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 JordanGunn/nunchuck --skill task-listgit clone --depth 1 https://github.com/JordanGunn/nunchuckWrote 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/jordangunn/nunchuck/task-list)<a href="https://agentmods.dev/skills/jordangunn/nunchuck/task-list"><img src="https://agentmods.dev/badge/skills/jordangunn/nunchuck/task-list/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/jordangunn/nunchuck/task-list"><img src="https://agentmods.dev/badge/skills/jordangunn/nunchuck/task-list.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 | $0.00035 | $0.00174 |
| Opus 5.5 | $0.00014 | $0.00070 |
| Sonnet 5.5 | $0.00007 | $0.00035 |
| Haiku 4.5 | $0.00003 | $0.00017 |
Grade A, and why
task-list 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.
What it actually says
INSTRUCTIONS
- Refer to
metadata.references.
What ships with it
16 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.
- references/00_ROUTER.md 509 B
- references/01_SUMMARY.md 247 B
- references/02_TRIGGERS.md 450 B
- references/03_ALWAYS.md 205 B
- references/04_NEVER.md 176 B
- references/05_PROCEDURE.md 536 B
- references/06_FAILURES.md 311 B
- references/07_INSTRUCTIONS.md 504 B
- references/08_PROCEDURE.md 282 B
- scripts/include/pyproject.toml 280 B
- scripts/include/task_hash.py 2.2 KB runs code
- scripts/include/task_list_cli.py 2.5 KB runs code
- scripts/include/task_parse.py 2.4 KB runs code
- scripts/include/task_time.py 1.3 KB runs code
- scripts/run.sh 212 B runs code
- scripts/validate.sh 638 B runs code
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 · 31 lines · 35 tokens per session scan A 2027976b9d65
task-list is a skill published in the GitHub repository JordanGunn/nunchuck (9 stars, last pushed 9mo ago), licensed MIT. It adds 35 tokens to every session and 174 once invoked, about $0.0001 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
antfu-issue-triage
Triage open issues and PRs of the current GitHub repo - find already-fixed, duplicate, and spam items, suggest labels, gauge demand from reactions and comments, then present a table of proposed actions and comments for confirmation before touching anything. Use when asked to triage, organize, clean up, or groom issues…
factory-human-digest
Experimental workflow for summarizing work that still needs human judgment across configured pull requests, issues, feedback, errors, and delivery signals. Use for a human decision queue or bounded review.
factory
Experimental workflow for configuring an autonomous software delivery factory. Use when choosing feedback sources, schedules, worktree behavior, autonomy gates, or host automations.
factory-collect
Experimental workflow for collecting and triaging product feedback, product telemetry, runtime errors, and issue reports. Use when scanning configured signal sources and applying separate fix, reply, or close rules.
factory-recover
Experimental workflow for finding and resuming interrupted coding runs. Use after an agent host restart or an interrupted automation.
feature-prioritisation
Aplica marcos de priorización (RICE, MoSCoW, Kano, ICE, Opportunity Scoring) para clasificar características e items del backlog. Úsalo cuando se te pida priorizar características, clasificar un backlog, decidir qué construir a continuación, o evaluar compensaciones entre ideas competidoras. Produce una lista de…