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 honeydew-ai/honeydew-ai-coding-agents-plugins --skill conversation-reviewgit clone --depth 1 https://github.com/honeydew-ai/honeydew-ai-coding-agents-pluginsWrote 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/honeydew-ai/honeydew-ai-coding-agents-plugins/conversation-review)<a href="https://agentmods.dev/skills/honeydew-ai/honeydew-ai-coding-agents-plugins/conversation-review"><img src="https://agentmods.dev/badge/skills/honeydew-ai/honeydew-ai-coding-agents-plugins/conversation-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/honeydew-ai/honeydew-ai-coding-agents-plugins/conversation-review"><img src="https://agentmods.dev/badge/skills/honeydew-ai/honeydew-ai-coding-agents-plugins/conversation-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Data Exfiltration · line 23 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
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 | $0.00062 | $0.02400 |
| Opus 5 | $0.00031 | $0.01200 |
| Sonnet 5 | $0.00012 | $0.00480 |
| Haiku 4.5 | $0.00006 | $0.00240 |
Grade A, and why
conversation-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 12d 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 — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
This skill is for semantic and context layer curators who want to use accumulated user feedback to drive continuous improvement. It structures the work into two phases:
- Categorize — review past conversations with feedback and classify each piece of feedback into an actionable improvement category
- Act — turn the categorized feedback into a concrete list of changes, then apply them to the semantic model or context layer on a branch
Prerequisites
Before starting, ensure a workspace and branch are set. Use get_session_workspace_and_branch to check the current context. If nothing is set, use list_workspaces and set_session_workspace_and_branch. See the workspace-branch skill for the full reference.
You will be reading the model as-is (use prod branch for reading), then applying changes on a development branch later.
Phase 1: Collect Conversations
1.1 Fetch conversations with feedback
Use list_analysis_chats to retrieve past conversations. Focus on those that have received feedback — positive or negative.
list_analysis_chats(limit=50, offset=0)
Paginate with offset until you have covered the desired time window or conversation count. For each conversation, note:
conversation_idtitlefeedback(the stored feedback text, if any)agentanddomaincreated_atandcreated_by
Filter to conversations with feedback. Skip conversations with no feedback unless the user explicitly wants to review all conversations.
If a conversation has no feedback or the stored feedback is inaccurate, use provide_analysis_feedback to record the correct label before categorizing:
provide_analysis_feedback(conversation_id="abc123", feedback="Data Issue: net revenue metric missing")
provide_analysis_feedback(conversation_id="def456", feedback="Good")
This keeps the stored feedback in sync with the categorization, so future reviews start from an accurate baseline.
1.2 Read the full conversation when needed
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.
- 12d ago First seen · 250 lines · 62 tokens per session scan A 55e4b115bb2a
conversation-review is a skill published in the GitHub repository honeydew-ai/honeydew-ai-coding-agents-plugins (39 stars, last pushed 13d ago), licensed Apache-2.0. It adds 62 tokens to every session and 2,400 once invoked, about $0.0003 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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