SKILLEMALL.ai

BB postgres-aiops

Use this skill whenever the user needs to operate or troubleshoot a PostgreSQL server/cluster as a DBA — a one-shot cluster health overview; server reads (version/uptime, settings, extensions, databases, roles); activity (sessions, idle-in-transaction, long-running queries, locks); query stats (pg_stat_statements top-N, EXPLAIN a statement); index health (unused indexes, missing-index hints, bloat, invalid/duplicate); table health (sizes, dead-tuple bloat, autovacuum status); replication (standby lag, replication slots, WAL); three flagship analyses — slow-query RCA (worst pg_stat_statements entry + EXPLAIN → cause/action), bloat & vacuum analysis (dead tuples + autovacuum lag → recommendation), and blocking lock-chain RCA (build the wait-for tree, name the root blocker); and guarded writes (terminate a backend, cancel a query, VACUUM/ANALYZE, create/drop an index, REINDEX, ALTER SYSTEM SET a parameter, reset query stats). Always use this skill for "postgres health check", "why is this query slow", "pg_stat_statements top queries", "EXPLAIN this", "table/index bloat", "which indexes are unused", "missing index", "autovacuum status", "who is blocking whom", "kill the backend holding the lock", "replication lag", "replication slots", "VACUUM this table", "create/drop an index", or "ALTER SYSTEM SET work_mem" when the context is a PostgreSQL database. Do NOT use when the target is OT / industrial equipment (Modbus, OPC-UA, PLCs — use industrial-aiops), a hypervisor, a storage appliance, a backup product, a container/cluster orchestrator, or a non-PostgreSQL database (negative routing hints only). Covers common PostgreSQL DBA operations with a built-in governance harness (audit, token budget, undo, risk-tiers). Beyond the mock suite, the reads plus a governed write and its undo have been exercised against a live PostgreSQL 16.14 instance (see docs/VERIFICATION.md).

ClawHub Claude Code author: wei zhou v0.10.0 MIT-0 6 files body ≈ 2 586 tokens Open the sourceclawhub.ai analyzed 2 d ago

server reads (version/uptime, settings, extensions, databases, roles); activity (sessions, idle-in-transaction, long-running queries, locks); query stats…

As a process B 71/100 · Nearly there — weak spots: result and completion, inputs and preconditions

ProcedurePostgreSQLAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
83/100
safety, quality, tests
Safety 60%
95
Quality 40%
64
Run on models
none yet
Process rating
B
71/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
55
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. Shorten the description to 1024 characters.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Bash

Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1894 chars, limit 1024
  • note description-budget description takes 1894 of the ~15000-char shared budget for all skills
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "installer"

Process rating: all ten parameters 71/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 55Failures and branches. 1 branches
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 42 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2586 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • low The response is described with custom markup (3 tags): a typed call is more reliable

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +3Description length 1893: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +5Description quotes 15 example trigger phrases
  • +4Description says when NOT to use the skill
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 42 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (3 of 4)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: suspicious
This PostgreSQL operations skill is documented and purpose-aligned, but it can run disruptive database writes without an enforced approval gate and installs an unpinned external package.
LLM: suspicious (high) · 12 Sept 2026