Jq

jq — JSON processing with the jq CLI

When to use

• User wants to extract, filter, transform, aggregate, or pretty-print JSON with jq
• Input is JSON / JSON array / NDJSON (newline-delimited JSON), API payloads, logs, configs
• Prefer this over writing a throwaway Python/Node script for the same JSON job

Do not use when:

• File is binary, CSV, XML, YAML-only (unless already converted to JSON)
• Task is general bash/Python scripting with no JSON core
• User only needs to open/edit JSON in an editor (no filter)

Prerequisites

command -v jq >/dev/null || { echo "jq not installed"; exit 1; }
jq --version   # expect 1.6+

If missing: tell the user to install (brew install jq / apt install jq) and STOP.

Procedure

Work phases in order. Do not skip. Prefer pure jq over python/node for JSON work.

Phase 1 — Structure analysis

1. Identify inputs: path(s), stdin, or API response the user provided.
2. Peek schema before complex filters:
   • Small file: jq 'type, (if type=="array" then length else keys end)' <file>
   • Huge / unknown: jq -c 'limit(1; .)' <file> or first NDJSON line via head -n 1
   • Validate: jq empty <file> — non-zero exit → report parse error and STOP
3. Note shape: object vs array vs NDJSON stream; nested keys needed; size class (<10MB / large).
4. Completion: input path(s) known, type known, filter target keys identified (or error reported).

Phase 2 — Filter construction

Design filter with explicit pipeline stages (compose with |):

1. Select path into focus: .items[], .[], .data.results?
2. Filter rows: select(.status == "active")
3. Transform shape: {id, name: .user.name} or map(...)
4. Aggregate if needed: group_by(.k) | map({k: .[0].k, n: length}) or map(.items | map(.price*.qty) | add)
5. Output flags: pretty default; -r bare strings; -c compact; -s only if slurp is required

Safety (mandatory):

• Pass untrusted strings via --arg / --argjson, never interpolate into the filter string
• Optional paths: use ? (.a.b?) to avoid hard errors on missing keys
• NEVER redirect jq onto the same path it reads: jq ... file > file truncates the file. Always:

jq '<filter>' file.json > file.json.tmp && mv file.json.tmp file.json

Deep cookbook (load only if needed): references/patterns.md

6. Completion: one copy-pastable jq ... command ready; --arg used where user/env text is involved.

Phase 3 — Execution

1. Run via shell: jq [flags] '<filter>' <file> or pipe into jq.
2. File rewrites: temp + mv only (see Safety).
3. Large files / NDJSON: stream; do not jq -s for simple filters/counts.
4. On non-zero exit: capture stderr; fix or report; do not invent output.
5. Always include the exact command in the user-facing answer.
6. Completion: command ran; stdout/stderr captured; exit code known.

Phase 4 — Validation

1. Empty result → re-check with keys, sample object, ? / casing.
2. Confirm output type matches request (JSON vs raw list vs count).
3. For file writes: jq empty file.json and spot-check changed + preserved fields.
4. Return command + short summary + truncated sample if large.
5. Completion: answer matches intent, or clear failure + diagnostic.

Essential patterns (keep loaded)

jq '.users[] | select(.age > 21)' data.json
jq 'map({name: .user.name, role: .auth.role})' data.json
jq 'group_by(.category) | map({cat: .[0].category, count: length})' data.json
jq --arg id "$ID" '.[] | select(.id == $id)' data.json
jq -r '.[].title' pulls.json
jq -c 'select(.level=="error" and .service=="auth")' app.ndjson | wc -l
jq '.version="2.0.0"' package.json > package.json.tmp && mv package.json.tmp package.json
# nested totals example
jq '[.orders[] | select(.status=="paid") | {buyer: .buyer.name, total: ([.items[] | .price*.qty] | add)}] | sort_by(-.total)' orders.json

Guardrails

• Validate JSON first (jq empty).
• --arg / --argjson for external values; single-quote filters in the shell.
• Avoid -s on large/NDJSON inputs for simple work.
• No shell-injecting user text into the filter body.
• Prefer jq over Python/Node for the same JSON transform.
• File rewrite: temp + mv only; never jq ... f > f.

Examples

Extract names (raw)

jq -r '.users[] | select(.status=="active" and .age>21) | .name' users.json | sort

Update config safely

jq '.version="2.0.0" | .scripts.build="tsc -b"' package.json > package.json.tmp && mv package.json.tmp package.json

Env-safe lookup

jq --arg name "$TARGET_NAME" '.users[] | select(.name == $name)' users.json

Non-trigger

"Convert this CSV" / "write a Python ETL" → do not load this skill.

References (load only if needed)

• references/patterns.md — streaming, merge, walk, try/catch, group_by detail