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 Wang-Cankun/cankun-skills --skill confergit clone --depth 1 https://github.com/Wang-Cankun/cankun-skillsWrote 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/wang-cankun/cankun-skills/confer)<a href="https://agentmods.dev/skills/wang-cankun/cankun-skills/confer"><img src="https://agentmods.dev/badge/skills/wang-cankun/cankun-skills/confer/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/wang-cankun/cankun-skills/confer"><img src="https://agentmods.dev/badge/skills/wang-cankun/cankun-skills/confer.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 | $0.00057 | $0.02021 |
| Opus 5.5 | $0.00023 | $0.00808 |
| Sonnet 5.5 | $0.00011 | $0.00404 |
| Haiku 4.5 | $0.00006 | $0.00202 |
Grade A, and why
confer 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 yesterday.
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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Confer — cross-model consultation with resumable threads
Requires bun plus at least one provider CLI (claude, codex, pi, omp, agy, or Oracle >= 0.21.4 with an authenticated ChatGPT browser profile).
You talk to a peer model through threads: open one with a question, the peer's session id is stored, and any later round — today or next week, from any host session — resumes the same peer-side context. All mechanics live in scripts/confer.mjs (single source of truth); use it instead of assembling provider CLI calls.
scripts/confer.mjs ask [provider] [-t name] <prompt|-> # saved preference, otherwise Pi's default model
scripts/confer.mjs open <provider> [-t name] <prompt|-> # explicit claude|codex|pi|omp|agy|oracle
scripts/confer.mjs config [provider|pi-model|oracle-profile [value]] # show or save defaults
scripts/confer.mjs reply <thread> <prompt|-> # continue with full peer-side context
scripts/confer.mjs all [--with-oracle] <prompt|-> # default claude+codex; flag explicitly adds Oracle
scripts/confer.mjs list | show <thread> # registry / transcript
scripts/confer.mjs doctor [--live [provider]] # live defaults to claude+codex
Pass - as the prompt and pipe stdin for anything long or containing quotes.
A round can take minutes. When you expect a long consultation and have other work, run the call in the background and pick the reply up when notified — never relay a peer through a subagent: the peer's own words must reach the user undiluted. Each ← transcript header records which model answered (and cost/tokens where the CLI reports them).
Steps
- Resolve the target. An explicit user choice takes precedence. Otherwise take the model and its route on this machine from the user's model roster (
~/.codex/agent-notes/models.md, when present), and name it by its full version. Without a roster or a choice,askuses the saved default (config), else Pi on Pi's own default model. Pass the chosen model through its provider's override, as a full id:
What ships with it
4 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.
- yesterday Changed · +9 lines 339df52ec38d
- 3d ago Changed · +5 lines · -4 tokens per session 289c4ffc15ca
- 16d ago Changed cd45981467a4
- 17d ago Changed · +11 lines · +30 tokens per session da90c7ad5b9f
- 19d ago Changed 32e022631a28
- 27d ago Changed a7569172ed28
- 1mo ago Changed 850141b443d1
- 1mo ago Changed · -28 tokens per session de1275bd5749
- 1mo ago Changed · +4 lines 6064adc24979
- 1mo ago First seen · 50 lines · 59 tokens per session scan A 67af1c58b6c9
confer is a skill published in the GitHub repository Wang-Cankun/cankun-skills (2 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 2,021 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.
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