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/agusloza2021/thoughtline/memorynpx skills add AgusLoza2021/Thoughtline --skill memorygit clone --depth 1 https://github.com/AgusLoza2021/ThoughtlineWhat 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 | $0.00040 | $0.01191 |
| Opus 5 | $0.00020 | $0.00596 |
| Sonnet 5 | $0.00008 | $0.00238 |
| Haiku 4.5 | $0.00004 | $0.00119 |
Grade A, and why
thoughtline-memory 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 2d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 2d ago First seen · 131 lines · 40 tokens per session scan A 16b616f50961
thoughtline-memory is a skill published in the GitHub repository AgusLoza2021/Thoughtline (4 stars, last pushed 3mo ago), with no licence file. It adds 40 tokens to every session and 1,191 once invoked, about $0.0002 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
add-memory-system
Research and integrate a new agent-memory repository into Agent Memory Atlas. Use when asked to add, analyze, catalog, or compare a memory system in this repository, including its commit-pinned system report, comparative overview coverage, homepage card, generated site, and validation.
reanalyze-memory-system
Re-read a repository Agent Memory Atlas already has a report for, at a newer commit, and fold what changed into the existing report. Use when asked to re-analyze, re-read, re-pin, refresh, or check for updates on a system already in the atlas, when an upstream project reports a fix, or when a freshness check flags a…
remove-meta-narrative
Detect and remove narration of the project's own history from published pages, so a reader gets the current state instead of a draft's changelog. Use before publishing or committing any edit to content/ or site/, when correcting a claim that turned out wrong, when folding a new finding into an existing page, and…
use-the-atlas
Design, review, or build a memory system for some other product, using the Agent Memory Atlas as the reference. Use when asked to add memory, persistence, or recall to a repository, to review an existing memory implementation against the atlas, or to decide which memory patterns a product actually needs. Runs in one…
screen-repository
Screen an untrusted checkout for auto-executing hooks and unpinned dependencies before reading or running anything in it. Required first step of add-memory-system and reanalyze-memory-system. Use whenever a repository is cloned onto this machine for analysis.
Effective Memory
The essential habits for an AI agent with memory — session bookends, learning triggers, verification, safety, and the operational discipline that turns raw recall into compounding intelligence. Pinned, always-injected.