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 AnthonyAlcaraz/agentic-graph-rag-skills --skill bi-temporal-edgegit clone --depth 1 https://github.com/AnthonyAlcaraz/agentic-graph-rag-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/anthonyalcaraz/agentic-graph-rag-skills/bi-temporal-edge)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/bi-temporal-edge"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/bi-temporal-edge/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/anthonyalcaraz/agentic-graph-rag-skills/bi-temporal-edge"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/bi-temporal-edge.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 | $0.00193 | $0.02755 |
| Opus 5.5 | $0.00077 | $0.01102 |
| Sonnet 5.5 | $0.00039 | $0.00551 |
| Haiku 4.5 | $0.00019 | $0.00276 |
Grade A, and why
bi-temporal-edge 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 2mo 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bi-Temporal Edge
Overview
Agent memory that overwrites facts in place loses the historical trace that
makes incident reconstruction possible. The DevOps running example from
Ch5/Ch6 makes this concrete: the checkout API had its EC2 instance type
changed from t3.large to m5.xlarge on 2026-03-10. The outage occurred
2026-03-15T08:00Z. By the time the post-mortem starts, the configuration
store says m5.xlarge — but the agent investigating the outage needs to
know what the config was at the time of the outage, not what it is now.
The bi-temporal edge tracks two times per relationship:
- Validity time (
valid_from,valid_until): when the fact was true in the domain.valid_until=Nonemeans currently valid. - Ingestion time (
ingested_at): when the system learned about the fact. May be days or weeks after the fact became true.
These two dimensions are independent. A fact can be valid-but-not-yet-known (staging update pushed to prod 2026-03-10 but only logged 2026-03-12), or known-but-no-longer-valid (we recorded it 2026-03-10, invalidated 2026-03-15 when the rollback happened). Both are common in production.
Once edges carry both timestamps, three new query primitives become
mechanical: was_valid_at(timestamp) answers point-in-time, history(node)
answers full-evolution, ingestion_lag(edge) answers debugging-the-debugger
("was our agent acting on stale data when it made that decision?").
The chapter pairs this with HINDSIGHT's typed-link extension: each edge
carries a link_type ({entity, semantic, temporal, causal}) and a weight
multiplier for graph traversal. During spreading-activation search, causal
and entity links get μ > 1; weak semantic or long-range temporal links get
μ ≤ 1. This biases the agent's reasoning toward explanatory connections.
When to Use
Trigger contexts:
- DevOps incident reconstruction — what was the config at outage time?
- Audit-grade question — "What did the agent know on 2026-03-15 when it recommended X?"
- Regulated environment — compliance evidence needs reproducible point-in- time queries.
- Multi-agent memory where Agent A wrote a fact, Agent B needs to know whether the fact was valid when Agent A wrote it.
What ships with it
3 files 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.
- 2mo ago First seen · 171 lines · 193 tokens per session scan A bfe6183e9b56
bi-temporal-edge is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (28 stars, last pushed 3mo ago), licensed MIT. It adds 193 tokens to every session and 2,755 once invoked, about $0.0008 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 2026-08-31.
bi-temporal-edge compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| bi-temporal-edgeAnthonyAlcaraz/agentic-graph-rag-skills | Skill | 28 | 3mo ago | MIT | A | 193 | Claude Code, Codex |
| lemmalogJordyZomer/lemmalog | Skill | 346 | 1mo ago | MIT | A | 105 | Claude Code, Codex |
| brain-ai-memoryHahyun-Lee/brain-ai-memory | Skill | 5 | 18d ago | MIT | A | 39 | Codex |
| graph-askstevepridemore/graph-memory | Skill | 2 | 3d ago | MIT | A | 43 | Claude Code |
| ingest-audiostevepridemore/graph-memory | Skill | 2 | 3d ago | MIT | A | 56 | Claude Code |
| ingeststevepridemore/graph-memory | Skill | 2 | 3d ago | MIT | A | 62 | Claude Code |
| graph-backupstevepridemore/graph-memory | Skill | 2 | 3d ago | MIT | A | 25 | Claude Code, Codex |