it can't be run, state why.
  8. State what was not verified — If any check above could not be performed (no test runner, no build script), explicitly list it in the final summary.

Where to save tokens instead: shorter progress updates, batch related edits, omit command output unless asked, compact at 50% context.

Detailed Docs (read on-demand)

平台分布

Codex

32.88%
按下载量换算165

Claude

32.15%
按下载量换算162

Cursor

18.44%
按下载量换算93

Gemini CLI

10.05%
按下载量换算51

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/redclawww/savethetokens --skill savethetokens 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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