Borrowing it
Nothing to install: this file belongs to Artexis10/exomem. 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/Artexis10/exomem/main/.codex/skills/openspec-verify-change/SKILL.mdgit clone --depth 1 https://github.com/Artexis10/exomemWrote 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/artexis10/exomem/openspec-verify-change)<a href="https://agentmods.dev/skills/artexis10/exomem/openspec-verify-change"><img src="https://agentmods.dev/badge/skills/artexis10/exomem/openspec-verify-change.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00032 | $0.01615 |
| Opus 5 | $0.00016 | $0.00807 |
| Sonnet 5 | $0.00006 | $0.00323 |
| Haiku 4.5 | $0.00003 | $0.00161 |
Grade A, and why
openspec-verify-change 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 8d 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 8d ago First seen · 173 lines · 32 tokens per session scan A 8b47bf0dce2e
openspec-verify-change is a skill published in the GitHub repository Artexis10/exomem (10 stars, last pushed today), licensed AGPL-3.0. It adds 32 tokens to every session and 1,615 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
code-review
Use before opening a pull request, marking one ready for review, or pushing further commits to a branch with an open pull request — or when asked to review a diff or a PR. Reviews the cumulative diff against the project's written commitments and reports evidence-grounded findings; works with any coding agent and needs…
kb
Set up, evolve, or operate a hraness/kb local-first Markdown knowledge base for coding-agent memory. Use when a user asks to design KB conventions or a recurring KB ritual; search or query a KB or Obsidian vault; load or recover repository context, plans, decisions, concepts, backlinks, semantic search, or Git…
github-deep-review
GitHub deep review: bugs, PRs, best fix, stale-or-real, read code first.
grooming
Shape ambiguous work into an agreed, executable plan. Use when the user asks to groom, scope, decompose, or prepare work before implementation.
research
Investigate an uncertain question and produce an evidence-backed decision. Use when the user asks to research, compare approaches, validate an assumption, or reduce uncertainty.
grooming-evidence
Ground work grooming in existing product evidence. Use while shaping tasks from code, documentation, tracker state, and user feedback.