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 fabioc-aloha/Alex_Skill_Mall --skill model-task-executiongit clone --depth 1 https://github.com/fabioc-aloha/Alex_Skill_MallWrote 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/fabioc-aloha/alex_skill_mall/model-task-execution)<a href="https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/model-task-execution"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/model-task-execution.svg" alt="Measured on agentmods" 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 | $0.00059 | $0.00817 |
| Opus 5 | $0.00030 | $0.00409 |
| Sonnet 5 | $0.00012 | $0.00163 |
| Haiku 4.5 | $0.00006 | $0.00082 |
Grade A, and why
model-task-execution 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 7d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Task Execution
Execute exactly what the user approved. A valid plan is necessary but not sufficient: explicit user consent must appear in the current conversation.
Preflight
- Validate the plan against the model-task plan schema.
- Compute a SHA-256 plan hash over canonical JSON and show it with the consent summary.
- Confirm
consent.requiredandconsent.statusmatch the operation. - Show provider, model, operation, transmitted inputs, retention evidence, cost estimate status, maximum approved cost, and fallbacks.
- Check whether the selected provider/model requires a credential. If it does,
verify only that provider-native login or the named host environment variable
is available; never print or persist its value. If missing, stop and route
to
setup-ai-operations. - Ask for explicit user consent. Do not invoke a paid, externally visible, state-changing, or data-transmitting provider tool before approval.
- Store the approved plan hash in memory for this execution only. Do not write credentials, raw secrets, or private inputs to an execution manifest.
Material-Change Gate
Any change to the provider, model, data boundary, or cost ceiling invalidates the plan hash and requires renewed consent. The same applies when a fallback adds a new provider, transmits additional data, or changes output visibility.
Dispatch
- Mark the next step
approved. - Invoke only the provider tool named in the step.
- Capture the provider job ID, exact model/version, start time, and immediate response.
- Poll status through the provider's own read operation. Do not submit a second job merely because the first is slow.
- If the provider fails, stop and report the error. Apply only an already approved fallback whose plan hash still matches.
- Download or attach outputs before provider retention removes them.
- Mark the terminal status and capture duration, available usage/cost evidence, output identifiers, and any data-retention note.
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.
- 7d ago First seen · 88 lines · 59 tokens per session scan A f7ba9c1577f0
model-task-execution is a skill published in the GitHub repository fabioc-aloha/Alex_Skill_Mall (4 stars, last pushed 3d ago), licensed MIT. It adds 59 tokens to every session and 817 once invoked, about $0.0003 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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