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 TheGoat395/Codex-Skills --skill agent-memory-provenancegit clone --depth 1 https://github.com/TheGoat395/Codex-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/thegoat395/codex-skills/agent-memory-provenance)<a href="https://agentmods.dev/skills/thegoat395/codex-skills/agent-memory-provenance"><img src="https://agentmods.dev/badge/skills/thegoat395/codex-skills/agent-memory-provenance/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/thegoat395/codex-skills/agent-memory-provenance"><img src="https://agentmods.dev/badge/skills/thegoat395/codex-skills/agent-memory-provenance.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 | $0.00013 | $0.00451 |
| Opus 5.5 | $0.00005 | $0.00180 |
| Sonnet 5.5 | $0.00003 | $0.00090 |
| Haiku 4.5 | $0.00001 | $0.00045 |
Grade A, and why
agent-memory-provenance 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Memory & Provenance
Separate memory types
Keep these distinct:
- run state: transient tool and workflow state;
- session history: bounded conversational continuity;
- evidence store: source documents, extracts, timestamps, and access status;
- decisions: approved choices, assumptions, owner, and reconsideration condition;
- retrieval index: derived search aid, never the sole record of truth.
Do not treat an LLM summary as evidence or a vector result as permission to act.
Design the record
For each retained item, define tenant/owner, source URI or file, capture time, fact-versus-inference label, allowed readers, retention period, deletion path, and sensitivity. Preserve originals when possible; make derived summaries reversible by linking them to their source records.
Use append-only non-sensitive event references for auditability; sensitive payloads need their own authorized deletion and retention path. Existing authorization governs storage; a memory design task does not authorize adding personal durable memories. Compact only with an explicit summary of what was retained, omitted, and still retrievable. Never place passwords, tokens, private keys, cookies, recovery codes, or unrelated client data in agent memory.
Retrieval and isolation
- Retrieve only records relevant to the active task and permitted tenant.
- Filter by source authority, freshness, locale, and client before semantic similarity.
- Treat stale or conflicting retrieval as a question to resolve, not silent context.
- Keep client credentials, documents, logs, and decisions isolated by default.
Verify and deliver
Test the changed behavior and material failure boundaries, selecting from cross-session recall, source citation, conflicting sources, sensitive-record exclusion, retention and tenant isolation. Deliver the requested result; select a memory map, data dictionary, retention matrix or evidence receipt only when needed for that result. Use $agent-orchestration-architecture for runtime ownership and $research-provenance-archive for durable research capture.
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 Changed · +4 lines · -39 tokens per session 786a920e45ec
- 1mo ago First seen · 36 lines · 52 tokens per session scan A cebceea54b3e
agent-memory-provenance is a skill published in the GitHub repository TheGoat395/Codex-Skills (134 stars, last pushed 6d ago), licensed MIT. It adds 13 tokens to every session and 451 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
inspector-workbench
Runs Inspector UI work on the standalone Threads state lab so the agent can see the overlay. Use when a CopilotKit employee asks an agent to fix, polish, add, or debug Inspector UI, chrome, panes, overlay actions, launcher, Home, Threads, Playground, Memory, or any visual behavior in @copilotkit/web-inspector. Don't…
common-context-optimization
Manage context and prompt-cache costs without losing task state. Use for context management, orchestration cost, token budgets, large tool outputs, cache misses, or long-running conversations.
agent-wiki
Incremental LLM-friendly wiki generator for Obsidian note vaults. Use when: (1) Building wiki from notes, (2) Ingesting notes to wiki, (3) Obsidian LLM wiki, (4) Incremental knowledge base management. Triggers: 'build wiki from notes', 'ingest notes to wiki', 'Obsidian LLM wiki', 'incremental knowledge base'.
capability-evolver
Self-evolution workflow for the agent. Before substantive work, recall past outcomes from evolution memory; while editing, detect improvement signals; at task end, record the outcome; when reusable, distill or search the EvoMap network for proven genes/capsules. Use when the user starts non-trivial work (a feature, a…
context-optimizer
A context-management assistant for long conversations with AI agents. It helps organize the information an agent needs when conversations become large or contain conflicting details.
dpf-elicit-tacit-knowledge
Use when durable DPF knowledge lives only in a human's head — a decision rationale, the why behind a choice, a profession technique, domain context a build depends on.