Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/intentdriven/abcdnpx agentmods add agents/intentdriven/abcd/embark-scaffolderWrote 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/agents/intentdriven/abcd/embark-scaffolder)<a href="https://agentmods.dev/agents/intentdriven/abcd/embark-scaffolder"><img src="https://agentmods.dev/badge/agents/intentdriven/abcd/embark-scaffolder/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/agents/intentdriven/abcd/embark-scaffolder"><img src="https://agentmods.dev/badge/agents/intentdriven/abcd/embark-scaffolder.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 | $0.00042 | $0.03795 |
| Opus 5.5 | $0.00017 | $0.01518 |
| Sonnet 5.5 | $0.00008 | $0.00759 |
| Haiku 4.5 | $0.00004 | $0.00380 |
Grade A, and why
prompting-research-embark-scaffolder 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompting SOTA — embark-scaffolder
Scope of this file. Agent-specific deltas only. Every general principle (Goldilocks structure, few-shot discipline, semantic versioning, OWASP LLM01) is in
../01-general-best-practices.md— do not duplicate it here. Cite the baseline by section number when relevant.Role. Research is the gate, not the source. Author writes the agent's prompt informed by this file;
lifeboat-oracleaudits alignment.Why this agent second. Per baseline § 7:
embark-scaffolderis the highest prompt-injection risk in the suite — it consumes a lifeboat that may have been authored by an attacker (someone hands you "a lifeboat" as a Trojan). Pairs naturally withchat-distiller(highest context-rot risk) — between them the template covers both major SOTA failure axes.
0. Agent at a glance
- One-line job. Given a lifeboat directory and a target repo's probe state, emit a JSON scaffold plan that places the lifeboat's principles, specs, ADRs, memory entries, and assets into canonical target locations — flagging conflicts and any divergence from the (user-confirmed) press release.
- Pass / lifecycle role.
embark(single-pass; runs after the press-release interview confirms the lifeboat's intent). - Inputs.
- Lifeboat directory:
README.md,press-release.md(the amended, user-confirmed version — hard input),principles.md,rescue/specs/,docs/adrs/,assets/_manifest.json,_provenance.json - Target probe state: emptiness check, existing-file inventory, conflict points
- Optional:
--refresh-auditflag → fresh oracle audit findings to compare vs disembark-time audit
- Lifeboat directory:
- Outputs.
scaffold-plan.json— list of(action, source, dest, conflict_resolution_required, rationale)tuples; deterministic Python applies the plan after user confirmationembark-report.{json,md}contributions: principle-injection mapping, conflict summary, press-release-divergence flags, audit drift (if refresh)
- Tools (read/write boundary). Read, Glob, Grep — read-only. NO Edit, Write, NotebookEdit, Bash. The agent emits a plan as structured output; deterministic Python (
scripts/abcd/embark.py) actually creates files. This separation is load-bearing: the agent never holds a tool that an injection could weaponise into a write. - Model.
inherit(default). Reasoning quality matters but the output is structured JSON, not free-form prose; Sonnet-class is sufficient. Pin to Opus only if conflict-resolution judgement regresses. - Expected token order-of-magnitude per invocation. Input: 15–40k (full lifeboat content + target probe). Output: 2–5k (JSON plan + rationale). Within Anthropic's 1k-2k sub-agent budget for summary content; this agent's output is structurally larger because it's a plan, not a summary — accept the higher budget.
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 · 116 lines · 42 tokens per session scan F 4b72344336cb
prompting-research-embark-scaffolder is an agent published in the GitHub repository intentdriven/abcd (3 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 3,795 once invoked, about $0.0002 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 2026-08-31.
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