Building an Autonomous Feedback Agent 🔗 **Source:** [Building an Autonomous Feedback Agent](https://joost.blog/autonomous-feedback-agent/) > [!note]+ Document Notes > tags: [autonomous-agents, developer-tools, wordpress] > author: Joost de Valk > source: joost.blog > reference: [https://joost.blog/autonomous-feedback-agent/] > date created: 2026-02-14 00:00 > > # 🏷️ Title: Building an Autonomous Feedback Agent > <!-- deep-read:sections start --> ## 🧭 Table of Contents - [📝 Summary](#-summary) - [📖 Main Content](#-main-content) - [🗒️ Notes](#️-notes) - [📚 References](#-references) - [🗣️ Paraphrase](#️-paraphrase) - [📰 Newsletter](#-newsletter) - [✅ Action Items](#-action-items) - [🧪 Hypotheses](#-hypotheses) - [📊 Methodology](#-methodology) - [❓ Questions](#-questions) - [🔮 Future Research](#-future-research) - [💡 Implications](#-implications) - [🧠 Learning Styles Assessment](#-learning-styles-assessment) - [📊 Accessibility Assessment](#-accessibility-assessment) - [🌍 DEIB Assessment](#-deib-assessment) - [✅ Logical Validity](#-logical-validity) - [🛡️ DARVO Analysis](#-darvo-analysis) - [🎯 Conclusion](#-conclusion) ### 🔗 References - Include links cited in or relevant to the article. --- ## 📝 Summary - What happened. - Joost built a fully autonomous feedback-to-code pipeline for Rondo Club (WordPress + React), where user feedback can result in PRs and deployments with minimal human involvement. - Why it matters. - It shows a concrete, working pattern for safe(ish) AI-driven code changes, shrinking the loop from user report to production fix. - Key stakeholders. - Solo/indie devs, small product teams, WordPress builders, AI tooling vendors, club members using Rondo Club. - Risks and tradeoffs. - Over-engineered changes, hidden failures in long-running agents, git-state corruption, prompt-injection and malicious input, over-reliance on automated review. - Bottom line. - With a strict human approval gate, tight prompts, monitoring, and automated review/merge rules, AI agents can reliably handle small code changes and optimizations in production applications. --- ## 📖 Main Content - Core arguments (plain language) - An end-to-end feedback agent is feasible today using off‑the‑shelf tools (Claude Code, GitHub, Copilot, bash, WordPress). - The key is **workflow design**, not model magic: structured input, explicit prompts, strict status handling, and robust safety rails. - A single human approval step and automated review can keep risk manageable while still removing most day-to-day toil. - The same pattern generalizes to other apps: any stack that can expose structured feedback and be driven by git + CI/CD can use this. - How the system works (high level) - Rondo Club is a WordPress app with a React frontend for managing a sports club. The author, as solo dev, wanted to remove himself as bottleneck. - The pipeline has staged flow: - Users submit feedback through an in-app form. - An admin approves items to enter the queue. - A Mac Mini‑hosted bash script regularly polls for work, calls Claude Code, and orchestrates git + GitHub. - PRs get automatic Copilot review, then are merged and deployed when deemed safe. - When idle, the agent fixes server errors and later optimizes code in small PRs. - Everything is built around **statuses** on feedback items and a clear priority order: finish in-review PRs, then new feedback, then optimizations. - Important definitions & context - **Claude Code**: Anthropic’s code-focused agent that can read a repo, run commands, and make changes when called with proper flags (here `--dangerously-skip-permissions`). - **Agent prompt**: A repo-level instruction file telling Claude exactly what branch names to use, what checks to run, how to format status, and what *not* to do. - **IN_REVIEW / NEEDS_INFO / DECLINED**: Standardized, machine-readable outputs parsed by the script to decide next actions. - **Optimization mode**: When there’s no feedback or errors, the agent systematically reviews source files for limited, high-confidence improvements, bounded by rate limits and git-hash tracking. - Uncertainties or contested claims - Long-term reliability of unsupervised optimization PRs across diverse stacks is still unclear; this is one app with a known codebase and owner. - The article assumes Copilot + Claude are sufficiently complementary reviewers; risk tolerance and error surface will differ for other teams. - Security model focuses mainly on prompt/feedback approval and script-level safety; threat models for more adversarial environments would need additional controls (e.g., sandboxing, permissioned environments, stricter CI). --- ### 🗒️ Notes - Rondo Club context - WordPress-based app with React frontend, used by a local football club to manage members, teams, and committees. - Main pain: as a solo developer, the author became a bottleneck between “user reports a bug” and “fix in production.” - Goal: user submits feedback, admin approves, AI agent takes over to create, review, and merge PRs, deploying autonomously for straightforward changes. - System stages (overview) - Six core stages initially described: 1. Submission – Feedback form inside the app. 2. Approval – Human admin gate. 3. Processing – Scheduled script talks to Claude Code. 4. Review – Automated PR review. 