Borrowing it
Nothing to install: this file belongs to victoriacity/openakari. 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/victoriacity/openakari/main/.claude/skills/publish/SKILL.mdgit clone --depth 1 https://github.com/victoriacity/openakariWrote 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/victoriacity/openakari/publish)<a href="https://agentmods.dev/skills/victoriacity/openakari/publish"><img src="https://agentmods.dev/badge/skills/victoriacity/openakari/publish.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 | $0.00021 | $0.01705 |
| Opus 5 | $0.00010 | $0.00852 |
| Sonnet 5 | $0.00004 | $0.00341 |
| Haiku 4.5 | $0.00002 | $0.00170 |
Grade A, and why
publish 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.
How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/publish
You are preparing a research paper for submission. Your job is to take an existing draft through a structured publication pipeline: verify completeness, check citations, format for the target venue, create figures, anonymize, and produce a submission-ready artifact.
The argument is a project name (reads publications/ directory) or a direct path to a paper file.
Step 0: Locate and assess the paper
- Find the paper draft. If given a project name, look in
projects/<name>/publications/. If given a path, read it directly. - Read the paper and identify:
- Current state (draft, formatted, submission-ready)
- Target venue (check for venue selection experiment or task notes)
- Deadline (check TASKS.md for deadline notes)
- Content completeness (are all sections filled in?)
- Read the project README and TASKS.md for context on what the paper covers.
Step 0.5: Synthesis pre-flight audit (mandatory)
Before assessing content, verify that the upstream data the paper builds on is actually correct. Follow the procedure at docs/sops/synthesis-preflight-audit.md:
- Enumerate upstream sources — list every experiment, analysis, and literature note the paper cites
- Flag provisional data — check
status:anddata_quality:fields in EXPERIMENT.md frontmatter - Verify key numerical claims — for numbers that appear in the abstract, tables, or drive conclusions, trace to the computational source (script + data file) and re-verify. Do NOT accept numbers copied from text.
- Verify literature citations — check
Verified:field in literature notes; fetch URLs for any unverified citations - Check cross-experiment comparisons — verify denominators match and are explicitly stated
- Gate decision — if any key claim is unverifiable or mismatched, fix before proceeding to Step 1
This step catches the most common synthesis failure: treating prior session outputs as verified ground truth when they may contain contaminated data, stale numbers, or fabricated citations (7 documented incidents — see projects/akari/feedback/feedback-incorrect-experiment-contamination-analysis-2026-03-01.md).
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 · 169 lines · 21 tokens per session scan A d132034a5a43
publish is a skill published in the GitHub repository victoriacity/openakari (47 stars, last pushed 6mo ago), licensed MIT. It adds 21 tokens to every session and 1,705 once invoked, about $0.0001 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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