Omnigent is an open-source orchestration layer for running and coordinating different AI coding agents through one system. It is for developers who want to combine agents, apply policies and sandboxing, and continue sessions across devices. The catalogue add-ons extend its agent workflows.
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 omnigent-ai/omnigent --skill resolve-author-fixgit clone --depth 1 https://github.com/omnigent-ai/omnigentWrote 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/omnigent-ai/omnigent/resolve-author-fix)<a href="https://agentmods.dev/skills/omnigent-ai/omnigent/resolve-author-fix"><img src="https://agentmods.dev/badge/skills/omnigent-ai/omnigent/resolve-author-fix/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/omnigent-ai/omnigent/resolve-author-fix"><img src="https://agentmods.dev/badge/skills/omnigent-ai/omnigent/resolve-author-fix.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 | $0.00024 | $0.02963 |
| Opus 5.5 | $0.00010 | $0.01185 |
| Sonnet 5.5 | $0.00005 | $0.00593 |
| Haiku 4.5 | $0.00002 | $0.00296 |
Grade A, and why
resolve-author-fix 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 today.
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.
How it starts
The opening of the file, as written. The whole thing — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Step 2B — Author the fix
No candidate PR exists, so you fix it yourself. Steps 2B.1–2B.5 below are the full author flow; then open a PR in Step 3.
2B.1 — Confirm the shared repro audit
Complete the shared repro audit before changing product code. Reuse its recorded behavioral baseline rather than trusting the recovered verdict or rerunning an unchanged audit. If the test, base, or relevant environment changes, repeat the audit. Ticket-only mode instead establishes its targeted fail→pass proof in 2B.4. The failure-quality checks below elaborate the shared requirement; they do not replace patch inspection or excuse the review path from the same audit.
It must fail because the buggy behavior is observed — a wrong value, an error toast, a traceback, a bad HTTP response, a missing/incorrect UI affordance.
It must not fail merely because it references something that does not exist
yet — an AttributeError/ImportError on a symbol the fix would add, an
element-not-found for UI the fix would introduce, a 404 on a route the fix would
register. That is an existence-check, not a reproduction: it would go green
the moment the symbol exists, regardless of whether the behavior is correct. If
the test fails that way:
- Rewrite it into a behavioral assertion that exercises the real journey and asserts the correct behavior/value, and confirm the rewrite fails for the right reason before proceeding.
- Flag it loudly in your handoff (
test_audit) so a reviewer knows the original repro test was an existence-check and you corrected it.
If the test PASSES on the unfixed tree, it may be stale or unreliable; do not
assume main has fixed the bug. A recovered verdict is a statement
about main AT REPRO TIME, not now. Verify the way repro-agent would: re-drive
enough of the journey to confirm the behavior is genuinely correct on the
current tree, and hunt for the fixing commit (git log on the code the
evidence points at). When it is really fixed, do not manufacture work: stop
with outcome nothing_to_fix, name the fixing commit in root_cause, and
recommend closing the ticket in your prose summary. If the test passes but the
journey still misbehaves, the test was too loose — treat it like the
existence-check case above: rewrite it until it fails on the real, still-live
behavior, and flag the rewrite in test_audit.
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.
- today Changed · +6 lines 653dae3e66c0
- 10d ago Changed · +1 lines 93b8ca2fc265
- 13d ago Changed · +60 lines b3a62c208179
- 19d ago First seen · 151 lines · 24 tokens per session scan A 14dc5192ab96
resolve-author-fix is a skill published in the GitHub repository omnigent-ai/omnigent (10,786 stars, last pushed today), licensed Apache-2.0. It adds 24 tokens to every session and 2,963 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 2026-09-25.
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fix
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systematic-debugging
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