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/me2resh/apexyard/debugnpx skills add me2resh/apexyard --skill debuggit clone --depth 1 https://github.com/me2resh/apexyardWhat 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 | $0.00025 | $0.04425 |
| Opus 5 | $0.00013 | $0.02212 |
| Sonnet 5 | $0.00005 | $0.00885 |
| Haiku 4.5 | $0.00003 | $0.00443 |
Grade A, and why
debug 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
The cost of one extra `curl`, one extra `Read`, one extra "what does the server actually return?" — is one minute. The cost of one wrong fix that ships, deploys, blocks the user, and then has to be diagnosed again is hou How it starts
The opening of the file, as written. The whole thing — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/debug — Structured Debug Methodology
Most "stuck" debug sessions look the same: pattern-match on the symptom, propose a fix, ship it, find a different symptom, repeat. Each cycle costs a deploy + a CEO approval + the user's patience. This skill enforces the discipline that prevents the loop:
- Capture the symptom precisely — the exact URL, the exact response, the exact step that fails.
- Read the architecture before guessing — map every layer the request touches.
- Form a hypothesis ladder — 3–5 candidate causes ordered by probability, each with an explicit evidence test.
- Gather evidence first, fix second — never ship a fix without a verification step that would have caught the wrong hypothesis.
- Verify the fix against the original evidence — re-run the symptom check, not just the unit tests.
The cost of one extra curl, one extra Read, one extra "what does the server actually return?" — is one minute. The cost of one wrong fix that ships, deploys, blocks the user, and then has to be diagnosed again is hours.
When to invoke
Trigger this skill when any of the following is true:
- A previous fix attempt didn't resolve the symptom
- The symptom involves multiple layers (CDN, framework, backend, auth, network)
- Pattern-matching on the symptom produces more than one plausible cause
- The CEO has had to point out the bug exists more than once
If the cause is genuinely obvious (typo in a string, off-by-one in a loop), don't invoke this — just fix it. The skill is for the cases where you're tempted to guess.
Process
Step 1 — Capture the symptom
Write down, in this exact shape:
URL / endpoint: <exact URL or command>
Action: <what the user did>
Expected: <what should have happened>
Observed: <what actually happened>
Repro recipe: <minimal steps that reproduce>
Surface evidence: <screenshot URL, console output, response headers, logs>
If any field is "I'm not sure" — STOP and gather it before going further. Hypotheses built on a fuzzy symptom are guesses dressed up.
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 · 286 lines · 25 tokens per session scan A 2120a32f71df
debug is a skill published in the GitHub repository me2resh/apexyard (497 stars, last pushed 2d ago), licensed MIT. It adds 25 tokens to every session and 4,425 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 2026-08-30.
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