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 jdrhyne/agent-skills --skill knowledge-graphgit clone --depth 1 https://github.com/jdrhyne/agent-skillsWrote 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/jdrhyne/agent-skills/knowledge-graph)<a href="https://agentmods.dev/skills/jdrhyne/agent-skills/knowledge-graph"><img src="https://agentmods.dev/badge/skills/jdrhyne/agent-skills/knowledge-graph/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/jdrhyne/agent-skills/knowledge-graph"><img src="https://agentmods.dev/badge/skills/jdrhyne/agent-skills/knowledge-graph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00037 | $0.00831 |
| Opus 5.5 | $0.00015 | $0.00332 |
| Sonnet 5.5 | $0.00007 | $0.00166 |
| Haiku 4.5 | $0.00004 | $0.00083 |
Grade A, and why
knowledge-graph 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Graph Skill
Maintain a lightweight, append-only entity graph that compounds durable facts across sessions.
When to Use
- Extract durable facts from recent work or conversation history
- Rewrite entity summaries from active facts
- Answer "what do we know about X?" without reopening large transcripts
- Keep shared context for people, companies, and projects inside the workspace
Data Model
Store the graph under:
<workspace>/life/areas/
people/<slug>/
companies/<slug>/
projects/<slug>/
Each entity folder should contain:
summary.mdfor the short, current snapshotfacts.jsonlfor atomic, append-only facts
Use one JSON object per line:
{
"id": "<slug>-NNN",
"fact": "Plain-English fact",
"category": "relationship|milestone|status|preference|context|decision",
"ts": "YYYY-MM-DD",
"source": "conversation|manual|inference",
"status": "active|superseded",
"supersedes": "<older-id>"
}
Fact Rules
- Keep facts atomic. One durable fact per entry.
- Append new facts instead of rewriting history.
- When something changes, add a new fact and mark the old one as superseded.
- Skip ephemera, greetings, speculation, and low-value chatter.
- Check existing facts before adding duplicates.
Durable facts usually include:
- role or relationship changes
- key decisions
- long-lived preferences
- major project milestones
- stable operating context
Workflows
Fact Extraction
- Read the recent daily note and the recent conversation window.
- Identify durable facts worth preserving.
- Resolve entity type and slug.
- Create the entity folder if it does not exist.
- Append new facts to
facts.jsonl. - Note extraction activity in the daily note if the workspace uses one.
Weekly Synthesis
- List entities changed during the week.
- Load active facts only.
- Rewrite
summary.mdin 3 to 8 concise lines. - Ensure contradicted facts are marked superseded.
- Record a short synthesis note in the daily log if applicable.
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.
- 1mo ago First seen · 117 lines · 37 tokens per session scan A 6e6800f7e1ae
knowledge-graph is a skill published in the GitHub repository jdrhyne/agent-skills (240 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 831 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 skills, from other repositories
memory
Two-layer memory system with search-based recall. Use to save important facts, search past events, or recall user preferences and project context across conversations.
wiki-ingest
Use when an operation issue asks to ingest a captured raw/ source into the LLM Wiki, or the user says "ingest ". Create durable source, entity, concept, synthesis, index, and log pages; use paperclip-distill for Paperclip bundles.
agent-v3-memory-specialist
Agent skill for v3-memory-specialist - invoke with $agent-v3-memory-specialist.
hindsight-local
Store user preferences, learnings from tasks, and procedure outcomes. Use to remember what works and recall context before new tasks. (user).
memory-context
Load relevant long-term memory from Hindsight before starting work. Use at the start of every task or request, before planning or answering, and whenever the user mentions people, projects, decisions, preferences or anything done before.
memory-retain
Save what was learned to Hindsight long-term memory. Use when a task finishes, when the user states a fact, decision or preference, and when you discover something a later run or another Bot would need.