recipes
Skill1 starsChanged 32 days agoMITView on GitHub
Instructions for the upstream workflow: Observe content-free botte-llm call shapes, review repeated read-only workflows, and run only locally approved declarative recipes.
5 other projects use a version of this.
Adopted by
Most recently taken up by
- yourbeckett/beckett-for-claudetheir own version, first seen Nov 1, 2026
- deepgram/skillstheir own version, first seen Oct 25, 2026
- syerad/claude-skill-loopstheir own version, first seen Jun 12, 2026
- brainsforai/brains-skillstheir own version, first seen Jun 5, 2026
- durandom/skillstheir own version, first seen Mar 5, 2026
Other versions5 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 5
- deepgram/skills★ 26same name
- durandom/skills★ 7same name
- syerad/claude-skill-loops★ 2same name
- brainsforai/brains-skills★ 1same name
- yourbeckett/beckett-for-claude★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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The file
Files
recipes — Verified read-only MCP workflows
Package scope and prerequisites
This catalog ships instructions, not the executable runtime. Python modules, scripts, local services and optional tools named below must be installed separately from the upstream project. Check their availability before running commands; if they are missing, explain the prerequisite instead of claiming execution or results. The host agent may use model tokens while following these instructions.
Recipes remove repeated LLM orchestration from common botte-llm workflows.
They are local declarative data, never generated Python or shell code.
Safety boundary
- Observation is off by default and covers only calls handled by
botte-llm. - Events retain tool names, argument types, verdicts, bounded sizes, timings, and fingerprints. They do not retain prompts, argument values, raw results, file contents, secrets, identities, or exact paths.
- A candidate requires the same verified sequence in three separate runs.
- Candidates are inert until approved through the CLI.
- V1 permits only the explicit deny-by-default tool registry in
tool_policy.py: read-only, local, no network, no subprocess, no writes. - Schema, policy, project, or engine drift makes execution fail closed.
- Recipe failure returns control to the caller; it never retries or invokes a fallback automatically.
Enable local observation
Set the environment variable only for the MCP process whose local usage should be observed:
$env:BOTTE_RECIPE_OBSERVE = "1"
python -m skills.llm_mcp.serverProject observations are stored in .botte/recipes/observations-v1.jsonl.
The file rotates after 2 MiB and keeps the newest half of complete records.
User-approved recipes are stored below
%LOCALAPPDATA%/BotteSecrete/recipes/; project overrides remain below
.botte/recipes/. Both locations are local and ignored by Git.
Review lifecycle
botte recipes --project C:/project mine --json
botte recipes --project C:/project list --json
botte recipes --project C:/project show <candidate-id> --json
botte recipes --project C:/project approve <candidate-id> --scope user --json
botte recipes --project C:/project reject <candidate-id> --reason not-useful --json
botte recipes --project C:/project disable <recipe-id> --scope user --jsonDeletion is deliberately explicit:
botte recipes --project C:/project delete candidate <candidate-id> --yes --json
botte recipes --project C:/project delete recipe <recipe-id> --scope user --yes --json
botte recipes --project C:/project delete observations --all --yes --jsonMCP execution
recipe_run is a lazy MCP tool, so its schema is not added to the permanent
tool prefix. It accepts an approved recipe ID, explicit runtime parameters,
project path, and optional dry_run. Dry-run validates the complete recipe and
returns only step/tool/argument-field names; it never dispatches a step.
An approved recipe grants no new authority. Each underlying tool must still be present in the current safe registry and MCP dispatcher.
V1 exclusions
Third-party MCP interception, network calls, local-model calls, filesystem writes, subprocesses, installation, deletion outside the recipe store, publication, semantic matching, automatic approval, automatic retry, and parallel execution are out of scope.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

