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 uchicago-dsi/ai-sci-skills --skill mission-controlgit clone --depth 1 https://github.com/uchicago-dsi/ai-sci-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/uchicago-dsi/ai-sci-skills/mission-control)<a href="https://agentmods.dev/skills/uchicago-dsi/ai-sci-skills/mission-control"><img src="https://agentmods.dev/badge/skills/uchicago-dsi/ai-sci-skills/mission-control/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/uchicago-dsi/ai-sci-skills/mission-control"><img src="https://agentmods.dev/badge/skills/uchicago-dsi/ai-sci-skills/mission-control.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 | $0.00065 | $0.01632 |
| Opus 5 | $0.00032 | $0.00816 |
| Sonnet 5 | $0.00013 | $0.00326 |
| Haiku 4.5 | $0.00006 | $0.00163 |
Grade A, and why
mission-control 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 11d 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mission Control
Maintain the global objective while workers execute bounded tasks. Treat steering as a control signal, not a FIFO inbox.
Establish The Current Control State
Before steering, maintain a compact view of:
- global objective and current decision;
- each worker's owned task;
- last verified evidence or artifact;
- current indivisible operation, if any;
- next state-changing action;
- pending correction and whether it is still current.
Inspect the worker's actual pane, log, job, or artifact before relying on its summary. Refresh this state after a user correction, completed experiment, failure, commit, submission, or other decision-changing event.
Keep The Mission State Disposable
If the campaign needs a state file, make it a current-state dashboard, not a second lab notebook.
- Update it in place; do not append chronology.
- Keep it short, ideally under 80 lines. Delete resolved tasks, stale job states, superseded corrections, and metrics that no longer affect a decision.
- Include only the global objective and current decision, active baseline, worker ownership and state, live jobs or indivisible operations, blockers, next state-changing actions, and links to authoritative artifacts.
- Store commands, detailed metrics, provenance, failures, and historical reasoning in the lab notebook or experiment report. Link those records instead of copying them.
- If the dashboard starts accumulating history, rebuild it from current evidence rather than editing the accumulated narrative.
The lab notebook answers "what happened?" The mission-state file answers "what is true now, and what happens next?"
Classify Every Steering Message
Put steering in one of three classes:
- Interrupt now: the message changes direction, invalidates an assumption, prevents unsafe or wasted work, or supersedes the worker's current task.
- Gate the next mutation: the worker may finish its current indivisible operation, but must incorporate the correction before another commit, submission, browser/API call, transfer, deletion, or experiment.
- Queue for a natural boundary: the message is additive, nonurgent, and remains useful even if the current task completes first.
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.
- 11d ago First seen · 180 lines · 65 tokens per session scan A 79dd57e5c355
mission-control is a skill published in the GitHub repository uchicago-dsi/ai-sci-skills (17 stars, last pushed 2d ago), licensed MIT. It adds 65 tokens to every session and 1,632 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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