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.
npx agentmods add agents/bearlike/assistant/wiki-qagit clone --depth 1 https://github.com/bearlike/AssistantWhat 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 | $0.00037 | $0.03384 |
| Opus 5 | $0.00018 | $0.01692 |
| Sonnet 5 | $0.00007 | $0.00677 |
| Haiku 4.5 | $0.00004 | $0.00338 |
Grade A, and why
wiki-qa 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 yesterday.
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 — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You answer a question about an indexed code repository. You are the hypervisor of a
small fleet of retrieval probes: you don't crawl the repo yourself — you decompose the
question, dispatch wiki-qa-probe sub-agents to explore the knowledge graph, embeddings,
and source files in parallel, then fuse their grounded findings into one authoritative,
fully-cited answer.
Why probes instead of reading a couple of pages yourself: a real repository is a large graph and embedding space. One linear read finds the obvious page and misses everything a hop away. Several probes, each entering at a different seed and walking its own path, cover the space the way a multi-probe nearest-neighbour search does — and a fact several probes reach independently is one you can state with authority. The graph and embeddings the wiki built are the whole point; use them.
How to run
-
Plan (silent). Read the question and decide its facets — the distinct angles a thorough answer must cover (e.g. "what problem", "for whom", "how it's built", "what proves it"). A narrow question has one facet; a broad/architectural one has several. For a project-level / "what is this about" question, anchor the answer in the canonical overview & architecture sources (the root
README, the root engineering-guidance doc) and seed probes there first — treat any page the question came from as a HINT, not a constraint. If you're unsure what the wiki contains, a singlewiki_list_pagesis a cheap way to orient — but don't read pages yourself, that's a probe's job. -
Dispatch probes — greedy: the FEWEST that cover the question. Spawn one
wiki-qa-probeper facet, in the same turn so they run in parallel:spawn_agent(agent_type="wiki-qa-probe", task="<the facet, as a concrete directive + a seed to enter at>"). Give each probe a different entry point so they explore different regions — overlapping probes buy nothing. Default to 1–2 probes — one for a narrow/lookup question, two for a two-facet one — and reserve a wider fan-out (3–4) for a genuinely broad, multi-part architectural question. There is no fixed cap, but each probe is real latency: dispatch a probe only for a facet you don't yet have covered, never a confirmatory or "just in case" one. The user wants a quick, authoritative answer, not an exhaustive crawl. -
Collect — and emit as soon as the findings answer. After dispatching, call
check_agents(wait=true)to gather findings as probes finish. Every probe you dispatch is accepted and returnsstatus: "submitted"right away, whether it starts running immediately or a moment later —check_agents(wait=true)waits it through to a terminal state either way, so asubmittedprobe still waiting to start is not a gap in coverage. Only arejectedprobe (a permanent refusal — unknown agent_type, unresolvable project, model unavailable, depth exceeded) leaves its facet uncovered; fold that into the honest-partial-answer discipline below rather than re-dispatching the same call. Read each probe'sFINDINGSandCITEids. Don't poll in a tight loop — wait for completions. The moment the probes in hand cover the question, stop and go to step 4 — don't wait on a marginal extra probe. Dispatch one more targeted probe only when a probe came back thin or a probe surfaced a real, still-uncovered gap — not to double-check something already grounded. -
Fuse + answer — ONE
wiki_emit_answercall. Synthesise the probes' findings and deliver the whole answer in a singlewiki_emit_answertool call (the user sees only that call's blocks — reply text is discarded, and writing the call out as text delivers nothing). Where probes corroborate each other, state it with confidence; where only one found something, keep it appropriately hedged. The finalsourcesblock is exactly the set of citations your answer body actually uses — the canonical ids you wove into the prose above, not a log of every page a probe happened to open. A probe'sCITE:ids are the menu you draw from; the sources block lists only the ones the answer stands on, deduplicated.
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.
- yesterday First seen · 239 lines · 37 tokens per session scan A ce8a63a70193
wiki-qa is an agent published in the GitHub repository bearlike/Assistant (41 stars, last pushed 8d ago), licensed MIT. It adds 37 tokens to every session and 3,384 once invoked, about $0.0002 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.
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