Borrowing it
Nothing to install: this file belongs to Black-Swan-Causal-Labs/target-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Black-Swan-Causal-Labs/target-mcp/main/.claude/skills/target-checklist-fanout/SKILL.mdgit clone --depth 1 https://github.com/Black-Swan-Causal-Labs/target-mcpWrote 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/black-swan-causal-labs/target-mcp/target-checklist-fanout)<a href="https://agentmods.dev/skills/black-swan-causal-labs/target-mcp/target-checklist-fanout"><img src="https://agentmods.dev/badge/skills/black-swan-causal-labs/target-mcp/target-checklist-fanout/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/black-swan-causal-labs/target-mcp/target-checklist-fanout"><img src="https://agentmods.dev/badge/skills/black-swan-causal-labs/target-mcp/target-checklist-fanout.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 | $0.00125 | $0.01900 |
| Opus 5 | $0.00063 | $0.00950 |
| Sonnet 5 | $0.00025 | $0.00380 |
| Haiku 4.5 | $0.00013 | $0.00190 |
Grade A, and why
target-checklist-fanout 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 12d 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TARGET checklist — parallel fan-out orchestration
You are the orchestrator. You do not score the paper yourself; you split the 39-leaf checklist across parallel subagents, each scoring a few leaves, then you merge their verdicts and submit them to the MCP server for validation and rendering. This cuts a ~20-minute single pass to ~2 minutes wall-clock.
The whole point is that all MCP calls stay with you (the orchestrator). The subagents are pure scorers: they receive the rubric and manuscript as plain text and return a verdict array. They do NOT need to reach the MCP tools themselves — keep it that way; it is simpler and avoids subagent-reachability questions.
Step 1 — Parse the manuscript (you, one MCP call)
Call parse_manuscript with the manuscript TEXT (or parse_pmcid for an
open-access PMCID). Pass citation= with the paper's full APA reference so it
appears on the render; on the PMCID path it is auto-built. Also pass
supplements= when you have them — TTE methods (estimand, identifying
assumptions) frequently live in a supplementary protocol table.
Keep the returned text_sha256 — you need it to submit.
Step 2 — Get the scaffold prompt (you, one MCP call)
Call assess_manuscript with mode="scaffold". It returns:
system— the rules + the full per-leaf rubric for all applicable leaves,user_content— the manuscript as the server ingested it (the ONLY text evidence quotes may be copied from),leaf_ids— the ordered list of leaves to score (usually all 39).
Do NOT hand-score. Move to the fan-out.
Step 3 — Split the leaves into batches (keep 6x/7x pairs together)
Group leaf_ids into ~11 batches of 3–4 leaves. Rule that matters: keep each
6x specification leaf with its paired 7x emulation leaf (and lettered/roman
siblings) in the SAME batch — judging the emulation of a component is more
coherent when the same subagent just judged its specification. This is the one
dependency in an otherwise-independent checklist.
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.
- 12d ago First seen · 147 lines · 125 tokens per session scan A 77a9996fe27d
target-checklist-fanout is a skill published in the GitHub repository Black-Swan-Causal-Labs/target-mcp (0 stars, last pushed 20d ago), licensed Apache-2.0. It adds 125 tokens to every session and 1,900 once invoked, about $0.0006 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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