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Agent skill

Personal Assistant Review Followup

by edonyzpc in edonyzpc/personal-assistant

Triage, confirm, fix, and validate code review findings in the personal-assistant repository after an agent team or human review.

AGPL-3.0Auto-check passedDevelopment

Install Personal Assistant Review Followup

skills CLI
$ npx skills add edonyzpc/personal-assistant --skill personal-assistant-review-followup -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install edonyzpc/personal-assistant personal-assistant-review-followup --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/edonyzpc/personal-assistant.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/personal-assistant-review-followup .claude/skills/personal-assistant-review-followup && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
personal-assistant-review-followup
GitHub stars
147
Token cost
~2k tokens
SKILL.md length
989 words
Files
2
Skills in repo
19
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Triage, confirm, fix, and validate code review findings in the personal-assistant repository after an agent team or human review.

  • Works in 9 steps: Verify each finding against its exact… → Classify each finding → Calibrate severity again against that… → …
  • The user asks whether review findings are real
  • SKILL.md covers Core Rule, Workflow, Decision Lens and Fix Discipline, plus 2 more sections
  • Calls npm and git

What it does

Personal Assistant Review Followup is an agent skill from edonyzpc/personal-assistant. Triage, confirm, fix, and validate code review findings in the personal-assistant repository after an agent team or human review. Use when the user asks whether review findings are real, whether they must be fixed, which findings are over-optimization, what architecture/product/program decisions are needed, or asks to implement and test the confirmed fixes after a review.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Development, covering Code review. The repository describes itself as: A plugin that harnesses AI agents and streamlining techniques to help you automatically manage Obsidian. The licence is AGPL-3.0.

When your agent uses it

  • The user asks whether review findings are real
  • Whether they must be fixed
  • Which findings are over-optimization
  • What architecture/product/program decisions are needed

Example prompts

  • “/personal-assistant-review-followup”

Workflow steps

9 steps, taken from the first numbered list in SKILL.md.

  1. Verify each finding against its exact trigger, affected code, and current
  2. Classify each finding
  3. Calibrate severity again against that evidence. A readability concern should
  4. Identify decision points before coding.
  5. If the user only asked for analysis or explicitly required read-only work,
  6. Ask for an unresolved product decision when viable fixes materially differ
  7. Implement the confirmed fix set only after the user explicitly asks to
  8. Add a regression test for the accepted runtime trigger when needed; do not
  9. Validate with focused checks, then app smoke only when the changed surface

What it can do on your machine

Read from SKILL.md and the folder at commit 5c22410. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npm
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm and git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Personal Assistant Review Followup loads about 2k tokens when it runs. Until then it costs about 102 tokens; SKILL.md has 989 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~102
When it runs · the whole SKILL.md, loaded when a task matches
~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from edonyzpc/personal-assistant at commit 5c22410, republished under its AGPL-3.0 licence (© edonyzpc). 989 words, ~1,957 tokens.

Download SKILL.mdSave it as .claude/skills/personal-assistant-review-followup/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
personal-assistant-review-followup
description
Triage, confirm, fix, and validate code review findings in the personal-assistant repository after an agent team or human review. Use when the user asks whether review findings are real, whether they must be fixed, which findings are over-optimization, what architecture/product/program decisions are needed, or asks to implement and test the confirmed fixes after a review.

Personal Assistant Review Follow-up

Core Rule

Use this skill after personal-assistant-review or any review that produced findings for the current personal-assistant repository.

The goal is not to fix every review comment. The goal is to separate real release risk from optional polish, get the needed decision, implement only the confirmed fix set, and verify the result without over-claiming.

Hard boundary: do not make product decisions while fixing review findings. If a finding can be fixed by removing, hiding, narrowing, or adding friction to a user-facing capability, ask the user before coding unless the user already made that exact product choice in this conversation and it still applies, or the fix restores a current accepted product contract. An agent-authored approval is not evidence of a user decision.

