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 agentmods add skills/patforna/auto-task/panelnpx skills add patforna/auto-task --skill panelgit clone --depth 1 https://github.com/patforna/auto-taskWhat 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 | $0.00033 | $0.00518 |
| Opus 5 | $0.00016 | $0.00259 |
| Sonnet 5 | $0.00007 | $0.00104 |
| Haiku 4.5 | $0.00003 | $0.00052 |
Grade A, and why
panel 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 2d 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Panel
Precondition
Must be run from main thread (reason: subagents can't spawn other subagents)
Usage
/at:panel <prompt>
Prompt is mandatory — if missing, fail loudly; don't infer.
Goal
Get independent perspectives on a topic from multiple models. Default line-up: the strongest available Claude model + Codex. Callers (or project config, e.g. auto-task's config) may specify different panelists.
Step 1: Expand Skill References
If the prompt invokes a skill (explicit /name only — not bare names in prose/diagrams), inline its SKILL.md, clearly fenced, after the instruction. Recurse into /name invocations found inside, but inline each skill at most once — never re-expand one already present (terminates; prevents cycles and bloat).
Example: /at:panel please /at:plan-task 054 but don't write to the task file.
Expanded prompt:
""" Please /at:plan-task 054 but don't write to the task file.
Skill references: === BEGIN plan-task SKILL.md === [full content of the plan-task skill's SKILL.md — resolve the skill wherever it is installed, e.g. the plugin cache under ~/.claude/plugins/cache/ or the project's .claude/skills/] === END plan-task SKILL.md === """
Step 2: Append Anti-Sycophancy Block
Append the block below to the prompt:
Answer directly and honestly. Do not hedge or soften to be agreeable. If your honest answer differs substantively from what the prompt seems to expect, give that one.
Step 3: Dispatch
Spawn one subagent per panelist in parallel and pass through the prompt — by default a Codex panelist (via codex:codex-rescue) and a Claude panelist (strongest available model).
If a requested panelist is unavailable (e.g. usage limit), fail loudly — do not substitute or skip.
Write the responses to /tmp/panel-<timestamp>-<slot>.md.
Step 4: Deliver
Present all responses verbatim, each directly attributed to its model (e.g. Codex / Claude), including the paths to the raw responses.
Do not synthesise, reconcile, or edit.
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.
- 2d ago First seen · 53 lines · 33 tokens per session scan A 1f97c8096980
panel is a skill published in the GitHub repository patforna/auto-task (3 stars, last pushed 14d ago), licensed MIT. It adds 33 tokens to every session and 518 once invoked, about $0.0002 per session on Opus 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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