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savethetokenssavethetokens 搜索

Agent Skill

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。

总安装

1,435

周安装

61

GitHub Stars

5

下载量

503
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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)

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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