5. Resolution – Merge + deploy. 6. Optimization – When idle, agent improves code. - Later detail effectively stretches this to seven by splitting error-fixing and optimization. - Stage 1: Feedback submission - All logged-in users see a feedback button that opens a modal. - For bugs, the form captures: - Title, description, steps to reproduce. - Expected vs actual behavior. - Optional screenshot uploads, optional system info (browser, app version, current URL). - For features: - Title, description, use case. - Users specify which project the feedback concerns (supports multiple repos). - Backend: - Feedback stored as a WordPress custom post type with metadata (status, type, priority, project). - REST API for CRUD + filtering by status/type. - New items start as `new`. - Stage 2: Approval (human gate) - Admin reviews each feedback item and explicitly sets to `approved` to allow the agent to pick it up. - This prevents vague, duplicate, or malicious input from driving code changes directly. - Without this gate, any logged-in user could cause an AI to write and merge code into the repo. - Stage 3: Feedback agent infrastructure - Implemented as a bash script on a Mac Mini, run via `launchd` every 5 minutes. - Loop mode: fetch oldest approved feedback, process, repeat until queue empty. - Work-fetch priority: 1. Handle existing in-review PRs (complete what’s started). 2. Then process oldest approved feedback. 3. Only when no work remains, enter optimization tasks. - Running Claude Code - On picking up a feedback item, the script: - Marks item `in_progress`. - Builds a structured prompt: feedback description, steps, conversation history, and system instructions. - Calls Claude Code with `--print --dangerously-skip-permissions`. - Sets a 10‑minute timeout with log output every 2 minutes for visibility. - Agent prompt (`.claude/agent-prompt.md`) instructs Claude to: - Create branch `feedback/{id}-{slug}`. - Analyze the issue and implement changes. - Run `npm run build` to check frontend compilation. - Commit, push, and open PR including feedback ID in title. - Emit a structured status: `IN_REVIEW`, `NEEDS_INFO`, or `DECLINED`. - Status handling and conversation loop - Script parses Claude’s output for status: - `IN_REVIEW`: PR created; script stores PR URL in feedback metadata and asks GitHub Copilot for review. - `NEEDS_INFO`: Claude asks questions; script posts them as comments on the feedback thread in the app so users can respond. - `DECLINED`: Feedback deemed non-actionable. - Failures/timeouts reset the item back to `approved` so it can be retried. - Conversation pattern: 1. User submits bug report. 2. Admin approves. 3. Agent returns `NEEDS_INFO`; question posted as comment. 4. User answers. 5. Admin re-approves (to avoid abuse/injection). 6. Agent processes again, now able to resolve and PR. - Safety features in the script - Lock file to prevent concurrent runs (since `launchd` may overlap). - Signal handlers for `SIGTERM` / `SIGINT`: - Reset feedback status. - Checkout `main` to leave repo clean. - Timeout enforcement: - Kill hung Claude processes after 10 minutes. - Crash recovery: - On unexpected exit, reset feedback status so items aren’t stuck. - Git cleanup: - Return to `main` and remove merged branches after each item. - Stage 4: Automated code review - After Claude opens a PR: - Script requests GitHub Copilot review. - Next run inspects Copilot feedback: - If no inline comments: treat as safe; move to merge. - If comments present: script checks out PR branch and formats comments into another prompt. - Review-fix prompt (`.claude/review-fix-prompt.md`) directs Claude to: - Fix *real* bugs, consistency, and safety problems. - Ignore style-only nitpicks, test requests, subjective suggestions. - Output if PR is `SAFE_TO_MERGE` after fixes. - If Claude says safe and merge conflicts occur (e.g., due to other merged PRs), Claude is tasked with resolving those too. - Otherwise, PR is escalated for human review. - Stage 5: Merge and deploy - Once PR is considered safe: 1. `gh pr merge --squash --delete-branch`. 2. Pull `main` with merged changes. 3. Run deployment script (rsync WordPress theme to production, clear caches). 