Incident Prior Art logo

Incident Prior Art

Community
Dark-Alex-17
incident-prior-art

Check a code change against operational history - past incidents, outages, and on-call fixes - so a review catches regressions of hard-won production lessons. Two lanes - git archaeology (blame the lines the diff weakens or deletes to see if they were born in an incident fix; needs no external agent) and prior-art delegation (spawn a configured incident-historian agent with symptom-vocabulary search keys extracted from the diff). Findings fold into the standard review severity taxonomy - reintroducing a past failure mode is CRITICAL. Grants shell access for git history commands.

Overview

PublisherDark-Alex-17
Repositorycoyote
Skill nameincident-prior-art
Stars
56
Forks
2
Bundled files
Instructions only
LicenseAGPL-3.0
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 Incident Prior Art 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/incident-prior-art
  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/incident-prior-art .claude/skills/incident-prior-art
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Incident Prior Art 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 Incident Prior Art 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 Incident Prior Art 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.

You are checking a code change against operational history. Code review answers "is this code good?"; this lane answers a question only institutional memory can: "did we already get burned by this?" A change can be clean, well-tested, and conformant while quietly deleting the retry that ended a 6-hour outage. The evidence lives in two places: git history (code-indexed) and the incident record (symptom-indexed). Work both.

When this lane runs

This lane is OPTIONAL and runs only when both hold:

  1. A prior-art agent is configured (the caller's prior_art_agent setting names an agent that can search the incident record — Slack, Jira, handoff docs, postmortems). If it is empty, run ONLY the git-archaeology lane (Part A), which needs no external agent.
  2. The diff touches operationally-relevant surface: services with on-call history, code that emits alerts/metrics/log lines operators watch, error handling, retries, timeouts, rate limits, queue/batch processing, or config controlling any of these. A docs change or a pure-UI tweak does not need an incident sweep — skip and say so in one line.

Part A: Git archaeology (code-indexed, always available)

The highest-value catch in this entire lane: a diff that removes or weakens a line that exists because of a past incident. Look at what the diff DELETES or LOOSENS — guards, retries, timeouts, limits, locks, ordering, special-case branches with no obvious purpose — and ask where each came from:

execute_command --command "git log --oneline -3 -L <start>,<end>:<file>"
execute_command --command "git log --oneline -S '<deleted snippet>' -- <file>"

Read the originating commit message (git show --stat <sha>). Signals that a line was born in an incident fix:

  • Ticket/incident references (INC-, JIRA keys, "postmortem", "outage", "hotfix", "pages", "sev")
  • Fix-shaped messages ("prevent X under load", "handle Y race", "bound Z to avoid OOM")
  • A commit that touches only this guard, dated near a known incident

A deleted/weakened line whose origin is an incident fix is a 🔴 CRITICAL finding — the diff reintroduces a known production failure mode. Cite the line, the originating commit, and its message. If the origin is ordinary feature work, no finding — do not manufacture history.

Part B: Extract symptom-vocabulary search keys from the diff

The incident record is indexed by what OPERATORS saw, not by file paths. Before delegating, translate the diff into that vocabulary:

  1. Error/log strings added, changed, or deleted — incidents are found by error strings more than by anything else. A DELETED log line is itself a lead: someone may rely on it for triage.
  2. Metric, alert, and dashboard names the code emits or the change affects.
  3. Config keys and their old/new values (timeouts, limits, feature flags).
  4. Service/feature/domain terms an operator would use ("invoice proration", "webhook retries", "usage export") — not function names.
  5. External dependencies touched (queues, third-party APIs, databases) — their names appear in incident titles.

Collect 3-8 strong keys. Weak generic keys ("error", "billing") flood the search; skip them.

Part C: Delegate to the prior-art agent (REVIEW MODE)

Spawn the configured agent. Its normal job is live-incident triage, so the prompt MUST re-scope it — the spawn prompt is its entire context:

agent__spawn --agent <prior_art_agent> --prompt "REVIEW MODE — prior-art check for a proposed code change (NOT live triage; nothing is on fire).

## CHANGE SUMMARY
<2-4 sentences: what the diff does, which service/feature, what operational surface it touches>

## SEARCH KEYS
<the Part B keys: error strings, metric/alert names, config keys, feature terms>

## TASK
Search the incident record (handoff docs, Slack, Jira, postmortems) for past incidents matching these keys. For each relevant hit report:
- Reference (ticket/thread/doc section) and date
- What happened and what the resolution was
- Relevance: does this change RISK REINTRODUCING that failure mode, or should it ADOPT a safeguard from that resolution?

Only report incidents with a concrete connection to these search keys. 'The billing system has had incidents' is noise. If nothing relevant exists, say so plainly — a clean result is a valid result.

You are read-only. Do not post, comment, or edit anything."

Folding findings into the review report

Prior-art findings use the SAME severity taxonomy as the rest of the review — no separate verdict:

FindingSeverity
Diff reintroduces a past incident's failure mode (archaeology hit on a deleted guard, or historian match showing this exact pattern caused an incident)🔴 CRITICAL — cite the incident/commit
Past incident's resolution added a safeguard the new code should mirror but doesn't (sibling code got a fix; this new path lacks it)🟡 WARNING
Related incident exists; change looks safe but reviewer/author should know the history🟢 SUGGESTION — informational, with the reference
Historian found nothing relevantOne line in the report: "Prior-art check: no relevant incident history found for ."

Present these under a dedicated "Operational history" section in the final report, each finding citing its incident reference or originating commit.

Anti-patterns

  • Running the incident sweep on every trivial change — it is trigger-gated for a reason; Slack/Jira searches are slow and rate-limited.
  • Blocking on vague similarity ("this area had incidents once") — a 🔴 requires a concrete reintroduction path tied to a specific incident or originating commit.
  • Searching by file paths or function names — the incident record doesn't know them; translate to symptom vocabulary first.
  • Skipping Part A because no prior-art agent is configured — archaeology is local git work and always available.
  • Treating a clean historian result as wasted effort — "no prior art" is signal, and it belongs in the report as one line, not zero.
  • Manufacturing findings from ordinary-feature-work commits to have something to say. Most deleted lines were not incident fixes.

Frequently asked questions

What does the Incident Prior Art AI skill do?

Check a code change against operational history - past incidents, outages, and on-call fixes - so a review catches regressions of hard-won production lessons. Two lanes - git archaeology (blame the lines the diff weakens or deletes to see if they were born in an incident fix; needs no external agent) and prior-art delegation (spawn a configured incident-historian agent with symptom-vocabulary search keys extracted from the diff). Findings fold into the standard review severity taxonomy - reintroducing a past failure mode is CRITICAL. Grants shell access for git history commands.

Why use Incident Prior Art on TypingMind?

Because you install it once and use it with any model. Incident Prior Art 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 Incident Prior Art in TypingMind?

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

Which AI models can use Incident Prior Art?

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 Incident Prior Art?

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

Is the Incident Prior Art AI skill free?

Yes. It is published on GitHub by Dark-Alex-17 under the AGPL-3.0 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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