# Ads Next > Continuous coach. Reads the most recent -audit-results.json files (cwd + ~/.claude-ads/history/), ranks Quick Wins and Critical Issues by impact × ease using the user's saved spend mix, surfaces the top 3 next actions, and optionally walks the user through fixing the #1 right now. Detects regressions vs the previous audit (score drops, new failures) and flags them as Priority 0. Use when user says next, what should I do, recommend, fix, suggest, what's next, qué sigue, recomienda, próximo paso. - Skill: `hainrixz/ads-next` (Agent Skill) - Install (CLI): `npx skillmds@latest add hainrixz/ads-next` - Raw SKILL.md: https://api.skillmd.com/api/skills/hainrixz/ads-next/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Security - Author: Hainrixz (https://skillmd.com/u/hainrixz) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/hainrixz/ads-next --- # Continuous coaching: what to fix next `/ads next` turns audits into a stream of small, ordered, verified actions. It's the second half of the seguimiento loop: `/ads start` connects, `/ads audit` measures, `/ads next` coaches. ## Subcommand routing | Arg | Behavior | |-----|----------| | (none) | Default — Discover → Rank → Top 3 → offer to walk through #1. | | `show` | Discover → Rank → print top 10 → exit (no walk-through prompt — good for client reports). | | `compare` | Print full delta between the two most-recent audits per platform via `python3 scripts/profile.py compare

`. | | `walk` | Skip the prompt — go straight to walking the user through the top action. | ## Core rules 1. **Read the profile first.** `python3 scripts/profile.py get` — if exit 2, suggest `/ads start` and exit (a coach with no context is just guessing). 2. **One action at a time during walk-through.** Same step-by-step verified loop as `/ads start` Phase C.3. 3. **Never invent issues.** If no audits are found in cwd or history, print the exact command to generate one (`/ads audit` or platform-specific) and exit. --- ## Phase 1 — Discover audit JSON Search for `*-audit-results.json` files in this order, newest first: 1. **``** — current working directory. Glob `*-audit-results.json`. 2. **`~/.claude-ads/history/`** — the persistent log. Read `~/.claude-ads/history/index.json` and resolve files for the most recent audit per platform. Validate each file: - Must parse as JSON. - Must include `platform`, `health_score`, `checks`. - `platform` must be one of `meta`, `google`, `tiktok`. If a file fails validation, skip it and print a single `WARNING: skipped ()` line. Don't abort the run. If **zero** valid audits found: ``` No audits yet. Run one first: /ads audit — full multi-platform /ads — single platform deep-dive ``` Then exit. --- ## Phase 2 — Persist any new audits to history For each audit file discovered in cwd that is NEWER than the most recent entry for that platform in `~/.claude-ads/history/index.json` (compare `generated_at`), copy it to history via: ```bash python3 scripts/profile.py save-audit ``` This is what builds the multi-session memory — every audit run from anywhere on disk eventually ends up in the history index. --- ## Phase 3 — Rank items by impact × ease Pool every `quick_win`, every `critical_issue`, and every `check` with `result` in (`FAIL`, `WARNING`) across all valid audits. Deduplicate by (`platform`, `check_id`) — if a check appears in both `critical_issues` and `checks`, treat as one item using the critical metadata. For each item, compute an **impact score**: ``` severity_weight = { critical: 5.0, high: 3.0, medium: 1.5, low: 0.5 }[severity] effort_inverse = 1 / (fix_time_minutes + 1) # default fix_time = 30 if null budget_share = platform_budget_share(platform) # see below impact = severity_weight × effort_inverse × budget_share × 100 ``` **`platform_budget_share`** — derive from the user's profile + audit `account_id` presence: - If profile lists 1 active platform, share = 1.0 for that platform. - If profile lists 2 or 3, share = `1 / n` baseline. Then bump the platform that the user spends most on (use `monthly_spend_usd` ÷ platform count as the rough prior; if the user has set per-platform spend in the future, use that instead) by +0.1 (cap at 0.8). - If profile is empty, fall back to equal weight. Sort all items by `impact` descending. --- ## Phase 4 — Regression detection (Priority 0) For each platform with ≥2 audits in history, run: ```bash python3 scripts/profile.py compare ``` Inspect the JSON output: - `score_delta < -5` → **PRIORITY 0 alert** appended at the top of the ranked list. Format: ``` ⚠ health dropped from /100 () → /100 () since your last audit. New issues: Resolved: ``` - `score_delta > +5` → success line at the very bottom of the output: `🎉 health improved + points since last audit. Keep going.` Regressions take precedence over normal Quick Wins. They appear ABOVE the Top 3 list, with their own header. --- ## Phase 5 — Output the top 3 Default subcommand prints: ``` Next actions (ranked by impact × ease for your $XXX/mo across ): 1. [ · · ] Finding: Recommendation: Estimated fix: min · Impact: Reference: 2. [...] 3. [...] (Run `/ads next show` to see the top 10. `/ads next compare` for full audit delta.) ``` If `show` subcommand: print top 10 instead, no prompt, exit. --- ## Phase 6 — Walk-through (default and `walk` subcommands) `AskUserQuestion`: > *"Want me to walk you through fixing #1 right now?"* Options: `Yes — step by step` · `Show me #2 instead` · `Exit, I'll handle it`. On `Yes`: 1. **Determine the reference file** for this check_id based on its platform and category. Lookup table: | Platform | Category prefix | Reference file | |----------|----------------|----------------| | meta | Tracking / Pixel / CAPI | `tracking-meta.md` | | meta | Creative / fatigue | `meta-audit.md` (Creative section — covers M28 fatigue) | | meta | Structure / Budget | `meta-audit.md` | | google | Tracking / Conversion | `tracking-google.md` | | google | Bidding / Structure | `google-audit.md` | | google | Quality Score / Keywords | `google-audit.md` | | tiktok | Tracking / Events API | `tracking-tiktok.md` | | tiktok | Creative / Spark | `tiktok-audit.md` | | tiktok | Structure | `tiktok-audit.md` | If no reference matches, use the check's own `recommendation` field as the only source. 2. **Step-by-step loop** (same pattern as `/ads start` Phase C.3): - Print step 1: Goal · Do this · Expect to see · How I'll verify. - `AskUserQuestion`: `Done — verify now` · `Hit an error` · `Pause`. - On `Done`: run the verification (re-call the relevant MCP tool / API script) and confirm the check now passes. If it does → ✓, move to step 2. If not → diagnose, retry. - Cap at 3 attempts per step. 3. After the action, ask if the user wants to proceed to action #2. On `Show me #2 instead`: re-enter Phase 6 with index 2. On `Exit`: print: > *Run `/ads audit` after you've fixed this to update your score, then `/ads > next` again for the new top 3.* --- ## Phase 7 — Save state At the end: ```bash python3 scripts/profile.py set last_command "/ads next" python3 scripts/profile.py set last_command_at "$(date -u +%FT%TZ)" ``` The history index updates itself when `/ads audit` runs next — `/ads next` itself doesn't add to history (it only reads + acts). --- ## Compare mode (`/ads next compare`) For each platform with ≥2 history entries: ```bash python3 scripts/profile.py compare ``` Print the full diff for each: score delta, grade change, new issues, resolved issues, file timestamps. No ranking, no walk-through prompt — this is the audit changelog view. --- ## Error handling - Profile missing → print one-liner suggesting `/ads start`, exit. - No audits anywhere → print one-liner suggesting `/ads audit`, exit. - All audits failed validation → print the warnings collected and exit. - During walk-through: if 3 retries fail, log to console "Skipped — come back to this with `/ads next walk` later" and ask whether to try #2. ## Output contract `/ads next` produces NO files. It is purely interactive coaching plus side-effects on `~/.claude-ads/history/` (via `save-audit`) and the profile's `last_command*` fields.