4. Update feedback item status to `resolved`. - Feedback ID is parsed from branch name (`feedback/1234-slug` → `1234`) to ensure correct linking. - Stage 6: Fixing server errors - When feedback queue and PRs are empty, the agent looks at server PHP error logs. - If fixable errors are found, it runs the same flow (branch, Claude Code, PR, review, merge). - Stage 7: Code optimization - In full idle mode (no feedback, no PRs, no server errors), the agent: - Iterates through source files across all projects. - Looks for small, high-confidence improvements: - Dead code removal, DRY fixes, reduced complexity, performance improvements. - If it finds something worthwhile, it creates `optimize/` PRs. - To avoid reprocessing unchanged code: - A tracker stores last git commit hash per file. - If stored hash matches current, file is skipped. - Daily limits: - Max 25 optimization runs and 10 PRs per day, controlling cost. - What works well (author’s observations) - Small, targeted changes (bug fixes, UI tweaks) are handled reliably if feedback is structured with clear steps and expected/actual behavior. - Prioritizing completion of in-review PRs prevents an accumulation of half-finished work. - Timeouts plus logging are critical to detect and debug stuck processes. - The human approval gate is both a safety and quality filter. - What to watch out for - Claude can over-engineer if prompt doesn’t insist on minimal, focused diffs. - Process monitoring needs visibility: without structured logs and progress updates, debugging remote agents is painful. - Git state can easily drift into unexpected states; scripts must be defensive around dirty working trees, checkouts, and stale branches. - Portability: running it yourself - General pattern for any project: 1. Build feedback intake (form, Slack bot, issue template) that captures structured signals. 2. Write a detailed agent prompt with rules for branching, commits, PR titles, and scope limits. 3. Wrap Claude Code in a robust monitoring script handling timeouts, cleanup, and status parsing. 4. Add a second layer of automated review (Copilot, another Claude pass, linters). 5. Automate merge/deploy for low-risk changes while keeping a human path for complex work. - Source is in the Rondo Club repo (`bin/get-feedback.sh` and `.claude/` prompts). --- ## 📚 References - Rondo Club autonomous agent article: https://joost.blog/autonomous-feedback-agent/ - Claude Code (Anthropic product page): https://www.anthropic.com - GitHub Copilot: https://github.com/features/copilot - WordPress (for CPTs and REST API): https://wordpress.org/ --- ## 🗣️ Paraphrase - Paragraph A paraphrase. - The author describes building a system where user-submitted feedback inside a sports club app automatically results in AI-authored pull requests, reviewed and merged with minimal human intervention. - Paragraph B paraphrase. - By combining a structured intake form, a scheduled bash script, Claude Code as the coding agent, GitHub Copilot for review, and careful safeguards, the workflow safely automates small fixes, log-driven repairs, and ongoing code cleanups. --- ## 📰 Newsletter Rondo Club just got a serious upgrade: Joost de Valk wired up an autonomous feedback agent that turns structured bug reports into production-ready PRs—no human typing required. It’s all WordPress under the hood (custom post types + REST API) with a React front end, Claude Code for implementation, and Copilot for review. If you maintain custom WordPress apps or themes, this architecture is worth a close look: a single approval click sends tickets into a loop that branches, builds, runs checks, opens PRs, and even deploys when reviews are clean. When there’s no feedback, the agent switches to reading PHP error logs and later to safe “optimize/” refactors. This is a practical blueprint for “AI as junior dev” inside WordPress: - Use CPTs to store feedback and status. - Let a headless script coordinate Claude and GitHub. - Keep a strict human gate and narrow prompts to avoid over-engineering. Curious? Start by mapping your own feedback → code → deploy loop and asking: which steps could a tightly-scoped agent handle? --- ## ✅ Action Items #### DevRel - Design a demo repo mirroring the Rondo Club pattern (feedback intake, agent script, prompts, PR review). - Create a hands-on workshop: “From WordPress feedback form to autonomous PRs.” - Publish reference docs and quickstarts for integrating Claude Code with common Git workflows. - Track adoption via sample repo forks, workshop attendance, and follow-up surveys. #### OSPO - Draft a policy for AI agents committing to org repositories (scope, permissions, audit requirements). - Require all agent-driven repos to use protected branches, status checks, and human override rules. - Establish a review cadence for prompts as governance artifacts (who owns and updates them). - Integrate AI-generated code labelling into SBOM and inventory processes. #### Community - Host live sessions where maintainers walk