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 DDS-Solutions/AI-TadPole-OS --skill systematic-debugginggit clone --depth 1 https://github.com/DDS-Solutions/AI-TadPole-OSWrote 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/dds-solutions/ai-tadpole-os/systematic-debugging)<a href="https://agentmods.dev/skills/dds-solutions/ai-tadpole-os/systematic-debugging"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/systematic-debugging/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/dds-solutions/ai-tadpole-os/systematic-debugging"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/systematic-debugging.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00025 | $0.01257 |
| Opus 5.5 | $0.00010 | $0.00503 |
| Sonnet 5.5 | $0.00005 | $0.00251 |
| Haiku 4.5 | $0.00003 | $0.00126 |
Grade A, and why
systematic-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 5d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[!IMPORTANT] AI Context & Knowledge Heritage
- Subsystem: Agent Skills Registry / systematic-debugging
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Shotgun debugging, speculative guesswork, unverified fixes, or flaky reproduction loops.
- Observability: Traceability via
execution/parity_guard.py([SKILL])
Systematic Debugging & RCA Protocol
Philosophy: Never touch code or propose speculative fixes until a tight, red-capable reproduction test proves the defect. Eliminate guesswork through binary hypothesis discrimination and 5-Whys root cause analysis.
Workflow Bindings: Integrated directly into/debugand/anneal.
🎯 Progressive Disclosure & L3 Reference Index
Read the core Sovereign debugging rules below; consult deep L3 Reference Guides on demand:
| L3 Reference Guide | Purpose | Trigger / When to Load |
|---|---|---|
references/rca_and_hypothesis_testing.md |
Hypothesis discrimination tables, 5-Whys root cause chains, and Point of Divergence schemas | Isolating elusive bugs & architecting regression tests |
scripts/reproduction_runner.py |
Automated reproduction runner measuring execution latency and testing for command flakiness | Establishing tight (<2s) reproduction loops |
🔄 1. The 4-Phase Debugging Process
[PHASE 1: REPRODUCE] ───> [PHASE 2: ISOLATE] ───> [PHASE 3: ROOT CAUSE] ───> [PHASE 4: VERIFY]
Build minimal, fast Formulate competing Find point of divergence Prove green pass &
red failing test (<2s) hypotheses ($H_A/H_B$) via telemetry map add permanent test
Phase 1: Reproduce & Tighten Feedback Loop
- 🛑 STRICT GATE: Do NOT modify code or hypothesize fixes until a tight, red-capable reproduction command is built and verified.
- Criteria:
- Red-Capable: Fails on the user's exact symptom before any fix is applied.
- Deterministic: 100% reproducible (use
reproduction_runner.py --flaky-checkto measure stability). - Fast: Completes in $< 2$ seconds.
What ships with it
2 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.
- 5d ago Changed · -32 lines 9dbd89f49cfd
- 1mo ago First seen · 127 lines · 25 tokens per session scan A 4e1aea15c230
systematic-debugging is a skill published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 1,257 once invoked, about $0.0001 per session on Opus 5.5. 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.
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