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Review Misses

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Dark-Alex-17
review-misses

The review-miss ledger: escaped defects (bugs that survived review and were caught later) recorded as reusable detection patterns. Review orchestrators load this skill, read the ledger, and route matching entries to reviewers as first-class checks — this is the feedback loop that makes the review stack converge instead of plateau. Triggers: loaded by review orchestrators (e.g. code-reviewer) at review start; "record a miss", "review miss"

Overview

PublisherDark-Alex-17
Repositorycoyote
Skill namereview-misses
Stars
56
Forks
2
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Dark-Alex-17 on GitHub. Read the source before you install it.

Installation

Install the Review Misses AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Dark-Alex-17/coyote/tree/main/assets/skills/review-misses
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Dark-Alex-17/coyote.git /tmp/coyote
mkdir -p .claude/skills
cp -r /tmp/coyote/assets/skills/review-misses .claude/skills/review-misses
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Misses in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Review Misses on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Review Misses is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

review-misses

A review stack without a feedback loop plateaus: it catches the same classes forever and misses the same classes forever. This skill closes the loop — every defect that ESCAPED review (shipped, then caught in prod, by the user, or by a later change) becomes a ledger entry with a detection pattern, and every future review runs those patterns as first-class checks.

The ledger

Location: $HOME/.config/coyote/share/review-misses.md (expand $HOME at runtime — never hardcode a user's home path). The share/ subdirectory is the convention for durable DATA files that are not configuration — it keeps the config root uncluttered (the /usr/share analogy). The record-miss macro migrates a ledger from the legacy config-root location automatically; when reading, if share/review-misses.md is absent, also check the legacy $HOME/.config/coyote/review-misses.md before concluding "empty".

Scope: global and permanent. One ledger per environment, shared across every repo, review, and reviewer — it is the institutional memory of escaped defects, not per-review state. Reviews only READ it; nothing about a review is ever written to it. The repo: and surface: fields are how one global ledger stays relevant per review (filtering, not partitioning). Concurrent reviews are fine: reviewers are concurrent readers, and writes happen only through the record-miss macro — a human act, append-only. It deliberately lives under config rather than a cache directory: caches are disposable by contract, and losing this file loses the learning loop.

  • Missing or empty ledger: skip silently — a one-line note in the synthesis ("review-miss ledger: empty") and nothing else. Never invent entries.
  • Entries are append-only history. Never rewrite or delete entries during a review.

Entry format:

markdown
## MISS-NNN — <one-line title>
- date: YYYY-MM-DD
- repo: <owner/repo where it escaped>
- surface: <rest-api | worker | db-migration | cli | iac | library | ci-cd | any>
- what escaped: <the defect, concretely>
- why review missed it: <root cause in review terms — wrong context, unchecked caller, mock-assert test, …>
- detection pattern: <the concrete check/grep/question a reviewer runs to catch this CLASS of defect>

Consuming the ledger (review orchestrators)

  1. At review start, read the ledger (if present).
  2. Filter entries to the ones RELEVANT to this review: matching surface (against the resolved surfaces), matching repo, or surface: any. Irrelevant entries stay out of worker context — the ledger must not bloat every review.
  3. Paste each relevant entry's title + detection pattern into the CONTEXT section of the file-reviewers (or lanes) whose files it could apply to.
  4. In the final report footer, note review-miss patterns applied: MISS-004, MISS-011 (or none relevant).

Applying a pattern (reviewers)

  • A ledger detection pattern is a FIRST-CLASS check — same standing as a loaded skill's checklist item. Run it against the diff like any other check.
  • When a pattern fires, the finding cites the entry: recurrence of MISS-007 in the finding title. Recurrences of an escaped defect class default to 🔴 — the class has already burned us once.
  • When a pattern doesn't fire, silence is fine — no per-entry accounting needed.

Recording a miss

Record via the record-miss macro (coyote --macro record-miss "<what escaped>"), which appends a numbered skeleton entry and prompts for the remaining fields. Or append by hand in the entry format above.

Collection is otherwise AUTOMATIC — no mining pass needed:

  • whetstone classifies every externally-raised finding after a run addresses review feedback, and proposes ledger entries (approval-gated) or drops one-offs into the candidates inbox (<config>/share/review-miss-candidates.jsonl) as recurrence evidence.
  • PR-review reconciliation (where a re-review compares what humans said against what the panel found) records each human-caught, panel-missed defect into the same candidates inbox. The inbox is raw material, never authority: nothing enters the LEDGER without your approval — that curation is the noise firewall that keeps the ledger trustworthy.

Rules for good entries:

  • The detection pattern is the payload. "Be more careful with X" is not a pattern; "grep for time.Now() in files that also touch period/interval math; flag any not going through the clock interface" is. If you can't state the check a reviewer runs, keep distilling before recording.
  • One entry per defect CLASS, not per incident — if a new miss matches an existing entry's class, sharpen that entry's detection pattern instead of adding a duplicate.
  • Never record secrets, customer data, or incident details beyond what the pattern needs.

Frequently asked questions

What does the Review Misses AI skill do?

The review-miss ledger: escaped defects (bugs that survived review and were caught later) recorded as reusable detection patterns. Review orchestrators load this skill, read the ledger, and route matching entries to reviewers as first-class checks — this is the feedback loop that makes the review stack converge instead of plateau. Triggers: loaded by review orchestrators (e.g. code-reviewer) at review start; "record a miss", "review miss"

Why use Review Misses on TypingMind?

Because you install it once and use it with any model. Review Misses is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Review Misses in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dark-Alex-17/coyote/tree/main/assets/skills/review-misses. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Review Misses?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Review Misses?

As many as you like. As long as a model supports skills, you can use Review Misses with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Review Misses AI skill free?

Yes. It is published on GitHub by Dark-Alex-17 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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