Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/danielpodolsky/ownyourcode/advisegit clone --depth 1 https://github.com/DanielPodolsky/ownyourcodeWhat 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 | $0.00021 | $0.02231 |
| Opus 5 | $0.00010 | $0.01115 |
| Sonnet 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 2d 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.
- 2d ago First seen · 313 lines · 21 tokens per session scan A e484820aea92
advise is a command published in the GitHub repository DanielPodolsky/ownyourcode (276 stars, last pushed 2mo ago), licensed MIT. It adds 21 tokens to every session and 2,231 once invoked, about $0.0001 per session on Opus 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.
Other commands, from other repositories
output
Generate output artifacts from active wiki content — summaries, reports, study guides, slide outlines, timelines, glossaries, comparisons. Outputs are filed back into the wiki.
audit-quiz-coverage
Find quiz coverage gaps from recent guide/CHANGELOG/CC-releases changes and propose new questions.
retex
Retex - Capture lesson learned dans memory après fix, rollback, erreur.
al-memory-create
Generate or update memory.md file tracking decisions, changes, and learnings throughout project development for continuity across sessions. Use when you need to create or update memory, track decisions, or maintain session continuity.
dashboard
Launch the learning dashboard web UI to view and edit plans, progress, and spaced repetition data.
learn
Start learning a new topic — asks clarifying questions, researches resources, and creates a structured learning plan.