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 agentmods add skills/corridortech/posecap/ad-groundnpx skills add CorridorTech/PoseCap --skill ad-groundgit clone --depth 1 https://github.com/CorridorTech/PoseCapWhat 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 | $0.00133 | $0.01416 |
| Opus 5 | $0.00067 | $0.00708 |
| Sonnet 5 | $0.00027 | $0.00283 |
| Haiku 4.5 | $0.00013 | $0.00142 |
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 2d 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 — 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.
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.
- 2d ago First seen · 95 lines · 133 tokens per session scan A 12c199eac2ac
ad-ground is a skill published in the GitHub repository CorridorTech/PoseCap (190 stars, last pushed 10d ago), licensed Apache-2.0. It adds 133 tokens to every session and 1,416 once invoked, about $0.0007 per session on Opus 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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