PoseCap: Skill for Codex

.agents/skills/ad-ground/SKILL.md

ad-ground is a skill for Codex from CorridorTech/PoseCap. It costs 133 tokens per session (1,416 once invoked), scanned A, original, Apache-2.0.

A research workflow for planning a non-trivial software change before implementation. It combines official documentation, tested examples from other projects, patterns already in the repository, and relevant Git history.

In plain words
What is it for?
Use it to investigate languages and libraries, find comparable implementations, inspect local conventions and past changes, and produce an evidence-based implementation direction without writing code.
Why use it?
It reduces the risk of choosing an approach based on incomplete research or assumptions. It also requires a clear justification when the proposed solution differs from the established path.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions Claude Code; installed under .agents/ (shared by several agents); mentions Codex.

This is CorridorTech/PoseCap's own configuration. It tells Codex how to work on PoseCap itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything PoseCap configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/CorridorTech/PoseCap/main/.agents/skills/ad-ground/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/CorridorTech/PoseCap

Made for: 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 ad-ground

README.md
[![agentmods](https://agentmods.dev/badge/skills/corridortech/posecap/ad-ground/github.svg)](https://agentmods.dev/skills/corridortech/posecap/ad-ground)
Your own site
<a href="https://agentmods.dev/skills/corridortech/posecap/ad-ground"><img src="https://agentmods.dev/badge/skills/corridortech/posecap/ad-ground/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 ad-ground

Your own site · 80×15
<a href="https://agentmods.dev/skills/corridortech/posecap/ad-ground"><img src="https://agentmods.dev/badge/skills/corridortech/posecap/ad-ground.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 133 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,416 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00133 $0.01416
Opus 5.5 $0.00053 $0.00566
Sonnet 5.5 $0.00027 $0.00283
Haiku 4.5 $0.00013 $0.00142

Measured 2mo ago against content hash 12c199eac2ac, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ad-ground 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.

.agents/skills/ad-ground/SKILL.md · 95 lines

How it starts

The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.

<background_information> Implements WORKFLOW §4 + §5 end-to-end as one research pass. The four sources are joined by AND, not OR — every non-trivial change runs the full research pass, then synthesizes a happy path, then justifies any deviation. Output is the input to whatever skill or freeform turn produces the implementation plan; this skill does not write code.

Codex auto-trigger on description keywords is less mature than Claude Code's. If auto-invocation does not fire on a non-trivial change, invoke this skill manually before implementing. </background_information>

Step 1 — four-source research pass, all four required:

Source A — official documentation. For each language and library in scope, cite the canonical doc URL and version. Read the relevant section. Ask the user for a known-good link rather than fabricating one. Output: bulleted citations, one per language/library, each with URL plus a one-line summary.

Source B — validated implementation references. ≥1 (prefer 2–3) public reference (open-source repo, Stack Overflow / forum answer, blog post, gist) solving the same technical research scope with similar techniques. Match is technical, not domain. Cite <source>:<locator> — <repo>:<path>:<line-range> for repos, <URL> for Stack Overflow / forum / blog / gist — and quote the relevant block. Never paraphrase from training memory. If search is inconclusive, ask the user for a known reference.

Source C — in-repo examples. Grep / glob for analogous patterns. Cite <file>:<line> plus a one-line description of how the existing example handles the same shape. If the codebase has no analog, state that explicitly.

Source D — git history. Run git log --all --oneline -- <relevant-paths>, git log --all --grep=<keyword>, sweep sibling active branches. Cite <commit-sha> plus touching file path and a one-line description. If empty, state "no prior attempt found." Narrow with --grep or -S on multi-thousand-commit repos.

Step 2 — happy path synthesis. In one paragraph, name the most-grounded approach for the research scope and cite at least one source per Source A / B / C. Source D included when it produced a hit; otherwise mark "no prior attempt found." The paragraph is the canonical answer to "what is the canonical, idiomatic way to solve this here?"

Step 3 — deviation gate. If the implementation about to be written deviates from the happy path, write the justification first. Must name the specific constraint, evidence, or trade-off forcing the deviation — generic "we want it differently" is insufficient. If the justification cannot be written confidently, loop back to Step 1 and look harder; do not deviate without it. Prescriptive gate, not descriptive — write the answer down.

Step 4 — confidence checkpoint. Soft verdict on:

  • A consulted (≥1 official-doc citation per language/library)
  • B consulted (≥1 implementation-reference citation, with cite-and-fetched code)
  • C consulted (in-repo analog cited or "no analog found" stated)
  • D checked (commits / branches surveyed; hit cited or "no prior attempt found")
  • Happy path declared (Step 2)
  • Deviation, if any, justified (Step 3)

If any check fails, surface the gap to the user and ask before proceeding. Do not block. The user retains authority to skip; the discipline is in surfacing.

<output_contract> A single message structured as:

## Recortte
<one sentence>

## Source A — official documentation
- <lang/lib>: <URL@version> — <one-line summary>

## Source B — validated implementation references
- <repo>:<path>:<line-range> — <one-line summary>   # repo form

Source C — in-repo examples

  • : —
  • (or: "no analog found in the codebase")

Source D — git history

  • —
  • (or: "no prior attempt found")

Happy path

<one paragraph synthesizing A + B + C + D, with citations>

Proposed implementation vs happy path

  • aligned:
  • deviates:
    • :

Confidence checkpoint

  • A consulted: yes / no —
  • B consulted: yes / no —
  • C consulted: yes / no —
  • D checked: yes / no —
  • happy path declared: yes
  • deviations justified: yes / no / n.a.

Read the full file on GitHub · 95 lines

Files

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.

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. 2mo ago First seen · 95 lines · 133 tokens per session scan A 12c199eac2ac

Subscribe to this mod's changes

ad-ground is a skill published in the GitHub repository CorridorTech/PoseCap (265 stars, last pushed 3d ago), licensed Apache-2.0. It adds 133 tokens to every session and 1,416 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.

Related

Other skills, from other repositories

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens