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.
git 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/commands/agusloza2021/thoughtline/tl-export)<a href="https://agentmods.dev/commands/agusloza2021/thoughtline/tl-export"><img src="https://agentmods.dev/badge/commands/agusloza2021/thoughtline/tl-export/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/commands/agusloza2021/thoughtline/tl-export"><img src="https://agentmods.dev/badge/commands/agusloza2021/thoughtline/tl-export.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 | $0.00015 | $0.00205 |
| Opus 5.5 | $0.00006 | $0.00082 |
| Sonnet 5.5 | $0.00003 | $0.00041 |
| Haiku 4.5 | $0.00002 | $0.00020 |
Grade A, and why
tl-export 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.
What it actually says
Build a markdown export of the active project's thoughtline memories.
Steps:
- Call
tl_contextwith a highRecentLimit(e.g. 200) to grab everything recent. - For each memory, call
tl_get_observationto retrieve full content. - Group by
type(decisions, conventions, architecture, bugfixes, …). - Write to
$ARGUMENTSif provided, otherwise default toTHOUGHTLINE-EXPORT.mdin the repo root.
Each entry's section should be:
## <title>
- **type**: <type>
- **topic_key**: <topic_key or "—">
- **updated**: <ISO date>
- **scope**: <project|personal>
<full content>
Confirm with the user before overwriting an existing file.
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.
- 18d ago Changed · +6 lines 9980912e9ed8
- 1mo ago First seen · 29 lines · 15 tokens per session scan A 15a541ddfabb
tl-export is a command published in the GitHub repository AgusLoza2021/Thoughtline (4 stars, last pushed today), licensed MIT. It adds 15 tokens to every session and 205 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 2026-08-31.
Other commands, from other repositories
pane
Open the Engram graph pane — make sure the machine core is running and share the URL.
digest
Digest this project into Engram — an explicit, one-time ingestion of the current working tree into typed memory nodes.
load
Load the shared Wingman context for this project.
tour
Tour this project's memory — what's captured, where it lives, how to recall it (core-memory-kit).
remember
Save something to long-term local memory.
recall
Search long-term local memory and answer from it.