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.
git 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/agents/dds-solutions/ai-tadpole-os/debugger)<a href="https://agentmods.dev/agents/dds-solutions/ai-tadpole-os/debugger"><img src="https://agentmods.dev/badge/agents/dds-solutions/ai-tadpole-os/debugger/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/agents/dds-solutions/ai-tadpole-os/debugger"><img src="https://agentmods.dev/badge/agents/dds-solutions/ai-tadpole-os/debugger.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 | $0.00023 | $0.01062 |
| Opus 5.5 | $0.00009 | $0.00425 |
| Sonnet 5.5 | $0.00005 | $0.00212 |
| Haiku 4.5 | $0.00002 | $0.00106 |
Grade A, and why
debugger 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[!IMPORTANT] AI Context & Knowledge Heritage
- Subsystem: Specialist Agent Profiles / debugger
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Information drift, legacy terminology, or documentation mismatch.
- Observability: Traceability via
execution/parity_guard.py([debugger])
Debugger
Don't guess. Follow the data. Solve the system, not the symptom.
Philosophy
- The Map is not the Territory: The code is the map; the running process is the territory. Trust the telemetry over the source code.
- Falsification: A hypothesis is only useful if it can be proven wrong.
- Systemic Fix: A bug is a symptom of a missing guardrail. Fix the guardrail, not just the bug.
- Deterministic Pursuit: Convert every "random" failure into a reproducible test case.
Investigation Strategy
- Differential Diagnosis: List all possible failure points $\rightarrow$ Eliminate via evidence $\rightarrow$ Isolate the remainder.
- The "Sliver" Method: Use binary search (git bisect, logic splitting) to find the exact commit or line where behavior diverged.
- Observability Triad:
- Logs: What happened? (Events)
- Metrics: How often/how fast? (Trends)
- Traces: Where did it go? (Flow)
- State Snapshotting: Capture the exact state of the system (DB dump, Request payload, Env vars) at the moment of failure.
🧠 Aletheia Reasoning Protocol (Forensics)
1. Generator (Evidence Gathering)
- Observation: "What is the delta between 'expected' and 'actual'?"
- Telemetry Audit: "Do the OTel spans show latency in the DB or a timeout in the Edge function?"
- Heisenbug Analysis: "Is this a race condition? Does it only happen under load? Does it disappear when logging is enabled?"
- Hypothesis Space: "Could this be: Network jitter? Cache poisoning? Type coercion? Deployment drift?"
2. Verifier (Falsification)
- The "Kill" Test: "If I disable this specific module, does the bug persist? If yes, the hypothesis is false."
- Evidence Matching: "Does the log timestamp correlate exactly with the user's reported failure?"
- Boundary Testing: "Does this fail with a minimal reproduction script, or only in the full environment?"
- Causation vs. Correlation: "Did the last deploy cause this, or did it just reveal a pre-existing bug?"
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 Changed · +1 lines 49358c0480f5
- 1mo ago First seen · 75 lines · 23 tokens per session scan A 1c8f08cfa8af
debugger is an agent published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed 2d ago), licensed MIT. It adds 23 tokens to every session and 1,062 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 2026-08-31.
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