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 AgusLoza2021/Thoughtline --skill memorygit clone --depth 1 https://github.com/AgusLoza2021/ThoughtlineWrote 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/agusloza2021/thoughtline/memory)<a href="https://agentmods.dev/skills/agusloza2021/thoughtline/memory"><img src="https://agentmods.dev/badge/skills/agusloza2021/thoughtline/memory/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/agusloza2021/thoughtline/memory"><img src="https://agentmods.dev/badge/skills/agusloza2021/thoughtline/memory.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 | $0.00040 | $0.01191 |
| Opus 5.5 | $0.00016 | $0.00476 |
| Sonnet 5.5 | $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 16d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Thoughtline Memory Protocol
Thoughtline is a local-first SQLite-backed memory system. It survives across sessions and compactions. This protocol is MANDATORY and ALWAYS ACTIVE when the plugin is installed.
Tools (namespace tl)
Core (always available, no ToolSearch needed):
tl_save— persist a memorytl_search— keyword + GLOB search (FTS5 + BM25)tl_context— recent activity for the active projecttl_get_observation— full untruncated content of one memorytl_session_start— open a sessiontl_session_summary— close a session with a structured digest
Via ToolSearch: tl_update, tl_delete, tl_stats.
Proactive Save — do NOT wait for the user to ask
Call tl_save IMMEDIATELY after any of:
- Architecture or design decision made
- Team convention documented or established
- Tool / library choice made with tradeoffs
- Bug fixed (include root cause)
- Feature implemented with a non-obvious approach
- Configuration change or environment setup done
- Non-obvious discovery, gotcha, or edge case
- Pattern established (naming, structure, convention)
- User preference or constraint learned
- User confirms or rejects an approach
Self-check after every task: "Did I or the user decide, confirm, prefer, fix, learn, or establish a convention? If yes → tl_save NOW."
Recommended content structure
**What**: [one sentence — what was done or decided]
**Why**: [the reason — user request, bug, perf, etc.]
**Where**: [files or paths affected]
**Learned**: [gotchas, edge cases, surprises — omit if none]
Topic key convention
category/subject or category/area/subject. Re-saving the same topic_key upserts (same sync_id, bumped revision_count) — no duplicates.
| Memory type | Topic key pattern |
|---|---|
decision |
decision/<area>/<choice> |
architecture |
architecture/<area> |
bugfix |
bug/<area> (or omit for one-off bugs) |
convention |
convention/<area> |
preference |
preference/<area> |
game-design-decision |
design/<system>/<choice> |
scene-pattern |
scene/<engine>/<pattern> |
asset-reference |
asset/<category>/<name> |
perf-gotcha |
perf/<platform>/<area> |
pipeline-step |
pipeline/<source>-to-<target>/<asset> |
script-pattern |
script/<engine>/<concept> |
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.
- 17d ago Changed · +6 lines f0a56518c2d1
- 1mo 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 17d ago), licensed MIT. It adds 40 tokens to every session and 1,191 once invoked, about $0.0002 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.
Other skills, from other repositories
plur-create-engrams
Create or improve PLUR engrams from conversations, documents, decisions, observations, and explicit preferences. Use for memory extraction, engram authoring, or reviewing proposed memories, including global, scoped, pinned, retrieved, and provisional knowledge. Ordinary use of existing memories does not require this…
recall
Recall this repository's OwnMem local memory before changing code, and keep it healthy. Use when a repository contains .ownmem/, when past debugging lessons could apply ("have we hit this before", "why is it done this way"), or when the user mentions ownmem, project memory, or recalling across sessions.
plur-memory
Persistent learning for AI agents. Open engram format. Your agent learns from corrections, remembers across sessions, and transfers knowledge across domains.
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. Keyword-matched after installation.
init
Install or update OwnMem in the current repository. Use when the user asks to set up OwnMem, add local project memory for coding agents, or refresh an existing OwnMem installation after a version bump.
plur-session-end
Extract durable learnings at the end of a session. Saves corrections, preferences, and codebase patterns as engrams — nothing ephemeral, nothing sensitive.