OR Key
drop another .md file to compare - side-by-side diff against bug-triage

bug-triage

Gathers reported problems and ranks them so you fix what matters first.
description: "Triggers on prompt mention of 'bug-triage'."
personal 2 files

What it does for you

Gathers reported problems and ranks them so you fix what matters first.

What it produces

A recent result, so you can see the kind of work it returns.

loading…

How to get it

These run inside the Snappy workspace. Want this working in your business? I set skills like this up with you, in one focused week.

Work with me
For developers how this skill is built, graded, and how it runs

at a glance- the short version

eval modeauto
categoryOps
stages3
dependssweep, github

what's inside - the parts that make up a skill 2/4 present

A skill is just a few plain-text files. Only the main one is required. The rest are optional, added as the work needs them. This is what the skill is made of; how it runs is just below.

The skill
state/skills/bug-triage/SKILL.md present
the skill itself, in plain text
The main file. It says what the skill is and lays out the steps in plain English.
Code
state/lib/bug-triage.ts not present
code the skill can run
Optional. Many skills are just words and need no code at all.
Scripts
state/bin/bug-triage/ not present
helper scripts
Optional. Added when a skill has a few commands to run.
Loader
state/skills/bug-triage/AGENTS.md present
what the AI loads on the fly
Loaded automatically the moment this skill is needed. Kept short on purpose.

how it's graded - what counts as a good run 5 criteria · 4 deterministic · 1 judge

Each row is one thing a good run has to get right. deterministic means a quick check decides, pass or fail. judge means the AI reads the result and rates it. Grading each piece on its own (instead of one overall score) shows exactly where a run fell short, so the fix is obvious.

name
kind
check
output_file_created
deterministic
File 'state/log/bug-triage/<date>.md' exists and is non-empty (file size > 100 bytes).
clusters_nonzero
deterministic
Clusters array has length > 0 (at least one cluster identified; not empty array).
cluster_uniqueness
deterministic
clusters_are_unique = true; no two clusters share the same stack fingerprint.
signal_count_threshold
deterministic
total_signals ≥ 1 (at least one signal collected from sweep, GitHub, or Xano).
cluster_quality
judge
Clusters are semantically cohesive (signals within a cluster share root cause); unclustered < total * 0.5; data sources are properly integrated.

how it runs - the shared frame every skill uses 2/5 present

Every skill runs the same way. One part does the work, a separate part checks it, and a short loader hands the AI exactly what it needs for the job. Anything this skill doesn't use shows a one-line note saying why, on purpose, not by accident.

makes the work The worker
inferred
collect sweep snapshot from the run command
No worker is named directly, so the command this skill runs is treated as the worker.
checks the work The reviewer
inferred
clusters_are_unique` (no two from the check command
The check is a quick command that confirms the result looks right.
frame
learns Self-correction
not present

This skill doesn't fix its own gaps yet.

tidies up Background fixes
present
queued for rewrite runs in the background
Bigger fixes that can't be made on the spot get queued and rewritten in the background later.
remembers Run history
present
state/log/evals.ndjson unknown runs
Every run is written down here, so the next time this skill is used it already knows how the last runs went.
Critical rules the things this skill must not get wrong
  1. NEVER hard-fail when gh is unauthenticated - log the auth failure and continue with the other two sources (sweep + Xano)
  2. ALWAYS cluster by exact stack-fingerprint FIRST, then fall back to fuzzy keyword Jaccard ≥ 0.4 - not the other way around
  3. ALWAYS rank clusters by size × recency, not size alone - old loud bugs shouldn't drown out fresh ones

what it has learned - fixes written back in over time sample

When a run hits something this skill didn't handle, the fix gets written back into the skill so it doesn't happen again. FIXED means it was corrected on the spot. LOGGED means it's queued for a bigger rewrite. Either way, the skill gets a little better and never makes the same mistake twice.

  1. Loading feedback rows…

how the work flows- step by step

inputs sweepgithub
1 generator
invoke
collect sweep snapshot + `gh issue list --state=open` + Xano error-log, normalize, cluster, write `state/log/bug-triage/<date>.md`
2 auditor
verify
`clusters_are_unique` (no two clusters share the same fingerprint) AND shape gate
3 data
eval log
`state/log/evals.ndjson` (skill: "bug-triage")

SKILL.md- the skill, written out in plain English

Backed by: state/lib/sweep.ts + state/lib/xano.ts

bug-triage

Reads sweep snapshot (filtering to items matching bug keywords), gh issue list --state=open across active repos, and Xano error-log rows from the last N days. Clusters by stack-trace fingerprint or keyword overlap.

