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-adr/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-adr)<a href="https://agentmods.dev/skills/corridortech/posecap/ad-adr"><img src="https://agentmods.dev/badge/skills/corridortech/posecap/ad-adr/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-adr"><img src="https://agentmods.dev/badge/skills/corridortech/posecap/ad-adr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Memory Poisoning · line 16 Skill attempts to fill the context window with filler content, displacing legitimate instructions and safety constraints. This can degrade agent performance or bypass safety boundaries.Fix: Implement context-window management that detects and rejects padding or stuffing attempts. Prioritize system instructions over user-injected content.
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 | $0.00069 | $0.00818 |
| Opus 5.5 | $0.00028 | $0.00327 |
| Sonnet 5.5 | $0.00014 | $0.00164 |
| Haiku 4.5 | $0.00007 | $0.00082 |
Grade A, and why
ad-adr 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<background_information>
Drafts doc/adr/NNNN-<short-title>.md for one architecture decision. Status lifecycle: proposed → accepted → deprecated | superseded by ADR-NNNN.
</background_information>
Step 2 — confirm scope. The ADR captures one decision. If the user's request implies multiple, ask which to write first; the others become follow-up ADRs.
Step 3 — fill from conversation only. Use the template below. Fill Context, Decision, Consequences, and Alternatives Considered from this conversation only — no fabrication. If a section has no signal, ask one question per gap.
Decision must be a directive ("We will…"), not a description.
Consequences lists positive and negative; do not balance for the sake of balance.
Alternatives Considered lists each rejected option with a one-line reason.
Step 4 — write the file. Path: doc/adr/<NNNN>-<short-slug>.md. Slug: kebab-case, ≤6 words. Status: proposed. Date: today, ISO format. Deciders: ask the user.
Stop after writing. Do NOT flip status to accepted — that requires user review.
Status: <proposed | accepted | deprecated | superseded by ADR-NNNN>
Date: <YYYY-MM-DD>
Deciders: <names or roles>
Context
<What is the issue motivating this decision? What forces are at play — technical, organizational, regulatory, cost?>
Decision
<State as a directive: "We will…". One decision per ADR.>
Consequences
<What becomes easier, harder, or different. List positive, negative, and neutral consequences.>
Alternatives Considered
<option>—<why rejected><option>—<why rejected>
<output_contract>
A single new file at doc/adr/<NNNN>-<short-slug>.md. Status proposed. No existing ADRs modified. No invented content.
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 · 68 lines · 69 tokens per session scan A 02844f0762e7
ad-adr is a skill published in the GitHub repository CorridorTech/PoseCap (265 stars, last pushed 3d ago), licensed Apache-2.0. It adds 69 tokens to every session and 818 once invoked, about $0.0003 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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