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/furkangonel/cowrangler/debuggingnpx skills add furkangonel/cowrangler --skill debugginggit clone --depth 1 https://github.com/furkangonel/cowranglerWhat 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 | $0.00019 | $0.00754 |
| Opus 5 | $0.00010 | $0.00377 |
| Sonnet 5 | $0.00004 | $0.00151 |
| Haiku 4.5 | $0.00002 | $0.00075 |
Grade A, and why
debugging 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.
How it starts
The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging SOP
The Scientific Method for Bugs
Step 1 — Understand the Symptom
- Read the error message / stack trace carefully (every line)
- Identify: What was expected? What actually happened?
- Note: When does it occur? Always, or only sometimes?
- Check: Is this a regression? When did it last work?
Step 2 — Reproduce the Bug
# Can you reproduce it reliably?
# If not, it may be:
# - Race condition (timing-dependent)
# - Environment-specific (env vars, OS, versions)
# - Data-dependent (specific inputs trigger it)
- Reduce to the minimal reproduction case
- Confirm the reproduction before investigating
- Check if it reproduces in a fresh environment
Step 3 — Read the Code
- Locate the exact file and function from the stack trace
- Trace the execution path from input → failure point
- Look at what changed recently:
git log --since="3 days ago" -- <file>
Step 4 — Form a Hypothesis
State your hypothesis explicitly:
"I think the bug is caused by X because Y"
Then check what evidence would confirm or disprove it.
Step 5 — Verify the Hypothesis
Add targeted logging to confirm:
console.log("[DEBUG]", { variableName, type: typeof variableName });
Or use the debugger:
node --inspect-brk dist/main.js # Attach Chrome DevTools
Step 6 — Fix
- Make the minimal change that fixes the root cause
- Do NOT fix symptoms — fix root causes
- Do NOT refactor while fixing (separate concerns)
Step 7 — Verify the Fix
# Run existing tests
npm test
# Confirm the original reproduction no longer fails
# Add a regression test to prevent recurrence
Common Bug Patterns
Async/Await Issues
// WRONG — Promise not awaited
const data = fetchData(); // returns Promise, not data
console.log(data.name); // undefined!
// CORRECT
const data = await fetchData();
Off-by-One
- Array indices:
arr[arr.length]is undefined,arr[arr.length - 1]is last - Loop boundaries:
< lengthvs<= length
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 · 116 lines · 19 tokens per session scan A 0e47e95d2b6e
debugging is a skill published in the GitHub repository furkangonel/cowrangler (2 stars, last pushed 4d ago), licensed MIT. It adds 19 tokens to every session and 754 once invoked, about $0.0001 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.
Other skills, from other repositories
ftshare-market-data
非凸科技金融数据技能集。覆盖 A 股股票列表、全字段/实时行情列表、分时价格、IPO、大宗交易、融资融券、单股行情估值、可转债、ETF、基金、指数(含分页指数描述、下载描述 PDF、权重汇总/成份明细、下载权重 xlsx、详情/K 线/分时)、宏观经济,以及港股公司介绍/估值分析/基础视图/K线、东财美股列表/历史K线/最新行情、native 美股基础信息/利润表/现金流量表/资产负债表、港股财报三表(利润/现金流/资产负债)/东财港股指数日K线等接口(market.ft.tech / ftai.chat)。用户询问 A 股、港股、美股的代码、行情、估值、K线、指数权重/描述、新闻与宏观数据时使用。.
eastmoney-market-valuation
查询东财市场日估值。当用户需要查询 A 股主要市场指数(上证指数、沪深300、深证成指、创业板指、科创50、北证50)的每日估值数据,包括市盈率、总市值、流通市值、收盘点位等;支持单日、区间查询,或了解东财市场日估值时使用。.
eastmoney-stock-valuation
查询东财个股日估值。当用户需要查询全部 A 股个股的每日估值数据,包括市盈率(TTM/LYR)、市净率、市现率、市销率、PEG、总市值、流通市值、收盘价等;支持单票单日、单票历史区间及全市场查询,或了解东财个股日估值时使用。.
cb-base-data
单只可转债基础信息(market.ft.tech)。用户问可转债基础信息、转债详情、某只可转债、转股价、转股价值、到期日、发行规模时使用。.
eastmoney-hk-index-daily-kline
查询东财港股指数日K线(恒生指数 HSI / 国企指数 HSCEI / 恒生科技 HSTECH 等):开高低收、成交量、成交额、振幅、涨跌幅、涨跌额、换手率。按指数代码/交易日/日期区间查询。Use when user asks about 港股指数K线, 恒生指数走势, 国企指数日线, 恒生科技指数, HK index kline, HSI/HSCEI/HSTECH, eastmoney-hk-index-daily-kline. 数据源东方财富。.
eastmoney-us-stock-daily-ohlc
查询东财美股历史日 K 线。当用户需要查询东财美股历史日 K 线;有日期范围时按 3 天窗口分批请求,无日期范围时全量拉取,或了解东财美股历史 OHLC、东财美股历史日 K 线时使用。.