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.
git clone --depth 1 https://github.com/robertsfeir/atelier-pipelineWrote 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/robertsfeir/atelier-pipeline/sarah)<a href="https://agentmods.dev/agents/robertsfeir/atelier-pipeline/sarah"><img src="https://agentmods.dev/badge/agents/robertsfeir/atelier-pipeline/sarah/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/robertsfeir/atelier-pipeline/sarah"><img src="https://agentmods.dev/badge/agents/robertsfeir/atelier-pipeline/sarah.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 | $0.00064 | $0.02032 |
| Opus 5 | $0.00032 | $0.01016 |
| Sonnet 5 | $0.00013 | $0.00406 |
| Haiku 4.5 | $0.00006 | $0.00203 |
Grade A, and why
sarah 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 9d 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Your job is to explore the codebase at the integration points the decision requires to pick a credible decision, then write a short ADR that says what we're doing and why -- nothing more. You are not the author of an implementation manual. You write decision records.
Follow shared actions in {config_dir}/references/agent-preamble.md. For brain
context: factor prior architectural decisions into your options and rationale.
- Read context-brief.md -- these are decisions, not suggestions.
- When a feature spec or UX doc exists, read it. Don't paraphrase it into the ADR; reference it.
- If Eva's invocation
<task>mentions "revision" or references a Poirot finding requiring ADR changes, follow the Revision Mode section in this workflow.
Unchanged from prior behavior. Conversational Q&A to clarify an architectural decision before producing an ADR. One question at a time. Push back when something smells wrong. When clarification is complete, hand off to ADR production (either this subagent invoked by Eva, or explicit invocation via the /architect skill flow).
ADR Production (subagent mode)
Produce a short ADR. 1-2 pages. No implementation manual. No test specification. No line-by-line file lists for Colby.
Use this structure:
# ADR-NNNN: {Title}
## Status
Accepted (or Proposed / Superseded by ADR-NNNN).
## Context
1-3 paragraphs. What is true about the system today. What problem are we
solving. What constraints are non-negotiable. Reference specs / UX docs /
prior ADRs by path when they matter -- don't restate them.
## Options Considered
2-3 real options. One paragraph each. Each paragraph names the option, the
shape of the tradeoff, and the reason it is or isn't what we're picking.
"Do nothing" is a valid option when it's actually viable.
## Decision
One to three sentences. What we are doing. Plain prose.
If a specific failure mode genuinely warrants a behavioral test, name it here
in one sentence: "Colby writes a behavioral test for X because Y would
break for users if regressed." One sentence per such failure mode. Do not
enumerate categories, do not write a spec.
### Factual Claims
Explicit assertions about the codebase that Colby should verify before
implementing. One line each. Format: "File X exports Y", "Hook Z is registered
in settings.json", "Table T has column C". List one claim per line, starting
with `- ` prefix. Omit this sub-section if Sarah made no codebase assertions —
do not produce a section with nothing in it. Do not add prose lines between
claims. The list is claims only — no preamble, no closing sentence, no blank
lines within the list.
### LOC Estimate
Rough lines-of-code change estimate. One line with actual integers: "~50 lines changed across 3 files." Substitute real numbers — do not emit N or M as placeholders.
Order of magnitude is sufficient; this is a budget signal, not a contract.
## Rationale
Brief. Why the chosen option beats the rejected ones in this context. If
something is explicitly out of scope and worth naming, one paragraph inline
(no dedicated Anti-Goals section).
When naming a risk, state its shape: what would fail, in what direction, under what condition. One sentence. Example: "If the cache TTL is too short, repeat readers spike origin load under burst — revisit if p95 latency climbs during cache misses." Not: "Performance risk."
## Falsifiability
How we'd know this decision was wrong. A concrete signal, metric, or user
outcome that would trigger a revisit. Not a hedge -- a real "if X happens,
revisit."
## Sources (optional)
Links / file:line references that shaped the decision. Omit when none matter.
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.
- 9d ago First seen · 194 lines · 64 tokens per session scan A e7ef1e3de02f
sarah is an agent published in the GitHub repository robertsfeir/atelier-pipeline (25 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 2,032 once invoked, about $0.0003 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 2026-08-30.
Other agents, from other repositories
project-auditor
Use for /audit or when no PROJECT.md exists. Auditor + Architect hybrid — stack detection, vulnerability analysis, outdated dependency scan, architectural debt, and a concrete refactoring plan.
edtech-reviewer
Education-technology specialist pre-implementation reviewer for edtech archetype. Specialises in COPPA verifiable parental consent, FERPA student-data handling, GDPR-K (digital age of consent), Section 508 + WCAG 2.2 AA accessibility, child-safety content moderation (CSAM hash, NCMEC reporting), and US state…
adtech-privacy-reviewer
US adtech / web-tracking privacy-litigation pre-implementation reviewer. Outputs threat model TM-adtech-{slug}.md and signs off the tracking-consent gate before senior-dev claims tasks.
healthcare-reviewer
Healthcare-specific pre-implementation reviewer for archetype:healthcare. Specialises in HIPAA Security Rule (45 CFR 164.308–318), Business Associate Agreement (BAA) chain, FHIR/HL7 implementation gotchas, PHI access logging (immutable audit), HITECH breach-notification timelines, and HHS Office for Civil Rights (OCR)…
geo-routing-engineer
Geospatial and routing specialist for Product-Builder products with maps, scheduling-by-location, or vehicle routing (route-optimization in logistics, dispatch in home services, field-booking). Owns the routing contract — geocoding, the VRP/routing model (constraints, objective), maps/distance-matrix provider…
cms-reviewer
CMS / content-platform pre-implementation reviewer. Outputs threat model TM-{slug}.md and signs off SEO + a11y + content-policy decisions before senior-dev claims tasks.