decision-ladder
Skill2 starsChanged 3 months agoMITView on GitHub
Ponytail-inspired YAGNI enforcement — climb the decision ladder before writing any code. stdlib → regex → existing module → new code.
1 other project uses a version of this, 1 of them edited.
Adopted by
Most recently taken up by
- zedarvates/frugal-skillsedited, first seen Nov 8, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 1
- zedarvates/frugal-skills★ 135% same
Wrote their own version · 0
None found.
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.
In this repository75 other files it gives its agents.
- AGENTS.mdAGENTS.md
- clean-cacheSkill
- agent-cacheSkill
- app_testSkill
- asset-qualitySkill
- auto_distillSkill
- auto-memorySkill
- auto_routerSkill
- benchSkill
- bootstrapSkill
- botte-proxySkill
- cacheSkill
- capabilitiesSkill
- cardinalSkill
- cavemanSkill
- checkupSkill
- clarificationSkill
- clusterSkill
- code_fingerprintSkill
- completion_proofSkill
- conductorSkill
- context_budgetSkill
- context_profilerSkill
- context_slicerSkill
- context_windowsSkill
- control_loopSkill
- cost_estimatorSkill
- cwe_kbSkill
- dag_optimizerSkill
- dashboardSkill
- demoSkill
- diff_languageSkill
- directives_auditSkill
- docgenSkill
- docs_stewardSkill
- eventsSkill
- fallow_likeSkill
- fast_contextSkill
- fixSkill
- harness_deltaSkill
- hermes-bridgeSkill
- hf_provenanceSkill
- infra_advisorSkill
- ingestSkill
- llm_backendsSkill
- llm_mcpSkill
- loaderSkill
- mcp_gatewaySkill
- meta_harnessSkill
- metricsSkill
- migration_auditSkill
- monte_cristoSkill
- mousquetairesSkill
- nlp_deterministicSkill
- nn_auditSkill
- prefix-prunerSkill
- prefix_treeSkill
- preflightSkill
- prompt_improverSkill
- reportSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 46
Add to workflow-check as a pre-code hook.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Decision Ladder
Ponytail-inspired YAGNI enforcement for botte-secrete. Before writing ANY code, climb this ladder. Each rung that passes saves the cost of every rung above it.
The Ladder
- stdlib — Is this already in Python's standard library? (os, pathlib, json, re, ast, sqlite3, http, etc.)
- regex_oneliner — Can a regex, string method, or one-liner do it? No function needed.
- existing_module — Does an existing botte skill already handle this? Reuse > rebuild.
- new_code — No simpler alternative exists. Write the minimal implementation.
Usage
from skills.decision_ladder.ladder import climb, audit_task_list
# Single task
result = climb("extract function names from a Python file")
# → LadderResult(rung="stdlib", solution="ast module (ast.parse/walk)", saved_lines=15)
# Audit a task list
report = audit_task_list([
"parse JSON config",
"design auth middleware",
"count word frequency in text",
"strip HTML tags from string",
"implement custom OR-Tools solver",
])
# → avoidable_pct: 80%, lines_saved: 85Integration
Add to workflow-check as a pre-code hook. See hook.py.
Metrics
Tracks:
- % of tasks that could be avoided (stdlib, regex, existing module)
- Estimated lines saved per task
- Confidence score per suggestion
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.

