otto-impact-assessment

otto-impact-assessment is a skill for Claude Code, Codex from omnigent-ai/omnigent. It costs 20 tokens per session (1,302 once invoked), scanned A, original, Apache-2.0.

A final-change review that examines the complete code difference, its consumers, tests, configuration, dependencies, and workflows. It records risks, affected behavior, and gaps before success is claimed.

In plain words
What is it for?
Use it to compare a branch with its target, trace changed helpers to their callers, identify risks, and document focused tests and remaining gaps.
Why use it?
It helps find breakage outside the files originally changed and checks whether tests and evidence cover the important behavior. It applies to both feature work and pull-request review.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to compare a branch with its target, trace changed helpers to their callers, identify risks, and document focused tests and remaining gaps.

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Install with agentmods
npx agentmods add skills/omnigent-ai/omnigent/otto-impact-assessment
About the project

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.

omnigent-ai/omnigent · 10,855 stars · on GitHub · omnigent.ai

Install

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.

Any agent
npx skills add omnigent-ai/omnigent --skill otto-impact-assessment
Clone the repo
git clone --depth 1 https://github.com/omnigent-ai/omnigent

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.

agentmods badge for otto-impact-assessment

README.md
[![agentmods](https://agentmods.dev/badge/skills/omnigent-ai/omnigent/otto-impact-assessment/github.svg)](https://agentmods.dev/skills/omnigent-ai/omnigent/otto-impact-assessment)
Your own site
<a href="https://agentmods.dev/skills/omnigent-ai/omnigent/otto-impact-assessment"><img src="https://agentmods.dev/badge/skills/omnigent-ai/omnigent/otto-impact-assessment/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.

agentmods 80×15 button for otto-impact-assessment

Your own site · 80×15
<a href="https://agentmods.dev/skills/omnigent-ai/omnigent/otto-impact-assessment"><img src="https://agentmods.dev/badge/skills/omnigent-ai/omnigent/otto-impact-assessment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,302 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00020 $0.01302
Opus 5.5 $0.00008 $0.00521
Sonnet 5.5 $0.00004 $0.00260
Haiku 4.5 $0.00002 $0.00130

Measured today against content hash dd541b1a4c3f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

otto-impact-assessment 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.

dev/agent-skills/otto-impact-assessment/SKILL.md · 85 lines

How it starts

The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Shared impact assessment — every resolution path

Before claiming success (Resolve: fixed) or approving a PR, check what the full final diff could break beyond the requested change. This applies to every mode (Resolve: author, existing-PR review, ticket-only, and review-remediation), including skip_push and workflow-owned publication. (Resolve: ticket-only and review-remediation still do not require an inherited repro; their publication/branch permissions are unchanged.)

  1. Inspect the candidate and its consumers. Start the assessment while investigating, then refresh it after the last edit. Record the target branch's full tip SHA as base_sha and the candidate's full HEAD SHA as head_sha. Inspect git diff <base_sha>...<head_sha> plus staged, unstaged, and untracked source/test changes. Include configuration, dependencies, and workflows; runtime prompts count as behavior changes even when stored in Markdown. In review modes, inspect the whole PR, not only edits made in this run. Follow shared helpers to their callers and existing tests. For each meaningful risk, identify the changed files, affected behavior, consumers, invariant to preserve, and focused check. A passing original repro is not coverage for every consumer.

  2. Exercise the relevant boundaries. Select checks from the actual change:

    Changed behavior Required focused coverage when applicable
    Shared startup, configuration, auth, or serialization Existing/default configurations and other affected consumers; exercise configuration creation, transport/decoding, and real process startup together. Correctly constructed objects supplied directly to a unit test do not cover that path.
    Sandboxing The applicable real OS sandbox with its intended gates enabled. A disabled gate or an unsupported/skipped sandbox test leaves that boundary uncovered.
    UI state or interaction The reported journey and affected adjacent journeys on the candidate build, including relevant selection, persistence, or reconnect behavior.
    Environment-derived defaults Ordinary configuration and relevant ambient variables populated; apply the agent's hermetic check (Resolve: 2B.5).
    Performance or timing Measure the claimed quantity under the relevant workload. Distinguish simulated-clock tests from elapsed-time measurements; functional success alone does not establish latency or throughput.

    Use existing tests where they cover the invariant; add a behavioral regression test only for a meaningful gap. Tests of the requested change follow their mode's baseline requirements (Resolve: review-remediation still skips fail-before proof). Checks protecting previously correct behavior may pass on both base and candidate; do not manufacture a failing baseline for them. Run the focused checks for each affected consumer/boundary, even when they live in a different module. Do not run the full repository suite locally. Do not weaken isolation or change global rollout settings to obtain a passing result.

  3. Record observed results in impact_assessment. Each risk names its check, result (passed, failed, blocked, or not_run), and retained evidence. Reference actual command/output or CI run artifacts; include the tested revision/build, dependency pins, and relevant environment/feature gates. Confirm the test imports and exercises this candidate, not another checkout, installed copy, stale SPA, or older CI runtime. Record worktree_state as clean only when the tested source/tests are clean; otherwise name the uncommitted source/test/support files and their content hashes. Artifact-only dirt can be noted separately. A HEAD SHA alone does not identify dirty code.

    Distinguish component tests, real process/sandbox checks, and live bot checks. An unrun, skipped, xfailed, or setup-failing required check is not a pass: name the missing boundary and reason in uncovered_boundaries. A large green test count or an unrelated green CI job does not fill that gap. Evidence references describe what was retained; never invent an execution record.

  4. Refresh before delivery. Re-read the final diff, Git status, and current base/head before every final handoff or review verdict. For an existing PR, compare with the live GitHub base/head SHAs, not just local refs. If the author moved the head, follow the mode's update/retry procedure without approving from old results. After a retry, new PR head, merge/rebase, CI/Polly fix, or changed code, assertions, dependencies, or relevant environment, rebuild the assessment and rerun affected checks. Reuse a result only when its tested contents and context still match; retain its original identity rather than relabeling old evidence as a new execution. Commit hooks can change tested files too. Keep earlier evidence and the saved checkpoint; never reset work to make the identities match.

  5. Let gaps affect the verdict. Success (Resolve: fixed) requires the original behavior to be proven as its mode requires, all identified regression risks checked, and no uncovered required boundary. A discovered regression must be repaired and checked before success or approval. If a change is only partly verified, preserve the work, use the partial outcome (Resolve: partially_fixed), and name the failing/unrun checks in remaining_work; use needs_more_info when verification cannot establish a resolution. Setup failures are blockers, not proof of a product regression. A missing recording alone still follows the agent's recording rules.

Read the full file on GitHub · 85 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.

  1. today First seen · 85 lines · 20 tokens per session scan A dd541b1a4c3f

Subscribe to this mod's changes

otto-impact-assessment is a skill published in the GitHub repository omnigent-ai/omnigent (10,855 stars, last pushed today), licensed Apache-2.0. It adds 20 tokens to every session and 1,302 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 2026-10-17.

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