through their first agent-driven PRs. - Provide starter issue templates for structured feedback (steps to repro, expected/actual). - Encourage “first AI issue” pairing: contributors watch and then review an agent PR together. - Share postmortems of agent failures to build shared safety practices. #### WordPress - Core: Explore a generic “feedback-to-issue” pattern that plugins/themes can adopt. - Core Performance: Experiment with an optimization agent that only suggests patches as PRs (no auto-merge). - Core AI: Define guidelines for agent prompts interacting with WordPress codebases and APIs. - Core Program Team: Document best practices for using agents in .org-hosted projects. - Mobile: Consider agent-assisted triage for crash logs and ANR reports. - Playground: Offer a sandbox where people can safely test an autonomous agent loop against a disposable WordPress instance. - Community: Add AI-agent talks and workshops to WordCamps and meetups. - Training: Build courses showing how to structure feedback and prompts for safe automation. - Marketing: Highlight successful WordPress projects using agent patterns. - Support: Update support workflows and SLAs when agents handle low-risk tickets. - Design: Provide UX patterns for feedback widgets and clarification loops. - Documentation: Document CPT + REST patterns for structured feedback and status. - Photos / TV / Polyglots / Accessibility / Meta / CLI / Hosting / Tide / Openverse / Themes / Plugins: - Identify where structured feedback plus small, automated PRs could reduce toil (e.g., translation QA, CLI command docs, plugin review metadata, Openverse ingestion pipelines). #### The WPOCC - Fund small pilots where maintainers adopt the agent pattern in their plugins/themes. - Sponsor documentation sprints to capture practical guardrails for AI in WordPress. - Offer microgrants for building WordPress-native tooling around feedback intake and agent orchestration. ### FAIR - Promote local, repository-level metadata about agent usage and prompts (for provenance). - Encourage transparent funding disclosure for AI tooling integrated into community projects. - Provide reusable governance templates (agent charters, risk scorecards) for shared infrastructure. --- ## 🧪 Hypotheses - H1: Structured, in-app feedback (with steps to reproduce and expected/actual behavior) significantly increases the success rate of autonomous bug-fix PRs. - H2: A single human approval gate before the queue meaningfully reduces prompt injection and malicious-code risk compared to fully open submission. - H3: Automated second-pass review (e.g., Copilot) catches a substantial portion of defects introduced by AI coding agents. - H4: Idle-time optimization agents improve code quality without materially increasing defect rates when constrained to small, focused changes and rate limits. - H5: Git-state safety mechanisms (lock files, cleanup, crash recovery) are necessary and sufficient to keep long-running agents stable over time. --- ### 📊 Methodology - For H1: - A/B test structured vs unstructured feedback on similar codebases; measure proportion of successful autonomous PRs and time to resolution. - For H2: - Compare incidents of unsafe or irrelevant PRs across deployments with and without admin-approval gates. - For H3: - Manually audit a sample of AI-created PRs with and without Copilot review; count escaped bugs and code smells. - For H4: - Track runtime errors, performance metrics, and defect reports before/after enabling optimization mode; correlate with number and type of optimize PRs. - For H5: - Run the agent across multiple repositories for weeks, logging all git failures, lock conflicts, and crash recoveries; analyze failure modes. --- ### ❓ Questions - How does this pattern scale from a solo-maintained app to large, multi-team monorepos? - What additional security layers are needed if untrusted or anonymous users can submit feedback? - How often must prompts be updated as codebases grow and models change? - What is the acceptable error rate for autonomous merges in production for different risk profiles? - Can similar agents safely handle schema migrations or more complex refactors? --- ### 🔮 Future Research - Formal threat modelling for AI agents with write access to production code. - Comparative studies of different automated reviewers (Copilot vs other LLMs vs static analyzers). - Frameworks for “least privilege” CI environments where agents can only touch defined paths. - User experience research on how non-technical users interact with clarification questions from agents. - Tooling to visualize and manage fleets of such agents across many projects. --- ### 💡 Implications - Practice: Teams can reframe AI from “chatbot helper” to “structured pipeline worker” with well-defined responsibilities. - Policy: Organizations