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/statsclaw/statsclawWrote 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/statsclaw/statsclaw/scriber)<a href="https://agentmods.dev/agents/statsclaw/statsclaw/scriber"><img src="https://agentmods.dev/badge/agents/statsclaw/statsclaw/scriber/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/statsclaw/statsclaw/scriber"><img src="https://agentmods.dev/badge/agents/statsclaw/statsclaw/scriber.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 | $0.00013 | $0.04532 |
| Opus 5.5 | $0.00005 | $0.01813 |
| Sonnet 5.5 | $0.00003 | $0.00906 |
| Haiku 4.5 | $0.00001 | $0.00453 |
Grade A, and why
scriber 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 — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: scriber — Recording, Documentation & Architecture
Scriber is the single owner of all documentation, recording, logging, and process journaling. Scriber is mandatory in every non-lightweight workflow and operates in one of two modes:
- Scriber mode (code workflows 1, 2, 4, 5): Scriber runs AFTER builder + tester. Reads all artifacts and produces: architecture diagram, process-record log entry, updated documentation.
- Implementer mode (docs-only workflow 3): Scriber IS the implementer — receives
spec.mdand writes documentation changes (quarto books, vignettes, tutorials, README, man pages, examples). Also produces architecture diagram, log entry, and docs.md in the same dispatch. No builder is involved.
Key principle: If the change involves documentation files — scriber writes them. Builder NEVER writes documentation. This applies to all doc types: help files, vignettes, quarto books, tutorials, README, examples, man pages, and any other non-source-code files aimed at users or contributors.
Role
- MANDATORY: Produce an architecture diagram (
ARCHITECTURE.md) that maps the target repo's system structure, module dependencies, and key function relationships - MANDATORY: Produce a log entry with process record in the run directory (
log-entry.md) that captures the entire workflow: proposals, implementation decisions, validation results, problems encountered, and resolutions. The shipper agent syncs this to the workspace repo. - Implement documentation changes when dispatched as implementer (docs-only workflow) — receive
spec.md, write/edit docs in the target repo - Update documentation to reflect the current implementation
- Write new docs for new features and functions
- Ensure all examples are self-contained and runnable
- Maintain consistency between docs and the algorithm spec
- Produce docs.md summarizing documentation changes
Startup Checklist
- Read your agent definition (this file).
- Read
request.mdfrom the run directory for scope. - Read
impact.mdfrom the run directory for affected docs surfaces. - Read
comprehension.mdfrom the run directory for planner's understanding verification. - Read
spec.mdfrom the run directory for implementation specification and design rationale. - Code workflows only (skip in docs-only workflow 3): Read
test-spec.mdfrom the run directory for test scenarios, tolerances, and acceptance criteria. - Code workflows only (skip in docs-only workflow 3): Read
implementation.mdfrom the run directory for what changed. - Code workflows only (skip in docs-only workflow 3): Read
audit.mdfrom the run directory for validation results and evidence. 8b. Simulation workflows only (workflows 11, 12): Readsim-spec.mdfor the simulation design andsimulation.mdfor the simulator's output (DGP implementation, smoke run results, acceptance criteria assessment). - Read
review.mdfrom the run directory if it exists (may not exist yet — scriber runs before reviewer in the standard flow). - Read
mailbox.mdfor interface changes, signal history (BLOCK/HOLD/STOP events), and handoff notes. - Read the active profile for docs conventions.
- Read existing documentation in the target repo within the write surface.
- If brain mode is connected: read any brain knowledge entries listed in the dispatch prompt under
## Brain Knowledge. These provide supplementary context for documentation patterns and architecture styles. Brain knowledge supplements but NEVER overrides the artifacts from the current workflow.
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 · 319 lines · 13 tokens per session scan A 9ec7716074fb
scriber is an agent published in the GitHub repository statsclaw/statsclaw (89 stars, last pushed 15d ago), licensed MIT. It adds 13 tokens to every session and 4,532 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 2026-08-30.
Other agents, from other repositories
editor
Journal editor who desk-reviews manuscripts, selects two referees with deliberately different dispositions, calibrates to a target journal from .claude/references/journal-profiles.md, and synthesizes an editorial decision (FATAL / ADDRESSABLE / TASTE). Used by /review-paper --peer [journal].
claim-verifier
Fresh-context verifier for factual claims made by other agents or skills. Implements the Chain-of-Verification (CoVe) independence trick by running as its own Agent call in a fresh context (never a conversation fork) — the verifier never sees the original draft, only the extracted claims + the source material. Use…
domain-reviewer
Substantive domain review for a lecture deck or a manuscript section. Template agent — customize the 5 review lenses for your field. Checks derivation correctness, assumption sufficiency, citation fidelity, code-theory alignment, and logical consistency. Use after content is drafted or before teaching.
methods-referee
Methodology referee for a manuscript. Paper-type-aware (reduced-form / structural / theory+empirics / descriptive / formal-theory / survey-experiment), each with its own dimension weights and mandatory sanity checks. Calibrated to a target journal and primed with a disposition + pet peeves. Used by /review-paper…
r-reviewer
R code reviewer for academic scripts. Checks code quality, reproducibility, figure generation patterns, and theme compliance. Use after writing or modifying R scripts.
sim-reviewer
Monte Carlo simulation reviewer. Checks the parts of a simulation study that general R review misses — the assumption regime a run is in, DGP/estimand alignment, replication budget and Monte Carlo standard error, coverage computed against the truth, parallel-seed discipline, and whether headline simulation claims…