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 monoes/monomind --skill mastermind-plan-to-tasksgit clone --depth 1 https://github.com/monoes/monomindWrote 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/monoes/monomind/mastermind-plan-to-tasks)<a href="https://agentmods.dev/skills/monoes/monomind/mastermind-plan-to-tasks"><img src="https://agentmods.dev/badge/skills/monoes/monomind/mastermind-plan-to-tasks/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/monoes/monomind/mastermind-plan-to-tasks"><img src="https://agentmods.dev/badge/skills/monoes/monomind/mastermind-plan-to-tasks.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 | $0.00032 | $0.02425 |
| Opus 5.5 | $0.00013 | $0.00970 |
| Sonnet 5.5 | $0.00006 | $0.00485 |
| Haiku 4.5 | $0.00003 | $0.00243 |
Grade A, and why
mastermind-plan-to-tasks scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mastermind Plan to Tasks
This skill is invoked directly via /mastermind-plan-to-tasks.
Inputs
brain_context: BRAIN CONTEXT block (injected by command, or loaded below if standalone)org_name: org to create issues in (required)plan: the plan text (required — paste inline or pipe in)project_id: assign all issues to this project (optional)workspace_id: assign all issues to this workspace (optional)dry_run: true | false (default: false — if true, print plan without creating issues)caller: command | master
Step 0 — Brain Load (standalone only)
If caller is not "command", load brain context following mastermind-protocol/SKILL.md Brain Load Procedure with namespace: ops.
Step 1 — Validate Inputs
[ -z "$org_name" ] && { echo "ERROR: --org required."; exit 1; }
[ -z "$plan" ] && { echo "ERROR: --plan required (the plan text to decompose)."; exit 1; }
orgFile=".monomind/orgs/${org_name}.json"
[ ! -f "$orgFile" ] && { echo "ERROR: Org '${org_name}' not found."; exit 1; }
Step 2 — Load Agents for Specialty Matching
echo "PLAN-TO-TASKS — $org_name"
echo "────────────────────────────────────────────────────────"
echo ""
echo "AGENTS IN ORG:"
jq -r '(.roles // [])[] | " \(.id) \(.title // "-") [\(.adapter.type // "?")]"' "$orgFile"
echo ""
Step 3 — Decompose Plan into Issues
Read the plan carefully and apply these rules:
Planning principles (from Paperclip plan-to-tasks):
- Plan deeply. Capture real detail: goals, constraints, unknowns, success criteria, risks. A shallow plan becomes rework.
- Know your team. Read the org's agents and their specialties (titles, roles, adapters) before assigning anything.
- Assign for specialty. Hand each piece of work to the most relevant agent. If no agent fits, flag the gap.
- Take responsibility. When you (the AI) are best-suited for a piece, assign it to yourself instead of delegating.
- Use the dependency tree. Express every concrete deliverable as an issue. Wire real blockers via
blockedByIssueIds. When done, dependents auto-wake. - Order, then parallelize. Sequence by real dependencies. Independent branches start in parallel.
- Enough is enough. Plans unblock execution. Don't re-plan already clear work.
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.
- 2d ago First seen · 245 lines · 32 tokens per session scan A fb1a4e31814d
mastermind-plan-to-tasks is a skill published in the GitHub repository monoes/monomind (23 stars, last pushed 2d ago), licensed Apache-2.0. It adds 32 tokens to every session and 2,425 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 2026-10-26.
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