Workflow

  1. Verify each finding against its exact trigger, affected code, and current contract before restating it. Separate verified defects from hypotheses; reviewer confidence or severity is not evidence.
  2. Classify each finding:
    • must-fix: correctness, data safety, privacy, user-visible breakage, product-contract violation, release blocker, or a direct failure of the current patch's stated goal.
    • should-fix-now: small, local fix that prevents a likely regression or stabilizes the changed behavior, even if not release-blocking.
    • defer: polish, theoretical failure path, localized copy cleanup without product-contract or safety impact, refactor preference, or risk without a concrete trigger.
  3. Calibrate severity again against that evidence. A readability concern should identify a concrete change entry, responsibility, or invariant that is hard to follow. Style preferences or speculative risks alone do not justify a refactor, broader scope, or a full validation gate.
  4. Identify decision points before coding.
  5. If the user only asked for analysis or explicitly required read-only work, make no writes; stop after the classification and decision options.
  6. Ask for an unresolved product decision when viable fixes materially differ in behavior or user effort. Reuse applicable user decisions and current accepted contracts; do not infer a decision from reviewer severity.
  7. Implement the confirmed fix set only after the user explicitly asks to implement or fix it. If that authorization already exists in this conversation and still covers the fix set, proceed without asking again.
  8. Add a regression test for the accepted runtime trigger when needed; do not add tests that merely assert documentation wording.
  9. Validate with focused checks, then app smoke only when the changed surface needs deployed Obsidian evidence.

Product choices requiring a decision when not already resolved by applicable user authority or a current accepted contract:

  • Removing or hiding a visible control, command, workflow, or shortcut.
  • Increasing or decreasing confirmation burden for durable, provider-backed, cost-bearing, privacy-sensitive, or future-behavior-changing actions.
  • Choosing between safety/trust and the product goal of reducing user burden.
  • Changing product copy, information architecture, queue/batch behavior, or review cadence in a way that changes what users can do.
  • Reinterpreting a current product doc, roadmap, tracker, or user-stated product principle.

When an unresolved product decision appears, present the smallest viable options with a recommendation and tradeoff. Do not continue into code edits for that decision until the user chooses; continue independent authorized fixes.

Show full SKILL.md (478 more words)Show less

Decision Lens

Use three lenses when the user asks whether findings need decisions.

Architecture:

  • Define ownership and invariants, not just symptoms.
  • Prefer boundaries already used by the touched modules.
  • If a fix only masks one UI symptom but leaves concurrent semantics undefined, ask for or propose the invariant.

Program:

  • Decide whether the implementation should reject, serialize, dedupe, or reuse in-flight work.
  • Prefer a minimal helper or state flag when the invariant is local.
  • Avoid broad refactors unless the finding proves an existing abstraction is misleading or dangerous.

Product:

  • Decide what the user should see and what should not happen twice.
  • Protect provider-backed, cost-bearing, destructive, or persistent actions from accidental duplicate execution.
  • Preserve the user's product intent and local product docs even when a reviewer suggests a safer but higher-burden alternative.
  • For Memory, Review Queue, Pagelet, and other review surfaces, explicitly weigh the burden of extra confirmations against trust and source evidence.
  • Keep ordinary copy in the product language for the touched surface. Internal terms are acceptable in diagnostics, logs, and developer-only output.

Fix Discipline

  • Do not fix deferred findings unless the user explicitly adds them to the confirmed fix set.
  • Do not make opportunistic changes outside the confirmed fix set.
  • Preserve user edits and existing uncommitted changes.
  • Use apply_patch for manual edits.
  • Per AGENTS.md Testing Instructions, do not claim Obsidian validation without deployed evidence.

Validation

Start with the smallest checks that prove the accepted finding is fixed. For code/DOM changes, run the Local Validation Gate from AGENTS.md, scoped to the affected suites. For plugin command, worker, DOM/CSS, or shared runtime changes, also include npm run lint. For documentation-only fixes, use the relevant documentation checks and git diff --check; do not run runtime gates without a runtime change.

Use Validation Planning And Reuse, Test Failure Diagnosis, and Multi-Agent Validation Coordination in AGENTS.md for evidence reuse, diagnosis, and shared validation ownership. Required phase, device, CI, and release gates still apply; do not assign each subagent a duplicate full gate.

When a finding relies on a probe or checker, confirm the relevant comparison semantics and evidence boundary before treating its output as a product defect. Inspect text matching/normalization, asynchronous completion and cleanup, or repeat-run behavior only where the reported trigger makes them relevant. Reuse existing proof or add the smallest normal/failing examples needed to close a reliability gap before adopting the result; do not require an unrelated matrix. A checker that misclassifies valid behavior needs correction, not a product change to satisfy it or weakened assertions. Record an unresolved checker gap as unverified evidence, not a confirmed product finding or a PASS.