will need explicit policies for AI-authored code, including logging, approvals, and rollback plans. - Security: Agent workflows must be designed as security-sensitive systems, not mere developer tools. - Education: Training should include how to design prompts, feedback forms, and safety mechanisms—alongside traditional coding. - Funding: Supporting shared infrastructure for feedback agents could be a high-leverage investment for ecosystems like WordPress. --- ### 🧠 Learning Styles Assessment - Reading level estimate - Likely around Grade 10–12 (Flesch-Kincaid), given technical terminology (launchd, rsync, REST API, git hashes) and long compound sentences. #### 👀 Visual learners - Present: - Implicit architecture (stages, flows) but mostly described in text. - Improve: - System diagrams showing feedback → agent → PR → review → deploy. - Sequence diagrams for conversation loops and error-handling. - Before/after code diff examples of “small, focused” PRs. #### 🔊 Aural learners - Present: - No explicit audio/podcast elements. - Improve: - Short audio summary of the architecture. - Recorded walkthrough of the bash script and prompts. #### 🗣️ Verbal learners - Present: - Clear headings (stages), stepwise breakdowns, defined statuses. - Improve: - Glossary of key terms (agent prompt, IN_REVIEW, NEEDS_INFO). - More analogies (agent as “junior dev” supervised by a “code reviewer”). #### 🧩 Kinesthetic learners - Present: - Encouragement to “run it yourself,” pointing to the real repo. - Improve: - Step-by-step tutorial with commands and checkpoints. - Small practice project to wire up a minimal feedback agent. #### 🧠 Logical learners - Present: - Structured pipeline, explicit priorities, condition-based behavior. - Improve: - Tables for status transitions and failure modes. - Formalized flowcharts and decision trees for merge criteria. #### 🧑‍🤝‍🧑 Social learners - Present: - Implied collaboration (admin approval, user-agent conversation). - Improve: - Suggested team rituals for reviewing agent-generated PRs. - Discussion prompts for engineering teams evaluating this pattern. #### 🧘‍♀️ Solitary learners - Present: - Enough detail to experiment alone by reading and exploring the repo. - Improve: - Reflection questions about one’s own feedback/PR pipeline. - Self-checklists for readiness to deploy an agent. #### 🌿 Naturalistic learners - Present: - No explicit environmental framing. - Improve: - Discussion of resource usage and cost efficiency (compute, API calls). - Analogies to ecosystems (agent as “decomposer” cleaning dead code). --- ### 📊 Accessibility Assessment *(Assuming a typical, modern WordPress blog theme.)* #### Screen reader semantics - Present: - Likely standard HTML headings, article landmarks. - Improve: - Ensure proper `<main>` landmark and descriptive link text for repo references. - Provide alt text for any code or diagram images if added later. #### Keyboard navigation - Present: - Standard WordPress themes usually offer tab navigation and visible focus. - Improve: - Confirm skip links and logical tab order; avoid keyboard traps in code or nav components. #### Contrast and readability - Present: - Most blog themes meet basic contrast, but it depends on styling. - Improve: - Check 4.5:1 contrast for body text; ensure comfortable line height and paragraph spacing for long technical sections. #### Text resizing and reflow - Present: - Responsive layout likely supports zoom to 200%. - Improve: - Ensure no horizontal scroll for code or wide content at high zoom; honor `prefers-reduced-motion`. #### Media and captions - Present: - Article is text-based; no captions needed as-is. - Improve: - For any future video demos, provide captions and transcripts; avoid autoplay. #### Forms and interactions - Present: - The content itself is static; no forms here. - Improve: - For any embedded demos of the feedback form, ensure labels, instructions, and error messages are screen-reader friendly. #### Standards reference - Map gaps to WCAG 2.2 AA: - 1.4.3 Contrast (Minimum), 1.4.10 Reflow, 2.1.1 Keyboard, 2.4.1 Bypass Blocks, 2.4.7 Focus Visible, 3.3.2 Labels or Instructions. --- ### 🌍 DEIB Assessment #### Cultural breadth - Present: - Example is a local football club—region-neutral but not particularly global. - Improve: - Include examples from different regions and organization types (nonprofits, schools, civic tech). #### Inclusive language - Present: - Neutral, technical language; no obvious exclusionary terms. - Improve: - Add brief plain-language summaries for non-developers curious about the system. #### Neurodiversity support - Present: - Logical headings, bullets, and stepwise sequences. - Improve: - More frequent subheadings for long sections. - Consistent patterns for enumerating stages and statuses. #### Representation - Present: - Focuses