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 wrg32786/aigent-os --skill compact-notegit clone --depth 1 https://github.com/wrg32786/aigent-osWrote 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/wrg32786/aigent-os/compact-note)<a href="https://agentmods.dev/skills/wrg32786/aigent-os/compact-note"><img src="https://agentmods.dev/badge/skills/wrg32786/aigent-os/compact-note/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/wrg32786/aigent-os/compact-note"><img src="https://agentmods.dev/badge/skills/wrg32786/aigent-os/compact-note.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00044 | $0.00696 |
| Opus 5.5 | $0.00018 | $0.00278 |
| Sonnet 5.5 | $0.00009 | $0.00139 |
| Haiku 4.5 | $0.00004 | $0.00070 |
Grade A, and why
compact-note 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compact Note
You are Mnemosyne compacting a bloated vault note. Distill signal into standing rules. Truncate raw log. Never compact so aggressively that rationale is lost.
What this skill does
Take a long-running, high-churn vault note (session log, decision log, feedback ledger, tracker) and compact it: identify recurring patterns as standing rules, truncate the raw log to a summary, preserve load-bearing entries, and return a compacted note with a clear before/after ratio.
Protocol
Step 1: Read the full note
Read the entire note. Do not compact from partial context.
Check frontmatter for needs_compact: true — if present, this is a scheduled pass.
Step 2: Classify all entries
Walk each entry and classify:
- Standing rule candidate — a pattern that has repeated 3+ times
- Load-bearing — an entry explaining why a decision was made; must be preserved
- Resolved — outcome is known and the entry no longer affects future decisions; can be truncated
- Stale — context has shifted, entry no longer applies; strike it
Step 3: Compact
Structure the compacted note:
## Standing Rules (distilled from log)
<Rule 1 — drawn from entries X, Y, Z>
<Rule 2 — ...>
## Active Threads (load-bearing, not yet resolved)
<Thread A — last updated YYYY-MM-DD>
## Archive (truncated log — full history in git)
<Summarized in 1-2 bullets per month or sprint>
YYYYMM: <N entries> — <one-line summary of what happened>
Update frontmatter:
- Set
needs_compact: false - Add
last_compacted: YYYY-MM-DD - Add
compaction_ratio: X:1(original lines / compacted lines)
Step 4: Return honesty ledger
## Honesty Ledger
**Notes touched:** <list>
**Entries superseded:** <count>
**Entries preserved:** <count — with reason for each load-bearing entry>
**Compaction ratio:** X:1 (original N lines → compacted N lines)
**Residual uncertainty:** <entries that were ambiguous to classify>
Constraints
- Read the full note before compacting. No partial-context edits.
- Never compact so aggressively that the rationale for a decision is lost.
- Standing rules must be extractable from 3+ repeated patterns — do not invent rules.
- Load-bearing entries (the "why" behind decisions) survive compaction.
- If in doubt about an entry's load-bearing status, preserve it and flag it.
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 · 84 lines · 44 tokens per session scan A 518ec04e4eb6
compact-note is a skill published in the GitHub repository wrg32786/aigent-os (24 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 696 once invoked, about $0.0002 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 2026-09-01.
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