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 skills/dimosgit/grounded-knowledge-engine/grounded-knowledge-workflownpx skills add dimosgit/grounded-knowledge-engine --skill grounded-knowledge-workflowgit clone --depth 1 https://github.com/dimosgit/grounded-knowledge-engineWhat 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 | $0.00091 | $0.01814 |
| Opus 5 | $0.00046 | $0.00907 |
| Sonnet 5 | $0.00018 | $0.00363 |
| Haiku 4.5 | $0.00009 | $0.00181 |
Grade A, and why
grounded-knowledge-workflow 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grounded Knowledge Workflow
Use the engine as shared memory, not as a replacement for judgment. Keep this skill thin: select the semantic operation and let the MCP server and CLI enforce retrieval, project scope, citations, and writes.
Use the one-call Q&A fast path
- For an ordinary definition, recall, explanation, or comparison, call
kb.answer_and_captureexactly once withresponseMode: autoandresponseFormat: compact, andcaptureStrategy: auto. - Do not call
kb.searchorkb.get_recordbefore it. The answer tool performs its own retrieval and grounding. Automatic retention is read-only. - After a successful call, return the answer, citations, capture status,
tokenUsage, andtimingsimmediately. - No note or review proposal is created by this fast path. Continue into maintenance only when the user explicitly asks for curation or retention.
Choose the operation
- For a named project or “continue where I stopped,” call
kb.resume_projectwith the explicitprojectId. - For an evidence-only search request, call
kb.search. - For one known record, call
kb.get_recordby path, title, slug, or filename. - For a grounded answer that may retain useful context, call
kb.answer_and_capture. - For project creation and administration, use the deterministic
gkeproject CLI rather than inventing an MCP file-management workflow. - When the user explicitly asks to preserve a project handoff or progress
boundary, use
gke checkpoint; never create one merely because a project was viewed or resumed. - When the user explicitly asks to preserve or inspect a durable decision, use
the full-profile decision MCP tools when available. Use the local
gke decisionsCLI for administration or when the MCP server is running the core profile. Local Cockpit preview/apply may append a reviewed change; the public Cockpit remains read-only.
Ground before answering
- Check local knowledge first for questions about the user's documents, projects, prior research, or previous decisions.
- Base factual claims on returned evidence and preserve workspace-relative citations.
- Distinguish sourced facts from inference or recommendations.
- If evidence is insufficient, say what is missing. Do not turn a weak match into certainty.
- Use external research only when the user asks for it or local evidence cannot answer a question that genuinely requires current information. Keep external findings distinct from existing local knowledge.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 176 lines · 91 tokens per session scan A 386f3e72424d
grounded-knowledge-workflow is a skill published in the GitHub repository dimosgit/grounded-knowledge-engine (5 stars, last pushed 5d ago), licensed MIT. It adds 91 tokens to every session and 1,814 once invoked, about $0.0005 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 2026-08-31.
Other skills, from other repositories
seller-research
Use when researching a merchant, storefront, marketplace seller, or merchant of record for a buying decision, especially when identity, refund terms, fulfillment, counterfeit risk, domain history, or independent buyer outcomes are uncertain.
design-mcp-server
Design the tool surface, resources, and service layer for a new MCP server. Use when starting a new server, planning a major feature expansion, or when the user describes a domain/API they want to expose via MCP. Produces a design doc at docs/design.md that drives implementation.
api-context
Canonical reference for the unified Context object passed to every tool and resource handler in @cyanheads/mcp-ts-core. Covers the full interface, its RequestContext base, all sub-APIs (ctx.log, ctx.state, ctx.requestInput, ctx.inputs, ctx.enrich, ctx.content), and when to use each.
api-canvas
DataCanvas primitive reference — a Tier 3 SQL/analytical workspace for tabular MCP servers, backed by DuckDB. Use when registering tables from upstream APIs, running ad-hoc SQL across them, and exporting results. Covers the acquire → register → query → export flow, per-table TTL, the token-sharing pattern for…
api-testing
Testing patterns for MCP tool/resource handlers using createMockContext and Vitest. Covers mock context options, handler testing, McpError assertions, format testing, Vitest config setup, and test isolation conventions.
field-test
Exercise tools, resources, and prompts against a live HTTP server via MCP JSON-RPC over curl. Starts the server, surfaces the catalog, runs real and adversarial inputs, and produces a tight report with concrete findings and numbered follow-up options. Use after adding or modifying definitions, or when the user asks to…