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 ankitkr3/compounded --skill compounded-authorgit clone --depth 1 https://github.com/ankitkr3/compoundedWrote 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/ankitkr3/compounded/compounded-author)<a href="https://agentmods.dev/skills/ankitkr3/compounded/compounded-author"><img src="https://agentmods.dev/badge/skills/ankitkr3/compounded/compounded-author/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/ankitkr3/compounded/compounded-author"><img src="https://agentmods.dev/badge/skills/ankitkr3/compounded/compounded-author.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 | $0.00096 | $0.02757 |
| Opus 5.5 | $0.00038 | $0.01103 |
| Sonnet 5.5 | $0.00019 | $0.00551 |
| Haiku 4.5 | $0.00010 | $0.00276 |
Grade A, and why
compounded-author 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Authoring a Skill (compounded)
You have just finished a task. You are deciding whether to propose a new skill.
There are two capture modes:
- Procedure mode — a successful multi-step task worth saving as a replayable procedure. Sections 1-2 apply.
- Rule mode — the user corrected you, and the correction encodes a generalizable behavioral rule. Section 1R applies instead of sections 1-2.
Default to NOT proposing. Skills you propose pollute the .proposed/ queue, cost the user a verifier inference call on the next applicable task, and dilute the trust signal if they fail. Propose only when the capture clears the relevant bar.
Always get user approval before saving. Whichever mode you are in, you MUST ask the user to approve the proposal via AskUserQuestion before calling skill_propose.py. See section 2.5.
0. Did the auto-proposer nudge you?
Nudges arrive two ways:
- Rule mode — your turn was interrupted with a
[compounded] Correction detected ...instruction (the Stop hook blocked the stop and fed it to you). Apply section 1R. - Procedure mode — the user saw a
[compounded] Auto-propose threshold reached ...message and relayed it (e.g. "save this as a skill"). Apply sections 1-2.
Either marker is a suggestion, not an instruction. Still apply the relevant qualifying bar. The auto-proposer is conservative but it doesn't know whether the capture is generalizable. Many high-signal turns are one-off chores, and many corrections are one-off taste calls.
If you decide not to propose despite the nudge, that's fine — say nothing to the user and move on. Don't apologize for not proposing.
1R. Rule mode
The user corrected your approach (e.g. "no, web-search for the latest model first instead of relying on training data"). The lesson is the delta between three things:
- What the user asked (their original intent)
- What you did (the mistake)
- How they corrected you (the rule)
Propose a rule only when all three are true:
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 · 216 lines · 96 tokens per session scan A 0e1435517cd6
compounded-author is a skill published in the GitHub repository ankitkr3/compounded (5 stars, last pushed 3mo ago), licensed MIT. It adds 96 tokens to every session and 2,757 once invoked, about $0.0004 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.
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