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 skills add toverux/cantrips --skill compoundgit clone --depth 1 https://github.com/toverux/cantripsWrote 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/skills/toverux/cantrips/compound)<a href="https://agentmods.dev/skills/toverux/cantrips/compound"><img src="https://agentmods.dev/badge/skills/toverux/cantrips/compound/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/skills/toverux/cantrips/compound"><img src="https://agentmods.dev/badge/skills/toverux/cantrips/compound.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 | $0.00078 | $0.01369 |
| Opus 5.5 | $0.00031 | $0.00548 |
| Sonnet 5.5 | $0.00016 | $0.00274 |
| Haiku 4.5 | $0.00008 | $0.00137 |
Grade A, and why
compound 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 13d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Harvest what this session learned so future sessions inherit it. Only knowledge that compounds gets written; every write is approved first, by the user or by a deputy engaged in their place.
1. Scan the session
Sweep the whole session for candidates: corrections the user made and preferences they stated, conventions that emerged, durable decisions settled or reversed, gotchas and root causes uncovered, approaches that failed and why, domain terms coined or clarified ("we call this X, not Y"), and workflow friction a procedure could remove.
2. Apply the quality bar
For each candidate, two questions: would this change a future agent's behavior in a different session, and is it non-obvious and stable? Any "no" → discard.
This bar is the defence against the eager-auto-memory failure mode: session-specific trivia — in-flight task state, one-off values, things any agent would infer anyway — must die here.
If nothing clears the bar, say so in one line and stop.
3. Route each survivor
- Project
AGENTS.md— durable shared preferences and conventions, cheap enough to always load. Domain terms go in its glossary section; when the glossary outgrowsAGENTS.md, propose graduating it to aCONCEPTS.mdfile. docs/adr/(opt-in store) — durable decisions: a project-shaping choice settled among live alternatives — whereAGENTS.mdholds preferences anddocs/solutions/holds problem-shaped learnings. One record per decision, peradr-format.md. Reversing a standing decision routes as a pair — a new ADR plus the status flip on the superseded record.docs/solutions/(opt-in store) — problem-shaped learnings: root cause, gotcha, what didn't work. One small file per solved problem, persolutions-format.md. Expensive knowledge, loaded on demand.- Project-local rules — path-scoped project conventions, when the project uses rules files.
- Skills — project-specific procedures → the project's skills directory (
.claude/skills/on Claude Code, or the harness's equivalent); generic workflow improvements → the user's personal skills collection, if configured. - User-global memory file (
~/.claude/CLAUDE.mdon Claude Code,~/.codex/AGENTS.mdon Codex CLI, or the harness's equivalent) — personal-only preferences, and the staging area to trial a candidate durable preference before graduating it to a shared file.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago Changed · +2 lines · +4 tokens per session 22d094147f3e
- 1mo ago Changed 0407d13a9239
- 1mo ago Changed · +4 lines ca67675deaf2
- 1mo ago First seen · 55 lines · 74 tokens per session scan A 9c7e1f60e953
compound is a skill published in the GitHub repository toverux/cantrips (3 stars, last pushed yesterday), licensed MIT. It adds 78 tokens to every session and 1,369 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
context-canvas-checkpoint
Maintain an optional session task map and explicit retrievable references in Context Canvas Codex. Use when navigation or long-context offload would materially help with goals, decisions, progress, dependencies, blockers, next steps, exploration summaries, large textual tool results, or selective historical…
aitask-note
Send a durable note to an existing aitask — context that task needs which is not itself work. Use when you learn something a task that already exists depends on, such as a stale assumption in its body, a wider blast radius, or a decision that changes its approach.
yesloop
Autonomous task loop — analyze, plan, execute, verify, review, finish. Runs as visible TUI agent in git worktree. Use when user says "yesloop", "loop", "run it autonomously".
security-review
Use when reviewing code for vulnerabilities, checking diffs for injection/XSS/auth/crypto issues. Invokes on code changes (.go/.py/.js/.ts/.java/.rs/.php/.rb), not docs-only diffs.
cortex-memory
Persistent cognitive memory for AI agents — query, record, review, and consolidate knowledge across sessions with spreading activation, FSRS scheduling, and NLI contradiction detection.
git-pr-review
A read-only reviewer for GitHub pull requests, which are proposed code changes submitted for review. It produces an evidence-based report about whether a pull request should be merged.