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-investigategit 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-investigate)<a href="https://agentmods.dev/skills/omnigent-ai/omnigent/resolve-investigate"><img src="https://agentmods.dev/badge/skills/omnigent-ai/omnigent/resolve-investigate/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-investigate"><img src="https://agentmods.dev/badge/skills/omnigent-ai/omnigent/resolve-investigate.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 | $0.00019 | $0.01528 |
| Opus 5.5 | $0.00008 | $0.00611 |
| Sonnet 5.5 | $0.00004 | $0.00306 |
| Haiku 4.5 | $0.00002 | $0.00153 |
Grade A, and why
resolve-investigate 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 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.
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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Establish the cause and intended behavior
Use this in every mode before choosing assertions or changing code. Keep the
investigation proportional to the reported problem; reuse evidence that still
matches the checkout. Record concise findings in the existing root_cause,
fix_summary, and test_audit fields, not a separate report.
Before editing product code, complete the applicable checks below and record their commands and observations. A plan to check later is not evidence. When a check cannot run, name the concrete blocker and keep the cause unresolved.
- Reconstruct the report. Read the report and relevant discussion, including linked evidence. Separate reported facts, observations, and hypotheses. Pin the entry point, build, harness, authentication mode/profile, configuration, and starting state. Mark unknowns; do not silently swap an OAuth user login for a service principal or PAT because they produce similar errors.
- Distinguish symptom from cause. Trace the actual path from trigger to failure. Treat the reporter's diagnosis, Repro leads, and proposed fixes as hypotheses. Check the strongest competing explanation suggested by code, logs, or history with an observation or focused test that distinguishes it. Explain why the selected cause accounts for the reported configuration and symptom. If no credible alternative emerges, say so briefly; do not invent one or implement a plausible generic fix without evidence. Exercise the reported configuration through the implementation being diagnosed. Controlled external services, clocks, and credentials are useful substitutes; replacing the suspected component with canned results assumes the cause instead of testing it. For example, a fake token factory proves how its caller handles those tokens, not how the configured credential provider behaves. Record which parts actually ran and which were substituted. A fail-before/pass-after test of that substitute does not confirm the incident. For credential-lifetime or recovery hypotheses, first establish a working control through the reported credential-selection code and SDK. When external auth services are unavailable or sanitized, substitute only boundaries this provider uses, with bounded local services and fake credentials. Static credentials need no invented issuance or refresh service. Keep application token resolution and the installed SDK intact. Record which provider actually returns the credential, including any fallback, using its runtime type or call trace. Starting SDK configuration or discovery does not prove SDK-provider success. Bound discovery and subprocess calls with timeouts. Get credential resolution working on its own before adding the host/reconnect loop. If initialization hangs, inspect the blocking stack. If it identifies external configuration metadata/discovery calls, provide controlled responses and record whether the control reached them. Preserve other concrete blockers. Load an isolated reported profile and use its credential at the caller's transport boundary. Keep the same application credential object while exercising the suspected expiry or rejection; record whether it refreshes or recovers before proposing a cache reset. Mocking that object or its factory cannot satisfy this check. Retain the controlled boundaries and remaining incident uncertainty. If you change this path, rerun the same configured control and discriminator on the candidate. A new mock-only regression test cannot verify recovery through the implementation you diagnosed; limit the fix claim accordingly.
- Find the design intent. Read nearby tests and documentation, targeted
git log -S/git blame, and relevant commit/PR discussion. Cite the source and revision; distinguish documented rationale from inference and note unavailable history. A fallback, fail-open path, default, or restriction may be deliberate. Do not reverse it just to satisfy a repro assertion. Check whether later decisions supersede the original rationale. Follow the reported path to any shared registry, default, or policy definition that governs the behavior, then read its rationale, relevant callers, and tests. Do not infer the full contract from one local fallback. Even when the local fallback is explicit, follow the phase or operation type to its declaration and inspect other consumers for a shared table or default controlling error behavior. Cite that declaration, or a bounded search if absent. Follow imported policy symbols to their defining files. Search for the declaration and other consumers; a filename in search output or a symbol in a caller is not a definition read. Record the controlling symbol/value, definition path, historical rationale, and pinning test intest_audit. If there is no shared definition, record the search that established that. - Choose and explain. Preserve intended behavior when correcting the defect. For a necessary policy change, explain the old behavior, the proposed behavior, affected users/configurations, alternatives, and supporting evidence. Existing tests are evidence of intent, not unquestionable policy. Repair unsupported repro expectations with the original evidence preserved; label tests of a proposed policy as such, not proof the old policy was a bug. Before adding a retry, trace the retried operation's state writes and external effects. If there are none, record the inspected read-only path. Otherwise, test failure after an effect commits but before success reaches the caller; retry counts alone do not show safety. Preserve existing behavior when the proposed retry lacks that evidence, and record the unsupported proposal as remaining work.
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 Changed · +43 lines 1c68ec103cbb
- 11d ago Changed · +14 lines 7e1dd0d7fe66
- 12d ago First seen · 60 lines · 19 tokens per session scan A 959f5d67a422
resolve-investigate is a skill published in the GitHub repository omnigent-ai/omnigent (10,832 stars, last pushed today), licensed Apache-2.0. It adds 19 tokens to every session and 1,528 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 2026-10-04.
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