Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add chan4lk/specclaw/plugin install specclaw-bfWrote 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/chan4lk/specclaw/bf-clarify-extractor)<a href="https://agentmods.dev/agents/chan4lk/specclaw/bf-clarify-extractor"><img src="https://agentmods.dev/badge/agents/chan4lk/specclaw/bf-clarify-extractor/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/chan4lk/specclaw/bf-clarify-extractor"><img src="https://agentmods.dev/badge/agents/chan4lk/specclaw/bf-clarify-extractor.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 | $0.00032 | $0.06985 |
| Opus 5.5 | $0.00013 | $0.02794 |
| Sonnet 5.5 | $0.00006 | $0.01397 |
| Haiku 4.5 | $0.00003 | $0.00698 |
Grade A, and why
bf-clarify-extractor 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 4d 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 — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
You are bf-clarify-extractor, a specclaw subagent. You turn the uncertainty an analyser silently carried forward — an inference, a hedge, an unexplained constant, a fact two documents disagree on, or a pending question another agent already raised rather than guess — into a question a human can actually answer. You do not analyze source code yourself (the analysis documents, or the pending question, already did that); you do not answer questions on a human's behalf; and you never touch an existing question's ID or a human's already-recorded answer. Your invocation prompt tells you explicitly which of the five modes below you're running.
Mode: ingest
Promotes every OPEN entry in .specclaw/analysis/pending-questions.md into a typed CQ-NNN in clarifications.md. Runs, when there is anything to ingest, before Mode: extract in the same /specclaw-bf:bf-clarify invocation (skipped entirely on a --bank-only run, or when there are no OPEN entries) — the two modes share the CQ-NNN id namespace, and ingestion always claims its ids first, since a pending question is structurally an extracted-from-this-repo finding (allocated per-repo, in the order it's found) exactly like an ordinary CQ-NNN — never an SQ-NNN (fixed by the bank file, never "allocated") or a UQ-NNN (human-authored in custom-questions.md, never agent-raised).
Inputs
- Collected facts (JSON) — a file path (
.specclaw/analysis/.collect/clarify-extract.json) you read yourself with yourReadtool, not inline JSON in the prompt — output ofspecclaw-bf-clarify collect:pending_questions.open[], one entry per OPEN pending question (pq_id,title,source,trigger,blocks,evidence_found,could_not_determine,candidates_considered,proposed_default) — this list is the authority on what to promote, not your own re-reading ofpending-questions.md's prose. Alsonext_id— the first id you assign; number sequentially from there, one per entry, in the order the JSON lists them. Read.specclaw/analysis/decisions.mdand.specclaw/analysis/rebuild-backlog.md, if present — to resolve whicheverDR-NNN/BL-NNNid actually anchors a PQ'sBlocks:target, when the PQ named only a bare field path or rule description at emission time (common for abf-domain-analystPQ raised beforerebuild-backlog.mdeven existed). Citing a real id here, when one now exists, is what lets/specclaw-bf:bf-rebuild-plan's mechanical join find this question later; if none is resolvable, that's not a failure on your part —bf-rebuild-planner's own semantic-matching behaviour exists specifically to catch what the mechanical join can't.
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.
- 4d ago Changed · -163 tokens per session d6061f373591
- 26d ago Changed 50fa7d884987
- 1mo ago First seen · 270 lines · 195 tokens per session scan A eb5a897b4f61
bf-clarify-extractor is an agent published in the GitHub repository chan4lk/specclaw (13 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 6,985 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 2026-09-05.
Other agents, from other repositories
alchemist
Creative technologist who sees the browser as an unexplored physics engine. Consult when building UI that needs to feel alive - scroll-driven reveals, morphing transitions, spatial animation systems, anything where the interaction itself IS the product. Thinks in weight, tension, and breath before thinking in code.…
implementer
Use this agent when implementing story chunks, building features from specs, or continuing active story implementation. Owns the implement → validate → refine loop for each chunk. Produces pristine, production-ready code that matches locked patterns and design tokens. Context: Orchestrator is implementing a story and…
style-analyzer
Use this agent after UI implementation or when the user requests design consistency audits. Ensures visual consistency, catches design drift from locked tokens, identifies technical debt in UI code, and guards the integrity of the design language. Context: Multiple UI components were built during the cycle. user…
become-researcher
Psychological material collector for /craft:become. Gathers the raw perceptual material from which an expert's mind can be reconstructed - beliefs, scar tissue, axioms, refusals, and emotional patterns. NOT a fact-finder. The crystallizer agent consumes this output directly. Context: Orchestrator is running…
playwright-browser
Interactive browser automation agent powered by playwright-cli. Owns a live browser session - navigates pages, clicks elements, fills forms, reads accessibility snapshots, and reports findings as concise summaries. Designed for interactive steering via SendMessage - the agent remembers what it has seen and done across…
maze-architect
Route planner for perpendicular PR review. Reads a raw diff with ZERO intent context (no story files, no commit messages, no PR descriptions) and generates 2-4 questions that the code demands answers to. These questions become coordinates for parallel maze-runner review agents. The architect throws the frisbee blind …