Memori Long-Term Memory
MemoriLabs/Memori
Adds structured long-term memory to OpenClaw agents, built automatically from sessions, with tools the agent calls to recall facts, summaries and decisions.
Analyze the token length of an OT-Agent conversation-format (ShareGPT-style) dataset — the per-trace distribution (median/p90/max) and/or counts under a token threshold + a metadata predicate (e.g.
Install Analyze Dataset Token Length
$ npx skills add open-thoughts/OpenThoughts-Agent --skill analyze-dataset-token-length -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install open-thoughts/OpenThoughts-Agent analyze-dataset-token-length --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/open-thoughts/OpenThoughts-Agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/analyze-dataset-token-length .claude/skills/analyze-dataset-token-length && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "analyze-dataset-token-length" agent skill from https://github.com/open-thoughts/OpenThoughts-Agent/tree/main/.agents/skills/analyze-dataset-token-length into .claude/skills/analyze-dataset-token-length/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-dataset-token-length", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/open-thoughts/OpenThoughts-Agent/tree/main/.agents/skills/analyze-dataset-token-lengthType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add open-thoughts/OpenThoughts-Agent --skill analyze-dataset-token-length -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install open-thoughts/OpenThoughts-Agent analyze-dataset-token-length --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-thoughts/OpenThoughts-Agent.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/analyze-dataset-token-length .agents/skills/analyze-dataset-token-length && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "analyze-dataset-token-length" agent skill from https://github.com/open-thoughts/OpenThoughts-Agent/tree/main/.agents/skills/analyze-dataset-token-length into .agents/skills/analyze-dataset-token-length/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-dataset-token-length", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add open-thoughts/OpenThoughts-Agent --skill analyze-dataset-token-length -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install open-thoughts/OpenThoughts-Agent analyze-dataset-token-length --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-thoughts/OpenThoughts-Agent.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/analyze-dataset-token-length .cursor/skills/analyze-dataset-token-length && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "analyze-dataset-token-length" agent skill from https://github.com/open-thoughts/OpenThoughts-Agent/tree/main/.agents/skills/analyze-dataset-token-length into .cursor/skills/analyze-dataset-token-length/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-dataset-token-length", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/open-thoughts/OpenThoughts-Agent.git --path .agents/skills/analyze-dataset-token-length--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add open-thoughts/OpenThoughts-Agent --skill analyze-dataset-token-length -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install open-thoughts/OpenThoughts-Agent analyze-dataset-token-length --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-thoughts/OpenThoughts-Agent.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/analyze-dataset-token-length .gemini/skills/analyze-dataset-token-length && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "analyze-dataset-token-length" agent skill from https://github.com/open-thoughts/OpenThoughts-Agent/tree/main/.agents/skills/analyze-dataset-token-length into .gemini/skills/analyze-dataset-token-length/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-dataset-token-length", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install open-thoughts/OpenThoughts-Agent analyze-dataset-token-lengthInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add open-thoughts/OpenThoughts-Agent --skill analyze-dataset-token-length -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/open-thoughts/OpenThoughts-Agent.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/analyze-dataset-token-length .github/skills/analyze-dataset-token-length && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "analyze-dataset-token-length" agent skill from https://github.com/open-thoughts/OpenThoughts-Agent/tree/main/.agents/skills/analyze-dataset-token-length into .github/skills/analyze-dataset-token-length/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-dataset-token-length", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add open-thoughts/OpenThoughts-Agent --skill analyze-dataset-token-length -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install open-thoughts/OpenThoughts-Agent analyze-dataset-token-length --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-thoughts/OpenThoughts-Agent.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/analyze-dataset-token-length .opencode/skills/analyze-dataset-token-length && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "analyze-dataset-token-length" agent skill from https://github.com/open-thoughts/OpenThoughts-Agent/tree/main/.agents/skills/analyze-dataset-token-length into .opencode/skills/analyze-dataset-token-length/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-dataset-token-length", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
analyze-dataset-token-length- GitHub stars
- 301
- Token cost
- ~1.5k tokens
- SKILL.md length
- 560 words
- Files
- 1
- Skills in repo
- 44
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Analyze the token length of an OT-Agent conversation-format (ShareGPT-style) dataset — the per-trace distribution (median/p90/max) and/or counts under a token threshold + a metadata predicate (e.g.
- Asked how long traces are
- SKILL.md covers The canonical OT-Agent tools…, Tokenizer convention, Three token-count… and Threshold + metadata…, plus 2 more sections
- Calls python
- How many fit a context window (32k/131k)
What it does
Analyze Dataset Token Length is an agent skill from open-thoughts/OpenThoughts-Agent. Analyze the token length of an OT-Agent conversation-format (ShareGPT-style) dataset — the per-trace distribution (median/p90/max) and/or counts under a token threshold + a metadata predicate (e.g. "taskcomplete AND < 32768 tokens"). Use when asked how long traces are, how many fit a context window (32k/131k), or to filter a trace dataset by length + a field. Uses the OT-Agent analysis tools + the Qwen3-8B tokenizer. Runs LOCALLY on the Mac (no GPU); full-dataset tokenization of ~10k multi-turn traces takes a few…
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Agent Workflows, covering Natural language processing and Context engineering. It works with Qwen. The repository describes itself as: Data recipes and robust infrastructure for training AI agents. The licence is Apache-2.0.
