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/lit-review/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/lit-review)<a href="https://agentmods.dev/skills/victoriacity/openakari/lit-review"><img src="https://agentmods.dev/badge/skills/victoriacity/openakari/lit-review/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/victoriacity/openakari/lit-review"><img src="https://agentmods.dev/badge/skills/victoriacity/openakari/lit-review.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00024 | $0.01209 |
| Opus 5 | $0.00012 | $0.00605 |
| Sonnet 5 | $0.00005 | $0.00242 |
| Haiku 4.5 | $0.00002 | $0.00121 |
Grade A, and why
lit-review 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 10d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/lit-review [project path]
You are conducting a literature review on the given topic. If a project path is provided, focus the review on that project's needs — read the project README first to understand the mission, open questions, and what's already known.
Step 1: Understand the need
- If a project path is given, read its README. Identify open questions and gaps that literature could address.
- Identify which CI layers are most relevant to the topic.
Step 2: Search
Use WebSearch to find relevant papers, technical reports, and blog posts. Search for:
- The topic directly
- The topic combined with CI-relevant terms (e.g., "LLM judge evaluation metrics", "3D asset quality benchmark")
- Key authors or venues if known from prior literature notes
Aim for breadth first — collect 8-15 candidate sources.
Step 3: Triage
For each candidate, make a quick judgment:
- Load-bearing: Directly addresses an open question or gap. Must read in detail.
- Contextual: Provides useful background or methodology. Worth a note.
- Incremental: Minor variation on known work. Skip unless nothing better exists.
Read load-bearing and contextual papers using WebFetch. Skip incremental ones.
Step 4: Verify and write literature notes
For each paper worth keeping, you MUST mechanically verify the URL before creating a note. This is mandatory — never create notes from memory alone.
Verification procedure (per paper):
- Fetch the URL using WebFetch. For arxiv URLs, fetch the abstract page (e.g.,
https://arxiv.org/abs/XXXX.XXXXX). - Title match: Compare the fetched page title against the claimed title. Minor formatting differences are acceptable; a completely different topic is a FAIL.
- Author match: Confirm at least one claimed author appears on the fetched page.
- On PASS: Create the literature note with
Verified: YYYY-MM-DD(today's date). - On FAIL: Do NOT create a literature note. Record the topic as a gap in the review summary. Never substitute a fabricated citation.
- On inconclusive (paywall, 404, timeout): Create the note with
Verified: falseand flag for manual verification. Do not cite in publications until verified.
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.
- 10d ago First seen · 119 lines · 24 tokens per session scan A 2c5d8c17859b
lit-review is a skill published in the GitHub repository victoriacity/openakari (47 stars, last pushed 6mo ago), licensed MIT. It adds 24 tokens to every session and 1,209 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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