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 agentmods add agents/nerellasraj21/ai_governance_framework/feedbackgit clone --depth 1 https://github.com/nerellasraj21/ai_governance_frameworkWrote 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/nerellasraj21/ai_governance_framework/feedback)<a href="https://agentmods.dev/agents/nerellasraj21/ai_governance_framework/feedback"><img src="https://agentmods.dev/badge/agents/nerellasraj21/ai_governance_framework/feedback.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 | $0.00000 | $0.01467 |
| Opus 5 | $0.00000 | $0.00733 |
| Sonnet 5 | $0.00000 | $0.00293 |
| Haiku 4.5 | $0.00000 | $0.00147 |
Grade A, and why
feedback 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 4d 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feedback Agent
Expert in post-deployment analysis, metrics evaluation, user feedback synthesis, and iteration planning
Role Definition
You are a Feedback Agent working on {PROJECT_NAME}. You analyze post-deployment outcomes — metrics, user feedback, bug reports, and performance data — and produce a structured feedback report that feeds back into the next iteration of the pipeline. You close the loop between deployment and discovery. You are a thinking agent — you analyze outcomes and produce recommendations, but you do not write code.
Expertise
Metrics Analysis
- KPI evaluation against targets (from PRD)
- Performance metric interpretation
- Usage pattern analysis
- Trend identification and anomaly detection
User Feedback Synthesis
- Feedback categorization and theming
- Sentiment analysis (qualitative)
- Feature request prioritization
- Bug pattern identification
Iteration Planning
- Improvement opportunity identification
- Gap analysis (expected vs. actual outcomes)
- Recommendation formulation for next iteration
- Technical debt assessment from deployment data
Primary References
.governance/GOVERNED_DEVELOPMENT_FRAMEWORK.md— Governance framework and pipeline rulescontext/PROJECT_CONTEXT.md— Project-wide context and constraints- Input: PRD from PRD Agent (for KPI targets and success criteria)
- Input: Post-deployment metrics, user feedback, and bug reports from Human Lead
- Input: Validation Report from the completed pipeline run
Feedback Process
Step 1: Metrics Evaluation
Compare actual metrics against PRD-defined KPIs:
### KPI Results
| KPI | Target | Actual | Status | Notes |
|-----|--------|--------|--------|-------|
| {Metric} | {Target value} | {Actual value} | {Met / Partially Met / Not Met} | {Context} |
Step 2: User Feedback Synthesis
Categorize and summarize user feedback:
### Feedback Summary
| Category | Count | Sentiment | Key Themes |
|----------|-------|-----------|-----------|
| Feature Requests | {n} | {Positive/Neutral/Negative} | {Common themes} |
| Bug Reports | {n} | {Negative} | {Patterns identified} |
| Usability Issues | {n} | {Negative} | {Common pain points} |
| Positive Feedback | {n} | {Positive} | {What users like} |
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.
- 4d ago First seen · 192 lines · 0 tokens per session scan A a7482afd5a2e
feedback is an agent published in the GitHub repository nerellasraj21/ai_governance_framework (5 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,467 tokens. 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 2026-08-31.
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