BD headroom-token-compression
Compress AI Agent tool outputs, logs, and RAG content using Headroom — reduce token usage by 20-95%
Compress AI Agent tool outputs, logs, and RAG content using Headroom — reduce token usage by 20-95%
As a process D 42/100 · Unfinished process — weak spots: when it triggers, inputs and preconditions, failures and branches
ProcedureGitHubAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
For the model run — optional
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 99 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
description-no-whenneither description nor a "## When to Use" section says when to use the skill
Process rating: all ten parameters 42/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Consistency. Frontmatter name (headroom-token-compression) differs from the folder (token-compression)
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 4 mutating operations with no state check
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 85Steps. 24 steps, 2 vague phrases
- 100Execution cost. Instruction body is 1211 tokens
- low 11 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 99: 120–800 characters recommended
- -2localhost URLs: will not work for another user
- +2Single-language instructions
- +4Structure: 17 headings
- +3Step-by-step instructions: 24 items
- +3Output format is stated explicitly
- +4Has examples (8 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.
External checks
ClawHub: suspicious
This skill is purpose-aligned for token compression, but it asks users to install an unpinned third-party tool that can wrap future agent sessions and cache raw prompts, logs, and tool output locally.
LLM: suspicious (high) · 11 Sept 2026