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 xyva-yuangui/XyvaClaw --skill effect-trackergit clone --depth 1 https://github.com/xyva-yuangui/XyvaClawWrote 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/xyva-yuangui/xyvaclaw/effect-tracker)<a href="https://agentmods.dev/skills/xyva-yuangui/xyvaclaw/effect-tracker"><img src="https://agentmods.dev/badge/skills/xyva-yuangui/xyvaclaw/effect-tracker/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/xyva-yuangui/xyvaclaw/effect-tracker"><img src="https://agentmods.dev/badge/skills/xyva-yuangui/xyvaclaw/effect-tracker.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 | $0.00003 | $0.01575 |
| Opus 5 | $0.00002 | $0.00788 |
| Sonnet 5 | $0.00001 | $0.00315 |
| Haiku 4.5 | $0.00000 | $0.00158 |
Grade A, and why
effect-tracker 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Effect Tracker 📊
统一的技能效果追踪与度量系统。
核心指标
技能执行指标
| 指标 | 说明 | 采集方式 |
|---|---|---|
invocation_count |
调用次数 | 自动计数 |
success_rate |
成功率 | 退出码判定 |
avg_latency_ms |
平均耗时 | 自动计时 |
p95_latency_ms |
P95 耗时 | 自动计时 |
error_types |
错误类型分布 | 错误日志分析 |
resource_cost |
资源消耗(API调用/token数) | 自动统计 |
业务效果指标
| 指标 | 适用技能 | 说明 |
|---|---|---|
content_quality_score |
xhs-creator, content-empire | 质检评分均值 |
publish_success_rate |
xhs-publisher, multi-platform-publisher | 发布成功率 |
engagement_rate |
content-empire | 互动率(点赞+收藏+评论/阅读) |
signal_accuracy |
quant-strategy-engine | 信号准确率(回测验证) |
research_depth_score |
auto-researcher | 信息源数量×交叉验证率 |
reasoning_confidence |
deep-reasoning-chain | 平均置信度 |
kg_growth_rate |
knowledge-graph-memory | 知识图谱日增实体数 |
数据存储
$OPENCLAW_HOME/logs/ # 默认 ~/.xyvaclaw/logs/
├── effect-tracker.sqlite # 效果追踪数据库
└── daily/
├── 2026-03-05.jsonl # 每日原始事件日志
└── ...
数据模型
CREATE TABLE skill_events (
id INTEGER PRIMARY KEY AUTOINCREMENT,
timestamp DATETIME NOT NULL,
skill_name TEXT NOT NULL,
action TEXT, -- 具体操作
status TEXT NOT NULL, -- ok/warn/fail
latency_ms INTEGER,
tokens_used INTEGER,
api_calls INTEGER,
error_type TEXT,
error_message TEXT,
metadata JSON -- 技能特定的额外指标
);
CREATE TABLE daily_summary (
date TEXT NOT NULL,
skill_name TEXT NOT NULL,
invocations INTEGER,
successes INTEGER,
failures INTEGER,
avg_latency_ms REAL,
p95_latency_ms REAL,
total_tokens INTEGER,
total_api_calls INTEGER,
business_metrics JSON, -- 业务指标快照
PRIMARY KEY (date, skill_name)
);
使用方式
记录事件（技能内部调用）
from effect_tracker import track
# 装饰器方式（推荐）
@track("xhs-creator", action="create_note")
def create_note(topic):
...
# 手动方式
with track("quant-strategy-engine", action="generate_signal") as t:
signals = generate_signals()
t.set_metadata({"signal_count": len(signals)})
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.
- 11d ago First seen · 216 lines · 3 tokens per session scan A e391c0e644ab
effect-tracker is a skill published in the GitHub repository xyva-yuangui/XyvaClaw (21 stars, last pushed 1mo ago), licensed MIT. It adds 3 tokens to every session and 1,575 once invoked, about $0.0000 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 2026-08-30.
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