Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jmagly/aiwg --skill workspace-prune-workinggit clone --depth 1 https://github.com/jmagly/aiwgWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jmagly/aiwg/workspace-prune-working)<a href="https://agentmods.dev/skills/jmagly/aiwg/workspace-prune-working"><img src="https://agentmods.dev/badge/skills/jmagly/aiwg/workspace-prune-working/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jmagly/aiwg/workspace-prune-working"><img src="https://agentmods.dev/badge/skills/jmagly/aiwg/workspace-prune-working.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 265 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- high Tool Misuse · line 266 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- medium Excessive Agency · line 22 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00023 | $0.02746 |
| Opus 5.5 | $0.00009 | $0.01098 |
| Sonnet 5.5 | $0.00005 | $0.00549 |
| Haiku 4.5 | $0.00002 | $0.00275 |
Grade A, and why
workspace-prune-working scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 369 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workspace Prune Working
Clean up the .aiwg/working/ directory by intelligently handling temporary files. Promotes valuable content to the main documentation structure, archives content worth preserving, and deletes truly temporary files.
Parameters
| Flag | Description |
|---|---|
project-directory |
Project root (default: .) |
--promote-all |
Promote all promotable files without prompting |
--archive-all |
Archive all archivable files without prompting |
--delete-all |
Delete all deletable files without prompting |
--dry-run |
Preview changes without modifying files |
--interactive |
Prompt for each file decision |
--force |
Skip confirmation prompts |
Purpose of .aiwg/working/
The .aiwg/working/ directory is designated for:
- Multi-agent work-in-progress drafts
- Temporary scratch files during orchestration
- Review feedback before synthesis
- Iterative document versions
It is NOT a permanent storage location. Files here should eventually be:
- Promoted → Moved to appropriate
.aiwg/subdirectory as finalized docs - Archived → Preserved in
.aiwg/archive/for historical reference - Deleted → Removed when no longer useful
Execution Steps
Step 1: Scan Working Directory
Inventory all files in .aiwg/working/:
# List all files with metadata
find .aiwg/working -type f -exec stat -c '%Y %s %n' {} \; | sort -rn
# Count by subdirectory
find .aiwg/working -mindepth 1 -maxdepth 1 -type d | while read dir; do
echo "$(basename "$dir"): $(find "$dir" -type f | wc -l) files"
done
Report:
Working Directory Scan
======================
Total files: 23
Total size: 145 KB
By Category:
architecture/ 8 files (SAD drafts, reviews)
requirements/ 5 files (UC iterations)
testing/ 4 files (test plan drafts)
scratch/ 6 files (temporary notes)
Age Distribution:
< 1 day: 4 files
1-7 days: 8 files
> 7 days: 11 files
Step 2: Classify Files
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 369 lines · 23 tokens per session scan A 731f6af28d06
workspace-prune-working is a skill published in the GitHub repository jmagly/aiwg (225 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 2,746 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
Other skills, from other repositories
launch
Planning releases and reporting delivery work from GitHub PR history. Use when versioning, CHANGELOGs, rollout or rollback plans, engineering metrics, retrospectives, or stakeholder reports are needed.
pdm
Navigating delivery status read-only: reconciles planned scope (specs/roadmap/PRD) against implemented code for what's built vs left. Not for priority scoring (Rank) or AC conformance (Attest).
sherpa
Guiding workflows by decomposing complex tasks (Epics) into Atomic Steps under 15 minutes each, with progress tracking and drift prevention. Use when complex decomposition is needed.
rank
Quantifying priority by scoring competing items with ICE/RICE/WSJF/MoSCoW/Cost of Delay/Kano. No code. Use to prioritize features/bugs/initiatives or arbitrate Must vs Should at MVP scoping.
spark
Proposing new features leveraging existing data/logic as Markdown specifications. Use when brainstorming new features, product planning, or feature proposals are needed. Does not write code.
missive
Invoke only when spawning a quest from a GitHub issue reference. Fetches GitHub issue context for quest spawning. Parses issue references, retrieves structured data via gh, and produces branch suggestions and PR keywords. Used standalone or as input to quest orchestration.