Borrowing it
Nothing to install: this file belongs to P1-103n1x/bab-ilu. 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/P1-103n1x/bab-ilu/v2.2-oss-public/.claude/skills/prompt/SKILL.mdgit clone --depth 1 https://github.com/P1-103n1x/bab-iluWrote 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/p1-103n1x/bab-ilu/prompt)<a href="https://agentmods.dev/skills/p1-103n1x/bab-ilu/prompt"><img src="https://agentmods.dev/badge/skills/p1-103n1x/bab-ilu/prompt/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/p1-103n1x/bab-ilu/prompt"><img src="https://agentmods.dev/badge/skills/p1-103n1x/bab-ilu/prompt.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 | $0.00086 | $0.02546 |
| Opus 5 | $0.00043 | $0.01273 |
| Sonnet 5 | $0.00017 | $0.00509 |
| Haiku 4.5 | $0.00009 | $0.00255 |
Grade B, and why
prompt scanned grade B with 1 finding 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
- The output prompt does NOT cite a single signature author — it cites the panel: `[lineage] Warburg panel: [[{panel}]] · AAT: {term} (aat:{id})` How it starts
The opening of the file, as written. The whole thing — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/prompt — Lens-aware deliverable generator
Lens awareness (v2.1)
/prompt is the lens-specific deliverable producer. Each lens declares
what "a useful creative output" means for its domain via
lens.deliverable_types. The same command name produces three very
different artifacts because deliverables are a lens-level value
judgment, not a Bab-ilu-level one.
Runtime wiring (shared by every mode):
- Resolve the active lens via
tools/lens_loader.load_lens(lens_id)— honor--lens <id>when explicitly overridden. - Inject
tools.lens_context.active_lens_preamble()at the top of every LLM-facing stage. The lens'sprompts.mdcarries the tone, rubrics, and traps specific to this deliverable form. - Pull
tier_0/tier_1_atom/tier_1_clusterfromlens.entity_model— the input wikilink shape and its downstream resolution are all tier-typed, not hardcoded to "aesthetic" or "panel". - Emit exactly one deliverable per invocation, shaped by the first
entry of
lens.deliverable_typesunless the user passes--type <name>to select a different one the lens declared.
Deliverable table
| Lens | deliverable_types declared in lens.yaml |
Output shape |
|---|---|---|
aesthetic-warburg |
[aesthetic, panel] (landed) |
aesthetic → four-segment labeled prompt (Subject / Style / Composition / Motion) for UI/image/video sub-modalities; panel → Warburg panel-level prompt |
engineering-alexander |
[pattern-card] (proposed) |
Alexander-style Pattern card: Context → Problem → Forces → Solution → Resulting-context with incident citations as Examples |
general-zettelkasten |
[progressive-summary] (proposed) |
Three-layer Progressive Summary: bold-highlighted key sentence in layer 1, 3–5 bold fragments in layer 2, one-sentence thesis in layer 3 |
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 · 221 lines · 86 tokens per session scan B c2ccdf9d84f4
prompt is a skill published in the GitHub repository P1-103n1x/bab-ilu (11 stars, last pushed 4mo ago), licensed MIT. It adds 86 tokens to every session and 2,546 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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