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 skills/romiluz13/auto-pi/memory-compoundingnpx skills add romiluz13/auto-pi --skill memory-compoundinggit clone --depth 1 https://github.com/romiluz13/auto-piWhat 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 | $0.00041 | $0.00671 |
| Opus 5 | $0.00020 | $0.00336 |
| Sonnet 5 | $0.00008 | $0.00134 |
| Haiku 4.5 | $0.00004 | $0.00067 |
Grade A, and why
memory-compounding 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Compounding
Memory is an index, not a transcript. Without review it accumulates stale entries that mislead future work. This skill is the discipline for keeping memory sharp, not just large.
When to use
- Monthly memory hygiene (AGENTS.md: "review both, prune stale entries").
- You notice the same gotcha recorded 3+ times.
- A memory entry contradicts the current code (trust the code).
- After a non-trivial debug/build cycle that produced a reusable learning.
The 5-outcome review rubric
For each entry in ~/.pi/agent/pi-hermes-memory/MEMORY.md, USER.md, failures.md, and the SQLite memories table (via memory_search), apply exactly one outcome:
| Outcome | When | Action |
|---|---|---|
| Keep | Accurate + useful | Leave as-is |
| Update | Correct but incomplete | Add the missing detail |
| Consolidate | Same lesson appears multiple times | Merge into one entry, remove duplicates |
| Replace | Outdated or superseded by current code | Replace with the current truth |
| Delete | No longer applies (framework changed, code removed) | Remove |
The promotion rule
When the same gotcha appears 3+ times in failures.md or across memory entries, promote it: write a dedicated reference file or a new skill section. A lesson that keeps recurring is a pattern, not a one-off — it earns a permanent home.
Solution docs (durable cross-project learnings)
After any non-trivial debug/build cycle, evaluate whether to write a solution doc to docs/solutions/:
- Write if: the problem took 3+ hypotheses to solve, OR the bug pattern appears in 3+ files, OR the solution contradicts a common assumption.
- Skip if: the fix was mechanical (typo, import error, one-line change).
Solution doc format:
# [Problem Title]
Category: debugging | architecture | testing | workflow | conventions
Tags: [comma-separated]
Date: YYYY-MM-DD
## Problem
[symptoms, not just the error message]
## What Didn't Work
[failed hypotheses and why]
## Solution
[what worked — with code example]
## Why
[the underlying principle]
## Prevention
[how to prevent this class of problem]
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 · 66 lines · 41 tokens per session scan A 389fb4896a83
memory-compounding is a skill published in the GitHub repository romiluz13/auto-pi (11 stars, last pushed 17d ago), licensed MIT. It adds 41 tokens to every session and 671 once invoked, about $0.0002 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 skills, from other repositories
wayfinder
Plan a huge chunk of work (more than one agent session can hold) as a shared map of decision tickets on your issue tracker, and resolve them one at a time until the way to the destination is clear.
ask-matt
Ask which skill or flow fits your situation. A router over the skills in this repo.
teach
Teach the user a new skill or concept, within this workspace.
writing-for-agents
Writing documents for agents. Use when creating or editing skills, or modifying AGENTS.md or CLAUDE.md.
diagnosing-bugs
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
setup-matt-pocock-skills
Configure this repo for the engineering skills: set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.