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/hashangit/openfusion/debug-mantranpx skills add hashangit/openfusion --skill debug-mantragit clone --depth 1 https://github.com/hashangit/openfusionWhat 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 | $0.00103 | $0.01104 |
| Opus 5 | $0.00051 | $0.00552 |
| Sonnet 5 | $0.00021 | $0.00221 |
| Haiku 4.5 | $0.00010 | $0.00110 |
Grade A, and why
debug-mantra 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 3d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Reliable repro** → capture the exact steps, inputs, and environment as a runnable artifact: failing test, curl script, CLI invocation, replay harness. 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.
Debug Mantra
Four-step discipline for any debug session. Recite verbatim, then apply in order.
Recite this — verbatim, as the first thing in your first response
Mantra:
- First is reproducibility. Can the issue be reproduced reliably?
- Know the fail path. Debugger first; then source trace + knob enumeration; then in-code instrumentation.
- Question your hypothesis. What would disprove it?
- Every run is a breadcrumb. Cross-reference all of them.
Then begin work.
1. Reproduce reliably
Build a runnable repro before anything else.
- Reliable repro → capture the exact steps, inputs, and environment as a runnable artifact: failing test, curl script, CLI invocation, replay harness.
- Flaky repro → the bug is not yet debuggable. Raise the rate first: loop the trigger, parallelise, add stress, narrow timing windows, inject sleeps. 50% flake is debuggable; 1% is not.
- No repro at all → stop. Say so explicitly. Ask the user for env access, captured artifacts (HAR, log dump, core), or permission to instrument. Do not proceed to hypothesise.
Target: a fast (1–5 s), deterministic pass/fail signal. Pin time, seed the RNG, freeze network, isolate filesystem.
2. Know the fail path
Once reproducible, find where the code breaks and what stops it from breaking. The differential narrows the search. Try in this order — escalate only when the prior tactic fails.
- Attach a debugger. If the env supports it, attach and step to the failure site. One breakpoint beats ten logs. Do this before turning any knobs.
- Source trace + knob enumeration. If no debugger (or it can't reach the bug), trace the code path end-to-end and list every knob that can influence the outcome:
- config flags, env vars, feature toggles
- branch conditions, input shape
- timing, concurrency, build options Each knob is a candidate axis to flip in the differential. Flip one at a time.
- In-code instrumentation. If outside knobs can't move the failure, go inside:
printf/ log statements at the suspected fail site, dump the relevant internal state. Tag every probe with a unique prefix (e.g.[DBG-a4f2]) so cleanup is a single grep. Let the trace show where reality diverges from your model.
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.
- 3d ago First seen · 76 lines · 103 tokens per session scan A 6084d056c752
debug-mantra is a skill published in the GitHub repository hashangit/openfusion (34 stars, last pushed 26d ago), licensed MIT. It adds 103 tokens to every session and 1,104 once invoked, about $0.0005 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 2026-08-30.
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