Context Profiler
Measure a project's always-on prefix (agent directives + core rules + MCP tool schemas + skill catalogue) in tokens and as a % of small local-model windows (64k/128k/256k), with a concrete reduction plan (lazy tool loading, on-demand skill search). Use to see how much of a modest machine's usable context is spent before any real work, and how to shrink it so weaker machines can run local LLMs usa…
Install
$ agentstack add skill-zedarvates-botte-secrete-context-profiler ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
context_profiler — how much window is gone before you start?
On a modest machine the usable window is shared between the model weights' RAM and the KV-cache, and every always-on token is paid twice (RAM + each turn). This measures the prefix an agent carries before its first message and frames it against real local windows, so you can shrink it.
python -m skills.context_profiler.cli . # prefix tokens + % of 64k/128k/256k
python -m skills.context_profiler.cli . --jsonComponents measured:
- directives — CLAUDE.md / AGENTS.md instructions ([[metrics]] always-on).
- core_agent — the shared
core-agent.mdrules, if present. - tool_schemas — the MCP tool definitions injected into the agent (the hidden
cost: on this repo ~3.8k tok for 38 tools).
- skill_catalog — the skills' descriptions IF the whole catalogue is injected.
It then reports the % of 64k/128k/256k windows and a reduction plan with honest token savings:
- lazy tool loading — expose ~5 core tools + a
find_tool(query)that loads a
schema on demand (the pattern this very harness uses via ToolSearch).
- on-demand skill search — don't inject the catalogue; use [[skill_finder]] /
[[context_budget]] to load only the relevant skills per task.
On this repo: prefix ~7.9k tok (12% of a 64k window) → ~1.4k tok (2%) once lazy tools + on-demand skills are applied. Exposed via [[llm_mcp]] as context_profile. Pure measurement, 0 cloud tokens.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: zedarvates
- Source: zedarvates/botte-secrete
- License: MIT
- Homepage: https://github.com/zedarvates/botte-secrete
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.