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 skills add shareAI-lab/lab-skills --skill review-ai-conversationsgit clone --depth 1 https://github.com/shareAI-lab/lab-skillsWrote 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/shareai-lab/lab-skills/review-ai-conversations)<a href="https://agentmods.dev/skills/shareai-lab/lab-skills/review-ai-conversations"><img src="https://agentmods.dev/badge/skills/shareai-lab/lab-skills/review-ai-conversations/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/shareai-lab/lab-skills/review-ai-conversations"><img src="https://agentmods.dev/badge/skills/shareai-lab/lab-skills/review-ai-conversations.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00086 | $0.00846 |
| Opus 5 | $0.00043 | $0.00423 |
| Sonnet 5 | $0.00017 | $0.00169 |
| Haiku 4.5 | $0.00009 | $0.00085 |
Grade A, and why
review-ai-conversations 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 13d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review AI Conversations
Recover the actual conversation bodies, then perform only the review, search, extraction, summary, comparison, or synthesis the user requested.
scope -> recover human + visible AI turns -> requested result
|
`-> optional deeper analysis only when requested
The user's original messages are authoritative for intent. AI replies, summaries, and generated artifacts are useful prior work, not automatic proof that an interpretation or factual claim is correct.
Interpret ordinary-language scope
Infer four things when supplied: Agent or location, time range or conversation IDs, content to find or analyze, and desired output. The user may specify any subset. Ask only when two plausible scopes would materially change the recovered conversations or answer. An explicit file, Store path, session ID, or named conversation does not need time-index discovery.
Load only the required guidance
If the user supplied conversation text directly, analyze it without Store references.
When local Stores must be read:
- Read
references/retrieval.mdcompletely. - Read only the matching product adapter completely:
- Read
references/lineage-and-intent.mdonly when forks, child Agents, copied history, context rollover, or intent drift matter. - Read
references/analysis-lenses.mdonly when the user requests question clustering, problem-space mapping, artifact/conclusion review, brainstorming, open questions, or insights.
For several products, recover each Store independently. Combine recovered
conversation families only after applying each product's identity and lineage rules.
Do not load unrelated product adapters. Do not read
adapter-maintenance-evidence.md during
ordinary review; read it only when maintaining or re-verifying adapters.
What ships with it
11 files 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.
- references/adapter-maintenance-evidence.md 11 KB
- references/analysis-lenses.md 2.3 KB
- references/lineage-and-intent.md 1.7 KB
- references/retrieval.md 2.9 KB
- references/sources/claude-code.md 3.5 KB
- references/sources/codex.md 5.0 KB
- references/sources/cursor-cli.md 1.5 KB
- references/sources/cursor-ide.md 2.3 KB
- references/sources/cursor.md 1.8 KB
- references/sources/grok-build.md 4.0 KB
- references/sources/opencode.md 3.2 KB
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.
- 13d ago First seen · 82 lines · 86 tokens per session scan A f01ae9dbdefb
review-ai-conversations is a skill published in the GitHub repository shareAI-lab/lab-skills (314 stars, last pushed 17d ago), licensed Apache-2.0. It adds 86 tokens to every session and 846 once invoked, about $0.0004 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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