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 manishiitg/coding-agent-loop --skill feedback-theme-to-product-decisiongit clone --depth 1 https://github.com/manishiitg/coding-agent-loopWrote 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/manishiitg/coding-agent-loop/feedback-theme-to-product-decision)<a href="https://agentmods.dev/skills/manishiitg/coding-agent-loop/feedback-theme-to-product-decision"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/feedback-theme-to-product-decision/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/manishiitg/coding-agent-loop/feedback-theme-to-product-decision"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/feedback-theme-to-product-decision.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 | $0.00023 | $0.00653 |
| Opus 5.5 | $0.00009 | $0.00261 |
| Sonnet 5.5 | $0.00005 | $0.00131 |
| Haiku 4.5 | $0.00002 | $0.00065 |
Grade A, and why
feedback-theme-to-product-decision 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 20d 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feedback Theme to Product Decision
Outcome
Turn a bounded set of customer feedback into a product decision brief with exact source coverage, denominator, current issue state and next evidence. A theme is not an approved roadmap item or a customer promise.
When to use
Use when Support hears a recurring product problem and Product must investigate, link, defer or decline it. Feedback & Review Analyst owns source normalization; Product Feedback Coordinator owns the product-side review. Reuse compatible Crews, but keep source privacy and owner boundaries explicit.
Discovery and user direction
Inspect existing Crews, feedback sources and allowed quotations, account segments, current issue/roadmap source, product evidence, and decision owner. Ask for missing scope only. Show a concrete proposal before Builder configures it; selection creates no issue or message.
Required inputs
Bind tenant, product, feedback period, source channels, deduplication rule, theme ID, unique reporter denominator, privacy policy, product owner, current issue source and decision criteria. Record missing coverage and counterexamples.
Plan and AgentWorks tools
Create steps for Feedback brief, blocking validation, Product review and blocking validation. Feedback emits feedback-theme-brief/v1 with stable record IDs, counts, denominator and representative evidence. Product receives only the validated bounded artifact and authorized references, re-reads issue/roadmap and product records, then emits product-feedback-decision/v1. An issue creation, status change or customer update is a separate exact-object approved route with a provider receipt.
Knowledge and persistence
Save source-window and theme IDs, deduplicated feedback IDs, numerator and denominator, segment, evidence and privacy scope, current issue IDs, owner decisions, action keys and receipts. On repeats, compare like source coverage and preserve earlier decisions.
Validation and reporting
The validator checks scope joins, unique IDs, counts, denominator, source references, decision state and issue-action evidence. The fictional theme and decision pass; an invalid decision fails. The dashboard distinguishes unreviewed, investigate, linked, deferred, declined and separately actioned themes with source coverage and owner.
What ships with it
8 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.
- examples/feedback-theme-brief.json 1.1 KB
- examples/invalid-product-feedback-decision.json 1010 B
- examples/product-feedback-decision.json 1.2 KB
- playbook.json 5.1 KB
- references/team-and-handoffs.md 2.3 KB
- scripts/test_validate_handoff.py 2.5 KB runs code
- scripts/validate_handoff.py 5.5 KB runs code
- SETUP.json 3.0 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.
- 20d ago First seen · 48 lines · 23 tokens per session scan A 2bb73de66588
feedback-theme-to-product-decision is a skill published in the GitHub repository manishiitg/coding-agent-loop (13 stars, last pushed 19d ago), licensed MIT. It adds 23 tokens to every session and 653 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-27.
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