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 hindsight-epistemic-classifiergit 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/hindsight-epistemic-classifier)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/hindsight-epistemic-classifier"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/hindsight-epistemic-classifier/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/hindsight-epistemic-classifier"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/hindsight-epistemic-classifier.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 | $0.00160 | $0.01848 |
| Opus 5.5 | $0.00064 | $0.00739 |
| Sonnet 5.5 | $0.00032 | $0.00370 |
| Haiku 4.5 | $0.00016 | $0.00185 |
Grade A, and why
hindsight-epistemic-classifier 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
HINDSIGHT 4-Network Epistemic Classifier
Overview
Production memory systems benefit from distinguishing what the agent observed from what it believes. HINDSIGHT (Latimer et al., 2025, cited in Ch4) organizes memory into four networks:
- World network — objective facts about external reality. Verifiable by external sources (the production region is us-east-1, the CEO of ACME is X, the API endpoint returned 503).
- Experience network — the agent's own first-person actions. "I called the deploy API at 22:30." "I retrieved 5 documents." First-person, timestamped, agent-as-actor.
- Opinion network — subjective beliefs with confidence scores. "I believe the root cause is X with 0.7 confidence." Inference, not observation.
- Observation network — synthesized entity summaries. "Sarah is the on-call lead this week" derived from the union of {Sarah's calendar, on-call rotation doc, prior incidents}. Distillation, not evidence.
Per the HINDSIGHT paper as quoted in Ch4: "developers and users can see what the agent knows versus what it believes." This skill is the classification layer that makes the distinction queryable.
When to Use
- Audit-grade agents — when a user asks "how do you know that," the response must trace evidence to network
- Regulated environments — opinion must be flagged as opinion, not stated as fact
- Multi-agent systems — Agent A's opinion should not become Agent B's fact via uncritical knowledge sharing
- Debugging hallucinations — if the agent stated X confidently, the network classification tells you whether X is evidence-grounded (World/Experience) or inference (Opinion/Observation)
Phrases: "where did the agent get this", "is this fact or inference", "justify the answer", "trust calibration", "HINDSIGHT", "epistemic status".
When NOT to Use
- One-shot agents that need no justification trail
- Storage-only systems (the classification is for retrieval-time reasoning, not just persistence)
- Pure-retrieval agents that never synthesize — the Observation network is empty, the Opinion network is empty; just use World + Experience
What ships with it
2 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 · 141 lines · 160 tokens per session scan A ba55f40b64de
hindsight-epistemic-classifier is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (28 stars, last pushed 3mo ago), licensed MIT. It adds 160 tokens to every session and 1,848 once invoked, about $0.0006 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.
hindsight-epistemic-classifier compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| hindsight-epistemic-classifierAnthonyAlcaraz/agentic-graph-rag-skills | Skill | 28 | 3mo ago | MIT | A | 160 | 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 |