on tools and workflows, not people imagery. - Improve: - When adding visuals, ensure diverse representation in screenshots or illustrations. #### Psychological safety - Present: - Respectful tone; no shaming; candid about risks and gotchas. - Improve: - Explicitly encourage cautious experimentation and rollback strategies to reduce anxiety about automation. --- ### ✅ Logical Validity #### Strong reasoning - Clear causal chain: - Structured feedback → better context → more reliable AI fixes. Supported by experience with small, focused changes performing well. - Prioritization logic: - Finishing in-review PRs before starting new work reduces partial work-in-progress; matches standard WIP-management best practices. - Safety rationale: - Admin approval + automated review + defensive scripting collectively reduce risk of bad code landing in production. #### Fallacies - No major fallacies apparent; arguments are mostly experiential and conditional (“works well for small changes,” “pattern is transferable if…”). - Potential overgeneralization risk: - Implicit assumption that this pattern will translate smoothly to any project; mitigated somewhat by caveats. #### Tone of voice - Pragmatic and engineering-focused: - Shares specific implementation details and war stories (timeouts, stuck processes). - Balanced optimism: - Enthusiastic about possibilities but explicit about failure cases and safeguards. --- ### 🛡️ DARVO Analysis - Deny: - No denial or minimization of risk; the article openly discusses over-engineering, stuck processes, and git issues. - Attack: - No attacks on critics or users; tone is constructive. - Reverse victim/offender: - None: the piece does not frame the author or tools as “victims” of reasonable concerns; instead it addresses issues directly. --- ## 🎯 Conclusion The article presents a concrete, working blueprint for turning structured user feedback into autonomous, AI-authored PRs that are reviewed, merged, and deployed with minimal human effort. The success of this approach hinges on disciplined workflow design: a human approval gate, narrowly scoped prompts, robust monitoring and git hygiene, and automated secondary review. For readers, practical next steps include mapping their own feedback-to-deploy pipeline, identifying low-risk areas for automation (small bug fixes, log-driven repairs), and incrementally introducing an agent with strict boundaries and clear escape hatches. --- ## 🧱 Refactor Appendix (append after 🎯 Conclusion) ### 📚 Literature Split Pad - Purpose: Stage Konik CEE units that become per-source Literature notes. - Split level: **H3**. - Include verbatim quotes under 25 words and a [view highlight] link. ### [H1] 5–10-word present-tense claim > [!quote] [Building an Autonomous Feedback Agent](https://joost.blog/autonomous-feedback-agent/) by [[Joost de Valk]] via [[joost.blog]]. Published 2026-02-14. > “It’s a fully autonomous feedback processing pipeline (in my case powered by Claude Code).” Two lines: context and use. Cite data if relevant. [view highlight](https://joost.blog/autonomous-feedback-agent/#H1) #xref #articleseed ID:rw_[MISSING] ### [H2] 5–10-word present-tense claim > [!quote] [Building an Autonomous Feedback Agent](https://joost.blog/autonomous-feedback-agent/) > “The goal: users submit feedback in the app, I approve it with a click.” Two lines: context and use. [view highlight](https://joost.blog/autonomous-feedback-agent/#H2) #xref ID:rw_[MISSING] --- ### 🔖 Atomic Split Pad (Zettel Candidates) - Purpose: Promote reusable claims into Atomic notes. - Split level: **H4**. - Each item: one idea, present tense, specific, testable. #### [Z1] Autonomous agents handle small, structured bug fixes - Works when feedback is structured and changes are small and localized. - Pointer to article: “What works well” section. - Links: [[AI-Agents]] [[Developer-Workflows]] #### [Z2] Human approval gates reduce AI-agent risk - Only approved feedback enters the queue, blocking malicious or vague input. - Pointer to article: “Stage 2: Approval”. - Links: [[AI-Safety]] [[Code-Review-Processes]] --- ### ✏️ Fleeting Queue (Extract by Selection) - Purpose: Capture quick actions or sparks for Fleeting notes. - Extraction: **Select one line** → “Refactor: Extract selection to new note (content only).” - Keep each line short and self-contained. - [F1] Prototype a minimal feedback → Claude Code → PR loop for a small WordPress plugin — https://joost.blog/autonomous-feedback-agent/ #fleeting #inbox - [F2] Design a generic agent-prompt template for safe, minimal code changes — https://joost.blog/autonomous-feedback-agent/ #fleeting #inbox - [F3] Map my current bug-report → deploy pipeline and mark steps an agent could own — https://joost.blog/autonomous-feedback-agent/ #fleeting #inbox --- <!-- deep-read:sections end -->