When your agent uses it
- Asked how long traces are
- How many fit a context window (32k/131k)
- Filter a trace dataset by length + a field
Example prompts
- “taskcomplete AND < 32768 tokens”
- “/analyze-dataset-token-length”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 3bd1917. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Analyze Dataset Token Length loads about 1.5k tokens when it runs. Until then it costs about 146 tokens; SKILL.md has 560 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from open-thoughts/OpenThoughts-Agent at commit 3bd1917, republished under its Apache-2.0 licence (© open-thoughts). 560 words, ~1,467 tokens.
Download SKILL.mdSave it as
.claude/skills/analyze-dataset-token-length/SKILL.md (or your agent's skills folder).- name
- analyze-dataset-token-length
- description
- Analyze the token length of an OT-Agent conversation-format (ShareGPT-style) dataset — the per-trace distribution (median/p90/max) and/or counts under a token threshold + a metadata predicate (e.g. "task_complete AND < 32768 tokens"). Use when asked how long traces are, how many fit a context window (32k/131k), or to filter a trace dataset by length + a field. Uses the OT-Agent analysis tools + the Qwen3-8B tokenizer. Runs LOCALLY on the Mac (no GPU); full-dataset tokenization of ~10k multi-turn traces takes a few minutes → run it in the background.
analyze-dataset-token-length
OT-Agent trace datasets are conversation-format (ShareGPT-style): each row is
{"conversations": [{"role","content"}, …], + metadata} (some use "messages"; metadata
fields are e.g. task, result, run_id, trial_name, model, agent). "Token length
of a trace" = the tokenized length of the whole conversation.
The canonical OT-Agent tools (don't reinvent)
scripts/analysis/utils.py— canonical pure conversation/token helpers:extract_conversation_text(record),render_token_representation(...), andcount_conversation_tokens(...). Every count must explicitly selectserialized,conversation_text, orchat_template; these are different measurements and must never be silently substituted for one another.scripts/analysis/context_length_compare.py— cross-dataset context-length comparison.
Tokenizer convention
Always Qwen/Qwen3-8B (AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", trust_remote_code=True)).
Our trace datasets are Qwen3-8B-tokenized even when named for GLM/Kimi/etc. — those "GLM-4.7-…"
models are Qwen3-8B SFTs (see memory reference_glm47_swesmith_is_qwen3_8b); the served model
name in a row's model field (e.g. hosted_vllm/<numeric-id>) is NOT a usable tokenizer name.
Three token-count representations — pick by the question
- conversation_text =
tokenizer(extract_conversation_text(row), add_special_tokens=False)— fast; slightly under-counts vs training (no chat-template tokens). Right for distribution/relative comparisons. - chat_template =
len(tokenizer.apply_chat_template(conv, tokenize=True, add_generation_prompt=False))— what an SFT trainer actually tokenizes; use when the question is "does it fit a 32k/131k training window." If a trace's role shape makes the template raise, report it as uncountable for this representation and tally it separately; do not substitute a plain-text count. - ⚠️ The two can differ by MORE than the wrapper tokens — and in the surprising direction.
Qwen3's chat template strips historical
<think>blocks from earlier assistant turns, so on thinking-mode tracesapply_chat_templatecan count fewer tokens than plain-concat (which keeps all thinking) — i.e. more traces "fit" under the template. So the "right" count for a< Nfilter depends on whether your SFT template preserves thinking: default Qwen3 (strips) → optimistic count; a thinking-preserving template (qwen3_thinking_acc.jinja2) → conservative count ≈ plain. Report BOTH and pick by the training template; for a safe "fits 32k" answer use the larger (plain / thinking-preserving) count. - serialized = compact JSON of the raw
conversationsvalue, including role fields and JSON punctuation. This is the legacy datagen-counter measurement; it is useful for continuity but is neither plain text nor training-faithful.
Threshold + metadata filter-count (the common ask)
Recipe: load_dataset (non-streaming) → per row compute (a) the token count and (b) a metadata
predicate → count the intersection; report each leg separately so it's auditable.
Show full SKILL.md (210 more words)Show less
⚠️ The metadata-confound trap (read this before any field predicate)
Instruction text leaks into the trace. Fields like task_complete appear verbatim in the user
instruction of EVERY trace (…include "task_complete": true in your response…), so a naive
'"task_complete": true' in full_text matches all rows (false 100%). Scope the predicate to the
agent's actual emission — i.e. an assistant-role message containing the field, not the prompt:
def agent_complete(conv):
return any(m.get("role") == "assistant" and '"task_complete": true' in (m.get("content") or "")
for m in conv)Always sanity-check the predicate VARIES (not all-true / all-false) before trusting a count — print the per-leg breakdown and an early per-1000-row progress line. (Same caution for any tool-name / status substring: confirm you're matching the agent's output, not the system/user scaffolding.)
