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 ne11nn/cantos-plugin --skill wrapgit clone --depth 1 https://github.com/ne11nn/cantos-pluginWrote 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/ne11nn/cantos-plugin/wrap)<a href="https://agentmods.dev/skills/ne11nn/cantos-plugin/wrap"><img src="https://agentmods.dev/badge/skills/ne11nn/cantos-plugin/wrap/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/ne11nn/cantos-plugin/wrap"><img src="https://agentmods.dev/badge/skills/ne11nn/cantos-plugin/wrap.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 | $0.00045 | $0.03412 |
| Opus 5.5 | $0.00018 | $0.01365 |
| Sonnet 5.5 | $0.00009 | $0.00682 |
| Haiku 4.5 | $0.00005 | $0.00341 |
Grade A, and why
wrap 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 — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What This Skill Does
End-of-session review. You look at what happened this session, extract anything worth keeping, and update the right files. Then commit.
/wrap is the authoritative end-of-session marker. The user invokes it only when the work is done, reviewed, and approved — never as a mid-session checkpoint. Treat this as the definitive close: the work is final, which licenses decisive consolidation. Don't tentatively jot what happened — lock in the wholistic patches that make this session's lessons permanent and stop their whole class from recurring.
This is the permanent record of the session. Do it right.
Step 1 — Identify the Active Assistant
Look at how this session started. Which assistant morphed in? Options:
folio→ brain file:.assistants/folio/folio.mdlyren→ brain file:.assistants/lyren/lyren.mdpylon→ brain file:.assistants/pylon/pylon.mdcantos(no morph) → no single brain file; update system-level files only- any assistant added during setup → its brain file at
.assistants/<name>/<name>.md
Step 1.5 — Consume the Brain Update Queue
Before reviewing the live conversation, check .tmp/brain-update-queue.md. When the optional brain_update_hook.py Stop hook is wired (see its activation note in .assistants/cantos/cantos.md), it writes candidate rules there at session end; it never auto-applies them, so wrap is where they get routed. The hook is opt-in and may be unwired — in that case the file is simply absent and you review the conversation directly (this step still runs from Step 2 onward).
For each unprocessed entry in the queue:
- Read the candidate (assistant tag, proposed rule, evidence).
- Run it through the three-question routing test in Step 3 below — same as for any candidate surfaced from this session's conversation.
- If applied, move the entry to a
## Appliedsection at the bottom of the queue file with a one-line note on what was edited and where. - If discarded (the candidate was a false positive — wrap notes the reason), move it to a
## Discardedsection. - Never leave entries lingering in the queue — every wrap empties the unprocessed section.
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 · 224 lines · 45 tokens per session scan A 58486925c780
wrap is a skill published in the GitHub repository ne11nn/cantos-plugin (1 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 3,412 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 2026-08-31.
Other skills, from other repositories
context-driven-development
Use this skill when working with Conductor's context-driven development methodology, managing project context artifacts, or understanding the relationship between product.md, tech-stack.md, and workflow.md files.
lembas
Use between workflow phases or when context feels bloated. Writes a structured checkpoint capturing task, findings, files, state, and next steps, then continues from that summary instead of the full history. Invoke standalone or automatically between quest phases.
knowledge-manager
Loads relevant patterns and known errors before track planning. Searches conductor/knowledge/ for solutions we've used before and errors we've encountered. Injects findings into the planner prompt to prevent reinventing solutions and repeating mistakes. Triggered automatically by orchestrator before PLAN step.
context-loader
Load project context efficiently for Conductor workflows. Use when starting work on a track, implementing features, or needing project context without consuming excessive tokens.
session-handoff-number-collision-with-unmerged-sibling
Detect and recover from a session-number collision when running session-handoff and a parallel/sibling session has authored a same-numbered handoff doc that hasn't merged into main yet. Use when: (1) you're in session-handoff Phase 1 about to write docs/handoffs/sessionNNNhandoff.md, (2) ls docs/handoffs/ shows the…
pm-crystallize
Promote a project-local lesson to /.great-pm/decisions.md (cross-project memory) when it has 3+ hits with high confidence. Surfaces candidates as PROPOSALS — never auto-promotes; human approval required.