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 larsboes/Axon --skill accordion-context-recallgit clone --depth 1 https://github.com/larsboes/AxonWrote 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/larsboes/axon/accordion-context-recall)<a href="https://agentmods.dev/skills/larsboes/axon/accordion-context-recall"><img src="https://agentmods.dev/badge/skills/larsboes/axon/accordion-context-recall/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/larsboes/axon/accordion-context-recall"><img src="https://agentmods.dev/badge/skills/larsboes/axon/accordion-context-recall.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 | $0.00089 | $0.00503 |
| Opus 5.5 | $0.00036 | $0.00201 |
| Sonnet 5.5 | $0.00018 | $0.00101 |
| Haiku 4.5 | $0.00009 | $0.00050 |
Grade A, and why
accordion-context-recall 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.
This is a copy
100% identical to accordion-context-recall — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Accordion, an external desktop app, may be folding parts of your context to save tokens. A folded block appears as:
{#3f9a2c FOLDED} Assistant analyzed the test failures: three imports were missing…
The 3f9a2c is a fold code — the short summary after FOLDED} is a digest. The original content is preserved and always retrievable.
recall — read folded content this turn
Call recall with one or more codes copied verbatim from the markers:
recall({codes: ["3f9a2c"]})
recall({codes: ["3f9a2c", "00a2cd"]})
The full original content of each block comes back as this tool's result, immediately — like reading a file. Your standing context is not changed: the block stays folded, costs no extra tokens, and the human's view is untouched.
This is the right choice when:
- You need a specific value, piece of code, or detail for the current step only
- You don't want the block to permanently reopen (it stays folded for future turns)
- You're under a tight token budget and opening it permanently would trigger more folding
When to use unfold instead
unfold({codes: [...]}) forces the block open in your standing context — the full content appears on your next turn and stays open until the human re-folds it. Use unfold when you will keep referencing the block over several turns, not just once.
Summary:
- recall — read once, now. No context change. Content in the tool result this turn.
- unfold — keep it open. Content in your context next turn, stays open.
Prefer recall for one-off reads; use unfold only when repeated access justifies the token cost of keeping it open.
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 · 39 lines · 89 tokens per session scan A 363de83c9b06
accordion-context-recall is a skill published in the GitHub repository larsboes/Axon (1 stars, last pushed 1mo ago), licensed MIT. It adds 89 tokens to every session and 503 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to accordion-context-recall, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
notes
Skill "notes" from Pinvou/pinvou-agent, covering ima notes, operations, write rules, examples and response handling.
shodh-memory
Persistent memory system for AI agents. Use this skill to remember context across conversations, recall relevant information, and build long-term knowledge. Activate when you need to store decisions, learnings, errors, or context that should persist beyond the current session.
learn
Manually record an insight, correction, preference, or rule that the agent should remember for future tasks.
memory-kit
Create a workspace-local .cowork/ memory kit (rules, identity, long-term notes, daily logs, heartbeat templates).
memorywhale
Query and write durable debugging memory recorded by MemoryWhale. Use when debugging a failure that may have happened before, when you need the exact error/flags/output from an earlier attempt, when the user asks "how did we fix this last time?", or once you've figured out why something failed / how a fix worked and…
tma1-peer
List recent sessions on this project by agent — peers (Claude Code, OpenClaw, Copilot CLI) or your own past sessions. Invoke this skill when the user asks you to read another agent's review feedback, see what someone else tried, act on cross-agent context, or recall your own earlier work here. Trigger phrases: "what…