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 skills add suboss87/FDEOps --skill debuggit clone --depth 1 https://github.com/suboss87/FDEOpsWrote 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.
[](https://agentmods.dev/skills/suboss87/fdeops/debug)<a href="https://agentmods.dev/skills/suboss87/fdeops/debug"><img src="https://agentmods.dev/badge/skills/suboss87/fdeops/debug/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.
<a href="https://agentmods.dev/skills/suboss87/fdeops/debug"><img src="https://agentmods.dev/badge/skills/suboss87/fdeops/debug.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00037 | $0.00252 |
| Opus 5.5 | $0.00015 | $0.00101 |
| Sonnet 5.5 | $0.00007 | $0.00050 |
| Haiku 4.5 | $0.00004 | $0.00025 |
Grade A, and why
debug 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 12d 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.
What it actually says
debug
Purpose
A diagnosed failure, repair and regression evidence.
Before investigating or acting:
- Read the task context contract.
- Read the method.
Load further references only when the task needs them. Everything linked is included in this skill; no other skill pack is required.
Principles
- Work directly from the supplied permitted context. Standalone work does not require an engagement folder or initialization. Record filenames in the method are optional persistence destinations when no engagement is bound.
- If called by @fde, reuse its current sanitized packet and scope. Do not restart setup, discovery or questions already answered.
- The task context contract controls persistence and authority in both modes. Preserve unknowns and distinguish implementation, verification, deployment and acceptance.
- Use the customer's repository instructions and available tools. Report a missing capability or unrun check honestly; do not claim that installing a skill provisions infrastructure.
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- .fde-generated.json 1.1 KB
- agents/openai.yaml 190 B
- references/build.md 4.7 KB
- references/debug.md 3.1 KB
- references/eval-pack.md 5.9 KB
- references/integrate.md 4.7 KB
- references/qa.md 2.6 KB
- references/review.md 5.3 KB
- references/ship.md 10 KB
- references/task-context.md 6.6 KB
- references/verification.md 5.5 KB
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.
- 12d ago Changed f30da7cf50e3
- 1mo ago Changed · +4 lines · +6 tokens per session 00b6f8c7c56b
- 1mo ago First seen · 22 lines · 31 tokens per session scan A 7862670d5c02
debug is a skill published in the GitHub repository suboss87/FDEOps (969 stars, last pushed 9d ago), licensed MIT. It adds 37 tokens to every session and 252 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-16.
Other skills, from other repositories
lcx-report-bug
Create a high-signal bug issue or PR in the repo that owns the defect. Use this whenever the user asks to report, file, open, or triage a LazyCodex, lazycodex-ai, omo-codex, Codex plugin, or upstream Codex CLI bug, especially when they need source-backed root cause, reproduction steps, fix guidance, and GitHub routing.
ast-grep
Searches and rewrites code by AST shape across 25 languages. Use when the target is a syntax pattern (every call/class/import shaped like X, a codemod, a YAML rule) rather than literal text; for plain strings, comments, or filenames, use rg.
debugging
Runs a hypothesis-driven debugging loop across any language or binary, escalating to orthogonal oracle angles and locking the fix with a failing test. Use for crashes, silent failures, hangs, wrong responses, memory leaks, async misbehavior, or reverse engineering.
lcx-doctor
Diagnose LazyCodex and Codex CLI installation health against the latest sources. Use whenever the user asks for a doctor or health check, says LazyCodex, lazycodex-ai, omo-codex, or Codex behaves oddly after an install, update, or config change, suspects a stale, drifted, or broken setup, or wants the local install…
remove-deadcode
Remove unused code from this project with ultrawork mode, LSP-verified safety, atomic commits. Triggers: remove dead code, dead code, cleanup, remove unused.
watch
Observe actual video and sound over the whole file or named ranges, with timestamped evidence and honest coverage. Use complementary playback, listening and targeted frames; return observations without edits or verdicts.