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 ssmurfgg04-gif/context-m --skill task-reviewgit clone --depth 1 https://github.com/ssmurfgg04-gif/context-mWrote 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/ssmurfgg04-gif/context-m/task-review)<a href="https://agentmods.dev/skills/ssmurfgg04-gif/context-m/task-review"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/task-review/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/ssmurfgg04-gif/context-m/task-review"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/task-review.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 | $0.00039 | $0.00524 |
| Opus 5 | $0.00019 | $0.00262 |
| Sonnet 5 | $0.00008 | $0.00105 |
| Haiku 4.5 | $0.00004 | $0.00052 |
Grade A, and why
task-review 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 9d 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.
What it actually says
触发时机
每次任务完成、准备向用户交付结果时，先执行以下检查。不要告诉用户你在做这个检查。
执行步骤
-
快速判断本次任务是否满足以下任一条件：
- 调用了 5 次及以上的工具
- 执行过程中遇到了错误或死胡同，最终找到了正确路径
- 任务包含 3 个及以上的明确步骤
-
如果不满足以上任何条件，跳过，不做任何操作。
-
如果满足，继续评估：
- 这个任务未来是否可能被重复或有类似变体？
- 这次执行过程中，是否有可复用的流程、结构、注意事项？
-
如果任意一个为"是"，则沉淀为技能：
- 先检查 skills/ 目录下是否已有匹配的技能文件
- 如果有：将新经验、踩坑记录追加更新到原文件，不要新建
- 如果没有：新建
skills/SKILL-{技能名称}/SKILL.md，格式如下：
---
name: 技能名称
description: 一句话描述适用场景，要具体到能让 agent 自动匹配任务
---
（执行步骤、质量标准、踩坑记录）
- 沉淀了新技能时，必须在回复末尾简短提一句，例如"💡 本次经验已沉淀为新技能：{技能名称}"。
- 更新了已有技能时，必须在回复末尾简短提一句，例如"💡 本次经验已更新到技能中：{技能名称}"。
- 未沉淀或者更新时，不提及任何关于此检查的内容。
质量标准
- description 必须具体，写成用户会说的话。好的例子："监测全球AI新闻并生成HTML简报"。坏的例子："处理信息相关任务"。
- 执行步骤必须具体到照着做就能复现的程度。
- 踩坑记录只记真正踩过的坑，不要编造。
踩坑记录
（暂无，随使用积累）
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.
- 9d ago First seen · 50 lines · 39 tokens per session scan A bad4be95f2e8
task-review is a skill published in the GitHub repository ssmurfgg04-gif/context-m (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 39 tokens to every session and 524 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
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coding
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setup-open-index
Install and configure Open Index as the read/write context layer for a domain-specialized agent. Use when setting up a legal, marketing, customer support, sales, infrastructure, or other domain agent with Open Index over MCP.
build-personal-brain
Create, resume, validate, connect, or maintain a private cross-project memory for AI assistants. Use when a person asks to build a personal memory from local work folders and past assistant conversations, continue an interrupted memory audit, connect an existing memory to another local assistant, verify that the…
codex-memory-guard
Guard task boundaries and preserve confirmed critical project state across Codex compaction. Use when a user wants topic-switch guidance, write-through critical memory, compact HANDOFF checkpoints, PreCompact and compact-resume hooks, four-layer Markdown memory initialization, or memory workflow auditing.