For Obsidian runtime smoke, use obsidian-test-vault-smoke and prefer a provider-free probe unless the accepted fix specifically requires live provider work. If a smoke probe temporarily monkey-patches the loaded test-vault plugin instance, patch only the smallest method needed, restore it in try/finally, and check fresh dev:errors before reporting success.

Output

markdown
**Fixed**
- <finding>: <what was done>

**Deferred**
- <finding>: <reason>

**Decisions**
- <architecture/product invariant chosen>

**Validation**
- PASS: `<check>` - <result>
- checks not run / residual risk

**Residual risk**
- <provider paths, real Obsidian UI paths not run, etc.>

© edonyzpc, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in .agents/skills/personal-assistant-review-followup of edonyzpc/personal-assistant.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit 5c22410

Compare with similar skills

Personal Assistant Review Followup next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Personal Assistant Review Followup compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Personal Assistant Review Followup this skilledonyzpc/personal-assistant147—~2kAutomated safety check: PassAGPL-3.0
PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
Code Review ChecklistshareAI-lab/learn-claude-code78k4 repos~1.1kAutomated safety check: PassMIT
Backend Code Reviewlangflow-ai/langflow155k—~3.5kAutomated safety check: NotesMIT
Mole Bug Patternstw93/Mole70k—~2kAutomated safety check: PassGPL-3.0
Backend Code Reviewlanggenius/dify158k—~676Automated safety check: PassCustom licence

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Categories

Questions about Personal Assistant Review Followup

What does Personal Assistant Review Followup do?

Triage, confirm, fix, and validate code review findings in the personal-assistant repository after an agent team or human review. Personal Assistant Review Followup is an agent skill from edonyzpc/personal-assistant. Triage, confirm, fix, and validate code review findings in the personal-assistant repository after an agent team or human review.

When should I use Personal Assistant Review Followup?

Personal Assistant Review Followup fits situations like: the user asks whether review findings are real; whether they must be fixed; which findings are over-optimization; what architecture/product/program decisions are needed.

How do I install Personal Assistant Review Followup in Claude Code?

Run `npx skills add edonyzpc/personal-assistant --skill personal-assistant-review-followup -a claude-code`. Or copy the skill folder (.agents/skills/personal-assistant-review-followup in edonyzpc/personal-assistant) into .claude/skills/personal-assistant-review-followup in your project. Claude Code loads it when a task matches its description.

How do I install Personal Assistant Review Followup in Codex?

Run `npx skills add edonyzpc/personal-assistant --skill personal-assistant-review-followup -a codex`. Or copy the skill folder (.agents/skills/personal-assistant-review-followup in edonyzpc/personal-assistant) into .agents/skills/personal-assistant-review-followup in your project. Codex loads it when a task matches its description.

Can I use Personal Assistant Review Followup in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add edonyzpc/personal-assistant --skill personal-assistant-review-followup -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/personal-assistant-review-followup, .gemini/skills/personal-assistant-review-followup, .github/skills/personal-assistant-review-followup and .opencode/skills/personal-assistant-review-followup in your project.

What does Personal Assistant Review Followup need to run?

Going by SKILL.md and its folder, Personal Assistant Review Followup needs the command-line tools its instructions call (npm and git).

Does Personal Assistant Review Followup access the network?

SKILL.md contains no URLs. Its commands use npm and git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Personal Assistant Review Followup safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Personal Assistant Review Followup use?

Personal Assistant Review Followup is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Personal Assistant Review Followup use?

About 2k tokens (SKILL.md is roughly 7.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Personal Assistant Review Followup?

Skills that share tags, products or a category with Personal Assistant Review Followup: PR Babysitter (openinterpreter/openinterpreter, 69k stars), Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), Backend Code Review (langflow-ai/langflow, 155k stars) and Mole Bug Patterns (tw93/Mole, 70k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Personal Assistant Review Followup?

edonyzpc (a GitHub user) maintains it in edonyzpc/personal-assistant, which has 147 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on October 10, 2026.

Source: edonyzpc/personal-assistant on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.