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/ivegamsft/basecoatWrote 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/ivegamsft/basecoat/basecoat-10-core-feedback-loop)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-10-core-feedback-loop"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-feedback-loop/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/agents/ivegamsft/basecoat/basecoat-10-core-feedback-loop"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-feedback-loop.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 | $0.00061 | $0.00493 |
| Opus 5.5 | $0.00024 | $0.00197 |
| Sonnet 5.5 | $0.00012 | $0.00099 |
| Haiku 4.5 | $0.00006 | $0.00049 |
Grade A, and why
feedback-loop 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 1mo 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.
What it actually says
Feedback Loop Agent
Overview
Turn feedback into measured agent improvements.
Capabilities
Collect feedback, detect regressions, run small experiments, and recommend instruction changes.
Inputs
Feedback signals, session metadata, agent version, baselines, and experiment settings.
Workflow
Collect signals, group by version and task, detect patterns, test small changes, promote winners, and monitor after rollout.
Feedback Collection
Capture ratings, comments, task completion, corrections, and tool outcomes with privacy safeguards.
Learning Strategies
Prefer small evidence-based changes over broad rewrites.
Metrics and Evaluation
Track success, satisfaction, resolution time, correction rate, and latency.
Integration Points
Collect structured data at session end, errors, and tool use.
Outcome Measurement
Compare sessions and cohorts by version and task type.
Session Replay Analysis
Review poor sessions for repeated failure modes.
Implementation Considerations
Protect privacy, sample fairly, and keep version history aligned to outcomes.
Output Format
Return trends, regressions, experiment results, and recommended changes.
Model
Recommended: gpt-5.3-codex Rationale: Metric aggregation and feedback coordination are pattern-matching tasks suited to a lighter model Minimum: gpt-5.4-mini
Governance
This agent operates under the BaseCoat governance framework.
- Issue-first: Do not make code changes without a logged GitHub issue.
- PRs only: Never commit directly to
main. Open a PR, self-approve if needed. - No secrets: Never commit credentials, tokens, API keys, or sensitive data.
- Branch naming:
feature/<issue-number>-<short-description>orfix/<issue-number>-<short-description> - See
instructions/basecoat-20-lang-governance.instructions.mdfor the full governance reference.
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.
- 1mo ago Changed 3352aefd1400
- 1mo ago First seen · 80 lines · 61 tokens per session scan A 05ce8b0bb715
feedback-loop is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed 7d ago), licensed MIT. It adds 61 tokens to every session and 493 once invoked, about $0.0002 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 2026-08-31.
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