What is debug-mantra?

A four-step routine for investigating software bugs: reproduce the problem, follow where it fails, test whether your explanation could be wrong, and compare all evidence.

Use it to structure debugging sessions, create a repeatable failing test or command, and decide when there is not enough evidence to propose a fix.

It prevents guesswork and premature fixes, especially when a bug happens only sometimes or cannot yet be reproduced.

How hashangit/openfusion configures Claude Code and Codex

Reuse

Borrowing it

Nothing to install: this file belongs to hashangit/openfusion. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/hashangit/openfusion/main/.agents/skills/debug-mantra/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/hashangit/openfusion

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/hashangit/openfusion/debug-mantra/github.svg)](https://agentmods.dev/skills/hashangit/openfusion/debug-mantra)

Your own site

<a href="https://agentmods.dev/skills/hashangit/openfusion/debug-mantra"><img src="https://agentmods.dev/badge/skills/hashangit/openfusion/debug-mantra/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/hashangit/openfusion/debug-mantra"><img src="https://agentmods.dev/badge/skills/hashangit/openfusion/debug-mantra.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 103 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,104 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00103	$0.01104
Opus 5.5	$0.00041	$0.00442
Sonnet 5.5	$0.00021	$0.00221
Haiku 4.5	$0.00010	$0.00110

Measured 2mo ago against content hash 6084d056c752, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo 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.

.agents/skills/debug-mantra/SKILL.md · 76 lines

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.

Read the full file on GitHub · 76 lines

Changes

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.

2mo ago First seen · 76 lines · 103 tokens per session scan A 6084d056c752

Subscribe to this mod's changes

debug-mantra is a skill published in the GitHub repository hashangit/openfusion (35 stars, last pushed 3d ago), licensed MIT. It adds 103 tokens to every session and 1,104 once invoked, about $0.0004 per session on Opus 5.5. 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.

Debugging

debug-mantra next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
debug-mantrahashangit/openfusion	Skill	35	3d ago	MIT	A	103	Claude Code, Codex
systematic-debuggingopen-metadata/OpenMetadata	Skill	16k	yesterday	Apache-2.0	A	37	Claude Code
log-error-digestzebbern/claude-code-guide	Skill	4.7k	3d ago	MIT	A	71	Claude Code, Codex
cwv-optimizeradobe/skills	Skill	319	yesterday	Apache-2.0	A	112	Claude Code
ci-fixersipyourdrink-ltd/bernstein	Skill	1.2k	yesterday	Apache-2.0	A	15	Claude Code, Codex
incident-response-incident-responsermyndharis/antigravity-skills	Skill	1.9k	23d ago	MIT	A	36	Claude Code
triaging-web-analytics-supportPostHog/posthog	Skill	41k	yesterday	none	A	151	Claude Code, Codex

openfusion: Skill for Claude Code