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 jmagly/aiwg --skill mission-controlgit clone --depth 1 https://github.com/jmagly/aiwgWrote 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/jmagly/aiwg/mission-control)<a href="https://agentmods.dev/skills/jmagly/aiwg/mission-control"><img src="https://agentmods.dev/badge/skills/jmagly/aiwg/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/jmagly/aiwg/mission-control"><img src="https://agentmods.dev/badge/skills/jmagly/aiwg/mission-control.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 | $0.00026 | $0.01495 |
| Opus 5.5 | $0.00010 | $0.00598 |
| Sonnet 5.5 | $0.00005 | $0.00299 |
| Haiku 4.5 | $0.00003 | $0.00150 |
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 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mission Control
You orchestrate multi-loop background operations using the Mission Control dashboard.
Triggers
Alternate expressions and non-obvious activations (primary phrases are matched automatically from the skill description):
- "mc start" / "mc dispatch" / "mc status" → Mission Control operations shorthand
- "background this" → dispatch as background mission
- "war room" for multi-task coordination → Mission Control session
Trigger Patterns Reference
| Pattern | Example | Action |
|---|---|---|
| Background tasks | "run these tasks in the background" | Start session + dispatch |
| Parallel orchestration | "orchestrate X and Y in parallel" | Start session + dispatch each |
| Monitor loops | "monitor background tasks" | aiwg mc status or aiwg mc watch |
| Start session | "start a mission control session" | aiwg mc start |
| Check status | "how are the background tasks doing?" | aiwg mc status --json |
| Stop missions | "stop background work" | aiwg mc stop |
Behavior
When triggered:
-
Determine intent:
- Starting new background work →
aiwg mc start+aiwg mc dispatch - Checking on existing work →
aiwg mc status - Stopping work →
aiwg mc stop
- Starting new background work →
-
For new background orchestration — Mission Control has a four-step lifecycle: start → dispatch → run → status. Missions stay
queueduntilmc runlaunches them as ralph loops; status syncs back to mc.session.json automatically whenmc statusormc watchis called.Apply the LFD loop-control contract before dispatching long-running or eval-driven missions:
- Give each mission a measurable completion criterion and at least one mechanical verifier; self-report is secondary evidence.
- Declare hard limits for iterations, wall-clock, token/tool/spend budgets where the surface can observe them. Budget exhaustion stops the mission and emits a best-output report instead of continuing randomly.
- Record the pre-change hypothesis, expected failure mode, distinguishing diagnostic, and structural variant for each retry cycle.
- After flat/non-improving cycles, require a structurally different approach within the configured exploration quota.
- For eval/holdout missions, expose only aggregate score/probe/status or VOID to workers; keep holdout answers and detailed lint diagnostics outside optimizer-readable mission output.
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 · 164 lines · 26 tokens per session scan A 0ed4115fb91d
mission-control is a skill published in the GitHub repository jmagly/aiwg (225 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 1,495 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 2026-08-30.
Other skills, from other repositories
launch
Planning releases and reporting delivery work from GitHub PR history. Use when versioning, CHANGELOGs, rollout or rollback plans, engineering metrics, retrospectives, or stakeholder reports are needed.
pdm
Navigating delivery status read-only: reconciles planned scope (specs/roadmap/PRD) against implemented code for what's built vs left. Not for priority scoring (Rank) or AC conformance (Attest).
sherpa
Guiding workflows by decomposing complex tasks (Epics) into Atomic Steps under 15 minutes each, with progress tracking and drift prevention. Use when complex decomposition is needed.
rank
Quantifying priority by scoring competing items with ICE/RICE/WSJF/MoSCoW/Cost of Delay/Kano. No code. Use to prioritize features/bugs/initiatives or arbitrate Must vs Should at MVP scoping.
spark
Proposing new features leveraging existing data/logic as Markdown specifications. Use when brainstorming new features, product planning, or feature proposals are needed. Does not write code.
missive
Invoke only when spawning a quest from a GitHub issue reference. Fetches GitHub issue context for quest spawning. Parses issue references, retrieves structured data via gh, and produces branch suggestions and PR keywords. Used standalone or as input to quest orchestration.