Borrowing it
Nothing to install: this file belongs to DanielPodolsky/ownyourcode. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/DanielPodolsky/ownyourcode/main/.claude/commands/own/advise.mdgit clone --depth 1 https://github.com/DanielPodolsky/ownyourcodeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/danielpodolsky/ownyourcode/advise)<a href="https://agentmods.dev/commands/danielpodolsky/ownyourcode/advise"><img src="https://agentmods.dev/badge/commands/danielpodolsky/ownyourcode/advise/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/danielpodolsky/ownyourcode/advise"><img src="https://agentmods.dev/badge/commands/danielpodolsky/ownyourcode/advise.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00021 | $0.02231 |
| Opus 5.5 | $0.00008 | $0.00892 |
| Sonnet 5.5 | $0.00004 | $0.00446 |
| Haiku 4.5 | $0.00002 | $0.00223 |
Grade A, and why
advise scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 313 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/own:advise
⚠️ PLAN MODE WARNING: Toggle plan mode off before running this command (
shift+tab). OwnYourCode commands don't work correctly with plan mode.
Query past learnings and leverage research tools before starting a new task.
Overview
This command is run before starting new work. It:
- Understands what you're about to work on
- Queries your learning registry for relevant patterns/failures
- Uses MCPs to research best practices and implementations
- Surfaces past insights to prevent repeating mistakes
- Ensures you have a plan before coding
"Those who don't learn from history are doomed to rewrite it with bugs."
Execution Flow
Phase 1: What Are You About to Work On?
Question: "What are you about to work on?"
Options:
1. A new feature from my spec
Description: Starting planned work
2. A bug fix
Description: Something is broken
3. An improvement/refactor
Description: Making existing code better
4. Something new (not specced)
Description: Unplanned work
Follow-up:
- "In a few words, what's the domain? (e.g., auth, forms, API, database, UI)"
- "What libraries or frameworks are you using?"
Phase 2: Query Global Learning Registry
Read the global learning registry at ~/ownyourcode/learning/LEARNING_REGISTRY.md and search for:
- Patterns matching the domain
- Failures matching the domain
- Recent learnings that might apply
Also check ~/ownyourcode/learning/patterns/ for any documented patterns in the domain.
Important: Learning is GLOBAL, not project-specific. This ensures learnings persist across all your projects.
Phase 3: Surface Relevant Learnings
If past learnings found:
┌─────────────────────────────────────────┐
│ RELEVANT PAST LEARNINGS │
├─────────────────────────────────────────┤
│ │
│ 📚 PATTERNS YOU'VE USED │
│ ───────────────────────── │
│ • [Pattern Name] (2026-01-15) │
│ "[Summary of what worked]" │
│ Location: ~/ownyourcode/learning/... │
│ │
│ ⚠️ FAILURES TO AVOID │
│ ───────────────────────── │
│ • [Issue Name] (2026-01-01) │
│ "[What went wrong and why]" │
│ Location: ~/ownyourcode/learning/... │
│ │
│ 💡 INSIGHTS │
│ ───────────────────────── │
│ • "[Key insight from past work]" │
│ │
└─────────────────────────────────────────┘
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 313 lines · 21 tokens per session scan A e484820aea92
advise is a command published in the GitHub repository DanielPodolsky/ownyourcode (292 stars, last pushed 4mo ago), licensed MIT. It adds 21 tokens to every session and 2,231 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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