Steps

  1. Collect the three sources in parallel.
  2. Normalize to {source, ref, text, ts, repo?}.
  3. Cluster: exact-match on stack fingerprint first, then fuzzy on

keyword Jaccard ≥ 0.4.

  1. Rank clusters by size × recency.
  2. Emit state/log/bug-triage/<date>.md.

Eval

score("bug-triage", run_id, {
  score:
    shape_ok && clusters_are_unique ? 1.0 :
    shape_ok ? 0.5 :
    0.0,
  total_signals: total,
  cluster_count: clusters.length,
  unclustered: unclustered.length,
  primary_issue:
    !shape_ok ? "shape-failed" :
    unclustered.length > total * 0.5 ? "clustering-threshold-too-tight" :
    null,
});

clusters_are_unique: no two clusters share the same fingerprint. Shape gate prevents silent clustering failure.

Gotchas

  • gh must be authenticated; if not, log the auth failure and continue

with the other two sources rather than hard-failing.

  • Jaccard threshold 0.4 is a guess; tune once we have a week of data.

Rubric

criteria:
  - name: output_file_created
    kind: deterministic
    check: "File 'state/log/bug-triage/<date>.md' exists and is non-empty (file size > 100 bytes)."
  - name: clusters_nonzero
    kind: deterministic
    check: "Clusters array has length > 0 (at least one cluster identified; not empty array)."
  - name: cluster_uniqueness
    kind: deterministic
    check: "clusters_are_unique = true; no two clusters share the same stack fingerprint."
  - name: signal_count_threshold
    kind: deterministic
    check: "total_signals ≥ 1 (at least one signal collected from sweep, GitHub, or Xano)."
  - name: cluster_quality
    kind: judge
    check: "Clusters are semantically cohesive (signals within a cluster share root cause); unclustered < total * 0.5; data sources are properly integrated."

AGENTS.md- what the AI loads when this skill comes up

bug-triage - loader

Per-turn rules for the bug-triage skill. Full reference: state/skills/bug-triage/SKILL.md. Do not skip these.

Critical Rules

  • NEVER hard-fail when gh is unauthenticated - log the auth failure and continue with the other two sources (sweep + Xano)
  • ALWAYS cluster by exact stack-fingerprint FIRST, then fall back to fuzzy keyword Jaccard ≥ 0.4 - not the other way around
  • ALWAYS rank clusters by size × recency, not size alone - old loud bugs shouldn't drown out fresh ones

Commands

| ui model | live composition via compose_inline, persisted as artifact lang_body, reopened with OpenArtifact | |invoke: collect sweep snapshot + gh issue list --state=open + Xano error-log, normalize, cluster, write state/log/bug-triage/<date>.md |verify: clusters_are_unique (no two clusters share the same fingerprint) AND shape gate |eval log: state/log/evals.ndjson (skill: "bug-triage")

Known Pitfalls

  • Jaccard threshold 0.4 is a guess. Tune once a week of data exists. If unclustered.length > total * 0.5, primary_issue is clustering-threshold-too-tight.
  • shape_ok is the floor - score 0.5 if shape passes but uniqueness fails; 0.0 if shape itself fails.

Self-Test

An agent reading this should correctly:

  1. [ ] Continue triage when gh auth is missing (log + proceed)
  2. [ ] Cluster by stack-fingerprint before keyword Jaccard
  3. [ ] Flag unclustered > 50% as a tuning issue, not a failure

Found a gap? Edit this file. <!-- footer-injection-point -->

api.ts- the code it can call

⚠ no api.ts - this skill has no typed action surface

scripts- helper scripts it can run

prose-only skill - 2 inline code blocks live in SKILL.md above (no state/bin/ sidecar yet).

how we check it- the checks, plus the last 10 runs

rubric auto no rubric declared
recent no runs actor/auditor: unverifiable
deps sweep github

no recent runs logged - the eval contract is declared but nothing has been graded yet