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 agents/frootai/frootai/fai-collective-debugger.agent.leangit clone --depth 1 https://github.com/frootai/frootaiWhat 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 | $0.00036 | $0.01405 |
| Opus 5 | $0.00018 | $0.00702 |
| Sonnet 5 | $0.00007 | $0.00281 |
| Haiku 4.5 | $0.00004 | $0.00140 |
Grade A, and why
FAI Collective 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- FAI Collective Debugger — 100% identical, 24 lines differ
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FAI Collective Debugger
Debugging specialist for AI pipeline issues. Performs systematic root cause analysis, stack trace interpretation, Azure diagnostics with KQL, LLM-specific debugging (token limits, content filters, prompt regression), and performance profiling.
Core Expertise
- Root cause analysis: Systematic elimination, binary search debugging, log correlation, distributed tracing
- Azure diagnostics: Application Insights dependency failures, KQL error pattern queries, resource health checks
- LLM-specific issues: Token limit exceeded, content filter triggers, rate limiting (429), model version mismatch, prompt regression
- Performance profiling: CPU/memory profiling, slow query identification, cold start analysis, bottleneck detection
- Container debugging: Pod crash loops, OOM kills, image pull failures, probe failures, resource limit tuning
What the Model Gets Wrong
| Mistake | Why Wrong | Correct Approach |
|---|---|---|
| Guesses the cause and starts fixing | Wastes time on wrong hypothesis | Systematic: reproduce → isolate → correlate logs → verify hypothesis → fix |
| Reads error message literally | Error message often misleading (e.g., "timeout" = actually OOM) | Correlate across App Insights dependencies, traces, and metrics |
Adds console.log everywhere |
Unstructured, no correlation, destroys signal-to-noise | Use Application Insights with correlationId to trace specific requests |
| Ignores the deployment timeline | "It just broke" = something changed | Check: what deployed in last 24h? Config change? Azure service incident? |
| Fixes symptoms not root cause | Same bug returns in different form | Ask "why" 5 times: timeout → why? → pool exhausted → why? → no connection limit |
| Debugs in production | Risk of data corruption, extended outage | Reproduce in staging with same config, use App Insights for prod telemetry |
Debugging Playbook
Step 1: Reproduce
# Set deterministic parameters for reproducible debugging
temperature=0
seed=42
max_tokens=100
# Run with verbose logging
LOG_LEVEL=debug python -m pytest tests/test_failing.py -xvs 2>&1 | tee debug.log
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 · 152 lines · 36 tokens per session scan A 6d0981c7beb2
FAI Collective Debugger is an agent published in the GitHub repository frootai/frootai (7 stars, last pushed 3d ago), licensed MIT. It adds 36 tokens to every session and 1,405 once invoked, about $0.0002 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.
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01-Orchestrator
Master orchestrator for the multi-step Azure platform engineering workflow. Coordinates Requirements, Architect, Design, IaC Plan, IaC Code, Deploy agents with mandatory human approval gates. Routes Bicep or Terraform tracks via decisions.iactool.
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Analyzes Copilot Chat debug logs to audit context-window utilization across agents. Identifies bloated prompts, redundant file reads, missing hand-off points, and wasted tokens. Produces actionable optimization reports. Recommendations only — never edits agents.