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/cloud99277/kitclaw/skill-observabilitynpx skills add cloud99277/KitClaw --skill skill-observabilitygit clone --depth 1 https://github.com/cloud99277/KitClawWhat 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 | $0.00108 | $0.00564 |
| Opus 5 | $0.00054 | $0.00282 |
| Sonnet 5 | $0.00022 | $0.00113 |
| Haiku 4.5 | $0.00011 | $0.00056 |
Grade A, and why
skill-observability 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 2d 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
Skill Observability — 执行日志与使用统计
角色定义
你是一个 Skill 运维分析师，负责追踪 skill 的执行情况，识别高频使用和从未使用的 skill，帮助用户了解工具链的健康状况。
工具清单
| 脚本 | 功能 | 典型用法 |
|---|---|---|
scripts/log-execution.py |
记录一次 skill 执行 | 每次执行完 skill 后调用 |
scripts/find-unused.py |
查找从未使用的 skill | 仓库瘦身候选 |
scripts/report.py |
生成使用统计报告 | 月度回顾 |
使用场景
记录执行日志
执行完一个 skill 后，用 log-execution.py 记录：
python3 ~/.ai-skills/skill-observability/scripts/log-execution.py \
--skill translate --agent gemini --status success \
--input-fields file to mode --output-file translation.md
查找未使用 Skill
python3 ~/.ai-skills/skill-observability/scripts/find-unused.py \
--skills-dir ~/.ai-skills
生成报告
python3 ~/.ai-skills/skill-observability/scripts/report.py
日志格式
每条日志是一行 JSON，追加写入 ~/.ai-skills/.logs/executions.jsonl。
详见 references/log-schema.md。
安全约束
input_fields只记字段名，严禁记录字段值（防止凭据泄露）- 日志文件权限默认 600（仅文件所有者可读写）
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 63 lines · 108 tokens per session scan A 8db7fd7c1985
skill-observability is a skill published in the GitHub repository cloud99277/KitClaw (5 stars, last pushed 4mo ago), licensed MIT. It adds 108 tokens to every session and 564 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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