Install
$ agentstack add skill-notque-vexjoy-agent-condense ✓ 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.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Condense
Strip prose filler from .md files. Preserve every instruction. This skill practices what it preaches.
Phase 1: SCOPE
Identify targets.
- Single file: User names a path. Read it.
- Glob: User gives a pattern (
agents/*.md). Expand, list matches, confirm with user. - Batch (10+ files): Dispatch parallel agents, one per file.
Mechanical pre-pass (deterministic, run before LLM condensing): strip trailing whitespace and consecutive blank lines that inflate Opus token counts. The script handles the mechanical reduction so the LLM phase focuses on prose density.
python3 scripts/check-whitespace.py --fix # 0=clean, 1=violations fixed
Run on the scoped targets (defaults to agents/**/*.md and skills/**/*.md when no path given). Then proceed to the LLM pass on the same files.
Gate: At least one target file identified and readable; mechanical pre-pass run.
Phase 2: CONDENSE
For each file:
- Read the full file. Record word count.
- Rewrite in place applying the rules below.
- Record new word count.
Rules
KEEP (never cut):
- Every instruction, rule, gate, phase, step
- Tables, code blocks, commands, paths
- YAML frontmatter (do not alter)
- Structure: headers, numbered lists, phase ordering
- Technical terms naming specific things
- Reference loading tables
- Error handling sections
- Non-obvious "because X" reasoning
CUT:
- Redundant restatements of the same rule
- "Because X" on obvious rules
- Motivational framing ("this will help you", "it is important to note")
- Filler phrases: "in order to", "it should be noted that", "it is worth mentioning"
- Examples that repeat what the phase already says
- Paragraphs saying the same thing from different angles -- merge to one
STYLE: Short sentences. Active voice. Concrete words. If you can cut a word without losing an instruction, cut it.
DELETE TEST
Before cutting any sentence: "If I remove this, does the reader lose an instruction, rule, or decision?" No = cut. Yes = keep.
Boundaries
Do not reorganize sections, change meaning, add ideas, alter paths/commands, drop tables or code blocks, or modify YAML frontmatter values.
Phase 3: VERIFY
For each condensed file:
- YAML check: Confirm frontmatter parses.
``bash python3 -c "import yaml; yaml.safe_load(open('').read().split('---')[1])" ``
- Report: Show
| File | Before | After | Reduction |table with word counts. - Instruction check: Grep original for key terms (phase names, gate names, commands). Confirm each appears in condensed version. If any missing, restore from original.
Gate: YAML parses. No instructions lost. Reduction reported.
Error Handling
No prose to cut: Report 0% reduction, move to next file.
Instruction removed: Re-read original, restore missing instruction, re-verify.
YAML broken: Restore original frontmatter verbatim, re-condense body only.
Non-.md file: Skip with warning.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: notque
- Source: notque/vexjoy-agent
- License: MIT
- Homepage: https://vexjoy.com
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.