How to run
Local, otagent python, HF token sourced; full-dataset tokenization of ~10k multi-turn traces is a few minutes → background it:
source "${DC_AGENT_SECRET_ENV:?set DC_AGENT_SECRET_ENV to the secrets file first}"
/Users/benjaminfeuer/miniconda3/envs/otagent/bin/python scripts/analysis/<script>.py # run_in_backgroundFirst run downloads + caches the parquet (~hundreds of MB). A benign 'NoneType' has no attribute 'ArrowInvalid' on streaming-generator teardown can be ignored (use non-streaming load_dataset anyway).
Worked example — swesmith, "task_complete AND < 32768 tok"
DCAgent2/GLM-4.7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k (9437 traces):
detect completion via the assistant-scoped "task_complete": true (not the instruction prose),
count tokens via apply_chat_template (Qwen3-8B), filter complete AND ct < 32768. Reports three
legs — #complete, #<32k, and the intersection — so the filter is auditable. (Early progress
1000/9437: complete=924 / fit32k=886 / both=854 confirmed the predicate varies ≈92%, i.e. the
confound was correctly excluded.)
© open-thoughts, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in .agents/skills/analyze-dataset-token-length of open-thoughts/OpenThoughts-Agent.
Open the folder on GitHubat commit 3bd1917
Compare with similar skills
Analyze Dataset Token Length next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Analyze Dataset Token Length this skillopen-thoughts/OpenThoughts-Agent | 301 | — | ~1.5k | Automated safety check: Pass | Apache-2.0 | |
| Memori Long-Term MemoryMemoriLabs/Memori | 17k | — | ~2k | Automated safety check: Pass | Apache-2.0 | |
| Project Developmentguanyang/open-agent-hub | 977 | 2 repos | ~4.7k | Automated safety check: Pass | MIT | |
| Context DoctorjzOcb/context-doctor | 119 | — | ~642 | Automated safety check: Pass | MIT | |
| Cognee Session Memory and Improvetopoteretes/cognee | 32k | — | ~3.2k | Automated safety check: Pass | Apache-2.0 | |
| Caveman Learn Token FixesJuliusBrussee/caveman | 111k | — | ~2.8k | Automated safety check: Pass | Apache-2.0 |
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Questions about Analyze Dataset Token Length
What does Analyze Dataset Token Length do?
Analyze the token length of an OT-Agent conversation-format (ShareGPT-style) dataset — the per-trace distribution (median/p90/max) and/or counts under a token threshold + a metadata predicate (e.g. Analyze Dataset Token Length is an agent skill from open-thoughts/OpenThoughts-Agent.g.
When should I use Analyze Dataset Token Length?
Analyze Dataset Token Length fits situations like: asked how long traces are; how many fit a context window (32k/131k); filter a trace dataset by length + a field.
How do I install Analyze Dataset Token Length in Claude Code?
Run `npx skills add open-thoughts/OpenThoughts-Agent --skill analyze-dataset-token-length -a claude-code`. Or copy the skill folder (.agents/skills/analyze-dataset-token-length in open-thoughts/OpenThoughts-Agent) into .claude/skills/analyze-dataset-token-length in your project. Claude Code loads it when a task matches its description.
How do I install Analyze Dataset Token Length in Codex?
Run `npx skills add open-thoughts/OpenThoughts-Agent --skill analyze-dataset-token-length -a codex`. Or copy the skill folder (.agents/skills/analyze-dataset-token-length in open-thoughts/OpenThoughts-Agent) into .agents/skills/analyze-dataset-token-length in your project. Codex loads it when a task matches its description.
Can I use Analyze Dataset Token Length in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add open-thoughts/OpenThoughts-Agent --skill analyze-dataset-token-length -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyze-dataset-token-length, .gemini/skills/analyze-dataset-token-length, .github/skills/analyze-dataset-token-length and .opencode/skills/analyze-dataset-token-length in your project.
What does Analyze Dataset Token Length need to run?
Going by SKILL.md and its folder, Analyze Dataset Token Length needs the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Analyze Dataset Token Length access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Analyze Dataset Token Length safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Analyze Dataset Token Length use?
Analyze Dataset Token Length is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Analyze Dataset Token Length use?
About 1.5k tokens (SKILL.md is roughly 5.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Analyze Dataset Token Length?
Skills that share tags, products or a category with Analyze Dataset Token Length: Memori Long-Term Memory (MemoriLabs/Memori, 17k stars), Project Development (guanyang/open-agent-hub, 977 stars), Context Doctor (jzOcb/context-doctor, 119 stars) and Cognee Session Memory and Improve (topoteretes/cognee, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Analyze Dataset Token Length?
open-thoughts (a GitHub organization) maintains it in open-thoughts/OpenThoughts-Agent, which has 301 GitHub stars. The repository holds 44 skills in this directory. The repository was last updated on September 28, 2026.
Source: open-thoughts/OpenThoughts-Agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.