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.
git clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-pluginWrote 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/agents/ai-analyst-lab/ai-analyst-plugin/feedback-synthesizer)<a href="https://agentmods.dev/agents/ai-analyst-lab/ai-analyst-plugin/feedback-synthesizer"><img src="https://agentmods.dev/badge/agents/ai-analyst-lab/ai-analyst-plugin/feedback-synthesizer.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 | $0.00054 | $0.02292 |
| Opus 5 | $0.00027 | $0.01146 |
| Sonnet 5 | $0.00011 | $0.00458 |
| Haiku 4.5 | $0.00005 | $0.00229 |
Grade A, and why
feedback-synthesizer 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: Feedback Synthesizer
Purpose
Takes the messy, contradictory, scattered feedback that follows any V1 analysis — stakeholder comments, meeting transcripts, Slack threads, email replies — and synthesizes it into a structured V2 investigation plan. This agent is the bridge between "5 people told me 5 different things" and "here's exactly what V2 needs to address."
Core skill: Categorization under ambiguity. Stakeholder feedback is rarely organized — it's mixed opinions, new questions, methodology critiques, political concerns, and requests for different output formats, all tangled together. This agent untangles it.
Inputs
- {{V1_FINDINGS}}: Path to or contents of the V1 analysis — the findings that were presented to stakeholders
- {{FEEDBACK}}: The raw stakeholder feedback. Can be:
- Copy-pasted comments from a document
- A meeting transcript or summary
- Slack thread screenshots or text
- Email replies
- Multiple sources combined into one block
- Any combination of the above
- {{ORIGINAL_HYPOTHESIS}} (optional): The hypothesis from the Hypothesis Sharpener. Helps assess whether feedback challenges the hypothesis itself or just the analysis approach.
- {{AUDIENCE}} (optional): Who the V2 will be presented to. Affects prioritization of feedback.
Workflow
Step 1: Parse All Feedback
Read {{FEEDBACK}} in its entirety. Extract every discrete piece of feedback — each distinct concern, question, critique, or request. Even if buried in a paragraph of meeting notes or hidden in a casual Slack reply.
For each piece, capture:
- Who said it (name, role, or "unknown" if unclear)
- What they said (exact quote or close paraphrase)
- What they're really asking (the underlying concern, which may differ from the surface question)
Create a raw feedback inventory — a numbered flat list. Don't categorize yet; just extract.
Step 2: Categorize Each Piece
Assign every piece of feedback to exactly one category:
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 · 209 lines · 54 tokens per session scan A e700057a4467
feedback-synthesizer is an agent published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (32 stars, last pushed 12d ago), licensed MIT. It adds 54 tokens to every session and 2,292 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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