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 skills add chrono-meta/forge-harness --skill corpus-grounding-expandergit clone --depth 1 https://github.com/chrono-meta/forge-harnessWrote 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/chrono-meta/forge-harness/corpus-grounding-expander)<a href="https://agentmods.dev/skills/chrono-meta/forge-harness/corpus-grounding-expander"><img src="https://agentmods.dev/badge/skills/chrono-meta/forge-harness/corpus-grounding-expander/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/chrono-meta/forge-harness/corpus-grounding-expander"><img src="https://agentmods.dev/badge/skills/chrono-meta/forge-harness/corpus-grounding-expander.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 | $0.00040 | $0.01274 |
| Opus 5 | $0.00020 | $0.00637 |
| Sonnet 5 | $0.00008 | $0.00255 |
| Haiku 4.5 | $0.00004 | $0.00127 |
Grade A, and why
corpus-grounding-expander 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 10d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
corpus-grounding-expander
Builds or broadens the grounded axiom a relay system quotes from. When a system's output must be constrained to verbatim source (fail-closed grounding), the corpus IS the axiom — this skill expands that corpus from multiple public-domain versions so grounding is robust and non-biased (a quote verified in ANY version counts), without ever adding a generator.
Origin: harvested from the-bible (2026-06-20) — 6 public-domain Bible versions, 197k verses, as the fail-closed grounding axiom. The grounds, inline, because the harvest record does not ship: one edition's wording makes a fail-closed check reject a faithful quote that followed a different edition, so the union across versions is what stops fail-closed grounding from failing closed against the user — and it keeps one translation's editorial choices from silently becoming the axiom. Generalizes to any verbatim-relay corpus (legal statute, RFC text, standards). Full harvest record — hub-local, not distributed in the npm package:
tracks/_contrib/field_harvest_2026-06-20_gate-locality-and-grounding-capabilities.md.
Triggers
- "get more sources" / "broaden the grounded corpus"
- "add another version of the corpus"
- "ingest the full as the grounding axiom"
/corpus-grounding-expander {source or version}
Natural Language Triggers (example user phrases)
- "이 코퍼스 여러 버전으로 통째로 가져와줘"
- "the grounding DB is just a sample — pull the whole thing, multiple editions"
- "add a second public-domain edition so we're not biased to one"
Steps
- Source + license check — identify the public-domain source(s) and confirm each is genuinely free to redistribute. Record the license literally (no assumption).
- Fetch (retry-disciplined) — pull each version; on transient error, backoff-retry before declaring the source unavailable (do not silently drop a version).
- Normalize to a single key schema — map every version to the SAME addressable key
(e.g.
ref → text) so cross-version quotation stays aligned. Write per-version files + an_indexrecording version id, license, scope, and record count. - Wire grounding as a union — the grounding check passes if the quote matches the canonical text at that key in ANY version. Never add a path that generates text — grounding is quote-only.
- Relay-integrity check — confirm the consumer (the gate) QUOTES the corpus and cannot emit
un-grounded text; run a fabrication probe (a known non-source quote must fail-closed) and write
the probe's actual output into the index under
probe_status:. Writing the probe spec without running it isprobe_status: not_run, not a pass.
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.
- 10d ago First seen · 66 lines · 40 tokens per session scan A c29f147d2ccf
corpus-grounding-expander is a skill published in the GitHub repository chrono-meta/forge-harness (14 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 1,274 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.
Other skills, from other repositories
ingest-collection
Rebuild a knowledge store from a paper collection by running paper-process per paper, then doing one final build and optional stance backfill.
knowject-rag-eval
A skill for creating or reviewing evaluation cases for RAG, a system that searches stored documents before generating an answer, including the quality of its citations.
unified-llm-api
Call model APIs through @prismshadow/agenthub — streaming text generation, image generation, speech synthesis, embeddings and the supported-model registry with one client.
graph-retrieval
Exposes graph-based retrieval as a tool capability via querygraph. Reads normalized graph store files, builds a query-relevant subgraph, and returns LLM-friendly semantic triples with replayable evidence metadata.
knowledge-layer
High-level deployment wrapper over RepoBrain core with graph-first knowledge injection and all-file support. Exposes refreshfilesystem and askfilesystem for building and querying the knowledge graph.
spring-ai-integration
Use when integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI ChatClient, prompt templates, embeddings, vector stores, and structured output. Use when user mentions Spring AI, LLM, ChatGPT, Claude, RAG, embeddings.