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 xobotyi/cc-foundry --skill task-creationgit clone --depth 1 https://github.com/xobotyi/cc-foundryWrote 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/xobotyi/cc-foundry/task-creation)<a href="https://agentmods.dev/skills/xobotyi/cc-foundry/task-creation"><img src="https://agentmods.dev/badge/skills/xobotyi/cc-foundry/task-creation/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/xobotyi/cc-foundry/task-creation"><img src="https://agentmods.dev/badge/skills/xobotyi/cc-foundry/task-creation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00050 | $0.03227 |
| Opus 5.5 | $0.00020 | $0.01291 |
| Sonnet 5.5 | $0.00010 | $0.00645 |
| Haiku 4.5 | $0.00005 | $0.00323 |
Grade A, and why
task-creation 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 — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A work item has one reader, and the two possible readers pay opposite costs. A human implementer picks the item up later and pays for volume: unstructured and over-long prose is what developers name among the problems that most delay a fix. An autonomous agent is handed the item directly and pays for absence: a large share of real issues are underspecified for one, and naming the files a change touches is the single largest measured lift in its success rate. The opposite costs — over-specification to an agent, under-specification to a human — are not established, so no rule here trades on them. Establish which reader the item is for, and let that decide what goes in.
Write only what was run or read. Completing a missing section trades absence for error: the item stops failing for what it omits and starts failing for what it asserts, and a machine writer buys structural completeness while reproducibility barely moves. Completeness is not accuracy. Plausibility survives a style checklist, so the check is verification against the system — run the path, read the code, capture the output — and whatever could not be verified is marked as such inside the item.
A filed item cannot answer a question. Interrogating a human recovers most of what underspecification costs, and a model cannot reliably tell an underspecified task from a complete one. Two consequences hold together: everything the implementer needs is front-loaded, because the queue has nobody to ask, and the writer cannot trust its own judgement that the item is complete, which is what the approval gate is for.
Establish the reader before drafting
- The human implementer is the default reader. Write for a person picking the item up later, including when the assignment is unknown.
- Select the agent reader only on a signal that the work goes to an agent — an explicit assignment to a coding
agent, an AFK classification in a task breakdown, or a queue an agent drains. Absent one of those, the reader is the
human implementer. The AFK and HITL classification itself belongs to
tasks. - The readers differ on volume, never on truth. Nothing in the reader decision licenses an unverified claim for either one.
What ships with it
1 file 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.
- 1mo ago First seen · 220 lines · 50 tokens per session scan A b5e26d4d64c0
task-creation is a skill published in the GitHub repository xobotyi/cc-foundry (22 stars, last pushed 7d ago), licensed MIT. It adds 50 tokens to every session and 3,227 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 2026-09-04.
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