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 arcasilesgroup/ai-engineering --skill ai-visual-recapgit clone --depth 1 https://github.com/arcasilesgroup/ai-engineeringWrote 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/arcasilesgroup/ai-engineering/ai-visual-recap)<a href="https://agentmods.dev/skills/arcasilesgroup/ai-engineering/ai-visual-recap"><img src="https://agentmods.dev/badge/skills/arcasilesgroup/ai-engineering/ai-visual-recap/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/arcasilesgroup/ai-engineering/ai-visual-recap"><img src="https://agentmods.dev/badge/skills/arcasilesgroup/ai-engineering/ai-visual-recap.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00116 | $0.09649 |
| Opus 5.5 | $0.00046 | $0.03860 |
| Sonnet 5.5 | $0.00023 | $0.01930 |
| Haiku 4.5 | $0.00012 | $0.00965 |
Grade A, and why
ai-visual-recap 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 27d 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 — 669 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ai-visual-recap
ai-visual-recap creates a visual plan built from a diff, not toward one. It
is the reverse of forward planning: instead of describing the change you are
about to make, you describe the change that was just made, at a higher altitude
than line-by-line review. The same plan data model serves both directions —
schema, API, file, and architecture changes become the same data-model,
api-endpoint, file-tree, and diagram blocks a forward plan would use, only
now they summarize work that exists. A reviewer scans the shape of the change
before spending attention on the literal lines.
Publish As An Agent-Native Plan — Never Inline
The deliverable is ALWAYS a published Agent-Native Plan, created with
create-visual-recap on the Plan MCP connector — NEVER inline chat content (not
Markdown prose, an ASCII sketch, a table, a fenced "wireframe", or a "here's the
recap" summary). A recap's entire value is the hosted, interactive, annotatable
plan; an inline summary is not a degraded recap, it is the thing a recap
replaces. If the plan (or legacy agent-native-plans) tools are not visible,
discover them through the host's tool_search first; if they are still missing,
STOP and give the user the client-specific reconnect step rather than improvising
an inline recap. Before publishing, or whenever a connector or auth error
appears, READ references/connection.md in this skill directory — it is the
single source of truth for the never-inline rule, connector discovery, and the
per-client reconnect steps. Local-files privacy mode (below) is the one
exception.
Local-Files Privacy Mode — read references/local-files.md
When the user wants no hosted Plan database writes — no DB writes, no Plan MCP
publish, fully local/offline/private recaps, or AGENT_NATIVE_PLANS_MODE=local-files
— do not call any hosted Plan tool except the schema-only get-plan-blocks
catalog lookup. Read the diff with the local recap collect-diff / scan /
build-prompt --local-files helpers, author a local MDX folder (set
kind: "recap" and localOnly: true), and preview it with plan local check,
plan local serve --kind recap, and plan local verify --kind recap. Before
using local-files mode, READ references/local-files.md in this skill directory
— it is the single source of truth for the full contract.
What ships with it
4 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.
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.
- 27d ago Changed · +2 lines 07688b9e45c2
- 1mo ago Changed · +31 lines 57c20ff9388e
- 1mo ago Changed · +3 lines f261e0671610
- 1mo ago Changed · +2 lines 92f0bbaf5529
- 1mo ago Changed · +564 lines · -31 tokens per session bb93a0e670d1
- 1mo ago First seen · 67 lines · 147 tokens per session scan A 3af98a5878fd
ai-visual-recap is a skill published in the GitHub repository arcasilesgroup/ai-engineering (61 stars, last pushed 5d ago), licensed Apache-2.0. It adds 116 tokens to every session and 9,649 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
spec-kitty-git-workflow
Understand how Spec Kitty manages git: what git operations Python handles automatically, what agents must do manually, worktree lifecycle, auto-commit behavior, merge execution, and the safe-commit pattern. Triggers: "how does spec-kitty use git", "worktree management", "auto-commit", "who commits what", "git…
spk-admin-git-workflow
Operate Spec Kitty git workflows, worktrees, safe commits, merge preflights, stale state checks, and recovery.
spk-gate-merge
Merge an accepted Spec Kitty mission safely, preserving git invariants, mission state, and post-merge follow-through.
setup-husky-dotnet
Use when configuring Git hooks in .NET projects before team commits occur, to enforce commit message standards and code formatting automatically.
ai-agents-generation-and-release
Operate the ai-agents generation and release machinery, covering the seven buildall.py generators, generateagents.py, the drift gates, the version-free plugin manifests, and the npm publish path. Use when you say regenerate the mirrors, run the drift checks, why is the plugin version gate red, release the npm cli. Do…
ship
Run the four pre-flight checks, then create the PR or validate a contributor branch without touching it. Use when you say ship it, ship this branch, or run pre-flight and open the PR, and run it after review. Do NOT use to open a PR without the gates (use push-pr), and do NOT use to run the review axes themselves (use…