Borrowing it
Nothing to install: this file belongs to CorridorTech/PoseCap. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/CorridorTech/PoseCap/main/.agents/skills/ad-next/SKILL.mdgit clone --depth 1 https://github.com/CorridorTech/PoseCapWrote 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/corridortech/posecap/ad-next)<a href="https://agentmods.dev/skills/corridortech/posecap/ad-next"><img src="https://agentmods.dev/badge/skills/corridortech/posecap/ad-next/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/corridortech/posecap/ad-next"><img src="https://agentmods.dev/badge/skills/corridortech/posecap/ad-next.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 | $0.00111 | $0.02732 |
| Opus 5.5 | $0.00044 | $0.01093 |
| Sonnet 5.5 | $0.00022 | $0.00546 |
| Haiku 4.5 | $0.00011 | $0.00273 |
Grade A, and why
ad-next 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 2mo 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<background_information>
Read-only state survey + prioritized next-action recommendations. Mirrors flutter doctor shape: layer-by-layer status + concrete fix per finding. Complements ad-drift — audit answers "is anything wrong?", next answers "what should I do?".
The skill writes nothing. Output is recommendations the user copies into the next conversation turn or the next CLI invocation.
Codex auto-trigger on description keywords is less mature than Claude Code's. If auto-invocation does not fire when the user asks about workflow status, invoke this skill manually. </background_information>
Do not parse skill bodies. Do not run tests. Do not invoke other skills.
Step 1 — classify scenario before ranking. Layer status is evidence; scenario determines the right next step.
- Fresh / unframed greenfield: no durable product framing, even if a framework scaffold or a few early files already exist. This is a product-framing problem, not an
AGENTS.mdproblem. - Product-framed greenfield: PRD exists, but no meaningful code yet. This is where
/ad-bootstrap,/ad-guidelines, optional/ad-design, then/ad-specbecome the normal sequence. - Brownfield: meaningful code exists and the scan can summarize the current product behavior. Existing code can supply product and architecture evidence;
/ad-bootstrapis scan-first here and may precede PRD backfill. - Feature planning: PRD/spec artifacts exist and have downstream gaps (accepted PRD with no specs, accepted spec with no tasks).
- Implementation in progress: dirty tree, branch ahead of
main, in-progress tasks, blocked tasks, or proposed ADRs. - Maintenance / install hygiene: stale kit state, profile/install mismatch, missing expected conditional skills.
If scenarios overlap, report the strongest active scenario in this order: implementation in progress, maintenance/install hygiene, feature planning, product-framed greenfield, brownfield, fresh/unframed greenfield. If code exists but product behavior cannot be summarized, choose fresh/unframed greenfield rather than brownfield.
Step 2 — layer-by-layer status. Render six sections in this exact order. Use words for status (present, in flight, missing, stale) — no emoji.
Layer 1 — Constitution: WORKFLOW.md (kit-shipped), AGENTS.md / CLAUDE.md (operational guide), GUIDELINES.md (full engineering reference). AGENTS.md missing is not the first greenfield finding when product framing is missing; recommend product discovery / PRD first, then /ad-bootstrap.
What ships with it
1 file 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.
- 2mo ago First seen · 112 lines · 111 tokens per session scan A 236f544ed8ee
ad-next is a skill published in the GitHub repository CorridorTech/PoseCap (265 stars, last pushed 3d ago), licensed Apache-2.0. It adds 111 tokens to every session and 2,732 once invoked, about $0.0004 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.
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