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/anentrypoint/freddie/systematic-debuggingnpx skills add AnEntrypoint/freddie --skill systematic-debugginggit clone --depth 1 https://github.com/AnEntrypoint/freddieWhat 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 | $0.00021 | $0.00691 |
| Opus 5 | $0.00010 | $0.00345 |
| Sonnet 5 | $0.00004 | $0.00138 |
| Haiku 4.5 | $0.00002 | $0.00069 |
Grade A, and why
systematic-debugging scanned grade A with 1 finding 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 yesterday.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| Network issue | tcpdump, curl -v | How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Systematic Debugging
Do not guess. Form hypotheses, design experiments, eliminate candidates, converge on root cause through observation.
The debugging loop
OBSERVE → HYPOTHESISE → EXPERIMENT → ELIMINATE → CONVERGE
Never skip to EXPERIMENT without OBSERVE. Never fix without CONVERGE.
Phase 1: OBSERVE — reproduce the bug
- Write a reproduction recipe — exact steps, inputs, environment.
- Confirm deterministic (always fails) vs intermittent (% failure rate).
- Find the smallest reproduction — strip everything not needed to trigger the bug.
- Capture: error message, stack trace, logs, HTTP request/response, query.
Phase 2: HYPOTHESISE — list candidate causes
Write every possible cause. Rank by:
- Proximity to the failure point
- Recent changes (last deploy, last config change)
- Complexity (more complex code = more places to hide bugs)
Phase 3: EXPERIMENT — test one hypothesis at a time
Hypothesis: Redis is evicting sessions under memory pressure
Experiment: redis-cli INFO memory | grep used_memory_human; redis-cli MONITOR | grep DEL
Expected if TRUE: memory near maxmemory, DEL commands on session keys
Expected if FALSE: memory headroom, no unexpected DELs
Result: ...
Never change two things at once. One variable per experiment.
Phase 4: ELIMINATE — cross off falsified hypotheses
- FALSIFIED — evidence rules it out
- CONFIRMED — evidence supports it
- INCONCLUSIVE — need more data
Stop when exactly one hypothesis is CONFIRMED and all others FALSIFIED.
Phase 5: CONVERGE — fix at root cause
- Write a failing test that reproduces the bug.
- Implement the fix.
- Confirm the test now passes.
- Check for similar bugs in adjacent code.
Debugging tools
| Situation | Tool |
|---|---|
| Slow code path | Profiler (node --prof, py-spy) |
| Memory leak | Heap snapshot (Chrome DevTools) |
| Network issue | tcpdump, curl -v |
| DB slow query | EXPLAIN ANALYZE |
| Race condition | Thread sanitiser, stress test |
| Intermittent failure | Log correlation by request ID |
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.
- yesterday First seen · 81 lines · 21 tokens per session scan A 557d6fcf83f9
systematic-debugging is a skill published in the GitHub repository AnEntrypoint/freddie (5 stars, last pushed 2d ago), licensed MIT. It adds 21 tokens to every session and 691 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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