Agent skill

bug-hunters

Run systematic bug hunting with spec reconstruction, adversarial validation, and confidence scoring. Use when you want to hunt bugs (not fix them), validate correctness, or run logic-first/code-first investigations. Triggers: bug hunt, spec reconstruction, logic-first, code-first, orchestrator, logic-hunter, cpp-hunter, python-hunter.

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Forks 6

Install this agent skill to your Project

npx add-skill https://github.com/DeevsDeevs/agent-system/tree/main/bug-hunters

SKILL.md

Bug Hunters

Use a single skill with role selection. If the user specifies a hunter, use that role; otherwise start with the orchestrator and ask which mode applies.

Role Map

  • orchestratoragents/orchestrator.md
  • logic-hunteragents/logic-hunter.md
  • cpp-hunteragents/cpp-hunter.md
  • python-hunteragents/python-hunter.md

Workflow

  1. Read README.md and follow the “hunt, don’t fix” rule.
  2. Spawn a child agent with agent_type equal to the selected role (orchestrator, logic-hunter, cpp-hunter, python-hunter).
  3. If no role is specified, default to orchestrator.
  4. Choose mode:
    • Logic-first when the problem is algorithm/spec correctness.
    • Code-first when the problem is language-specific failures or UB.
  5. Pass mode and scope in the spawn message, then use wait for completion.
  6. Use adversarial validation and only report MEDIUM+ confidence findings.
  7. Present a bug report, not remediation.

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Get current date and time in various formats. Use whenever you need the current date, time, timestamps, or formatted datetime values for any purpose (logging, file naming, scheduling, comparisons, etc.)

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DeevsDeevs/agent-system

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This skill should be used when the user asks about Polars DataFrame library (Apache Arrow) for Python or Rust. Triggers: "polars expressions", "lazy vs eager", "scan_parquet streaming", "convert pandas to polars", "pyspark to polars", "kdb to polars", "group_by_dynamic", "rolling_mean", "polars window functions", "asof join", "polars GPU", "polars parquet", "LazyFrame". Time series: OHLCV resampling, rolling windows, financial data patterns. Performance: native expressions over map_elements, early projection, categorical types, streaming.

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