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 agentmods add skills/aeonfun/aeon/action-converternpx skills add aeonfun/aeon --skill action-convertergit clone --depth 1 https://github.com/aeonfun/aeonWrote 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/aeonfun/aeon/action-converter)<a href="https://agentmods.dev/skills/aeonfun/aeon/action-converter"><img src="https://agentmods.dev/badge/skills/aeonfun/aeon/action-converter.svg" alt="Measured on agentmods" height="20"></a>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 | $0.02556 |
| Opus 5 | $0.00012 | $0.01278 |
| Sonnet 5 | $0.00005 | $0.00511 |
| Haiku 4.5 | $0.00002 | $0.00256 |
Grade A, and why
action-converter 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 5d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- action-converter — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
${var} — Optional focus area (e.g.
health,networking,learning,shipping,crypto,repo). If empty, covers all areas. Treated as a tiebreaker, not a hard filter.
Read memory/MEMORY.md for stated goals, "Next Priorities", tracked items, and current topics.
Read the last 7 days of memory/logs/ for recent activity, patterns, and what's already been suggested or done.
Read memory/topics/ (every file) for active threads.
Read memory/cron-state.json for failing or stuck skills.
Read memory/watched-repos.md for repos under attention.
Read output/articles/ (last 7 days, filenames only — peek at the 2 most recent for theme).
If soul/SOUL.md exists, read it for identity, voice, focus areas.
Run gh pr list --state open --limit 20 --json number,title,createdAt,isDraft,reviewDecision,headRefName 2>/dev/null to get open PRs (used to anchor "ship" / "review" / "merge" loops).
Graceful bootstrap — each of the reads above may be missing on cold starts. For every source, if the file/directory is missing or empty (including memory/topics/*.md, memory/cron-state.json, and gh pr list returning empty or erroring), skip it and record BOOTSTRAP: <resource> not yet populated in the run's working notes. Continue with whatever signals are available — the skill must degrade gracefully, never fail. If every single source is empty, fall through to the ACTION_CONVERTER_NO_CONTEXT mode below.
Steps
1. Detect mode
Decide which exit mode this run will produce based on context volume:
- ACTION_CONVERTER_NO_CONTEXT — if BOTH
memory/logs/has 0 entries ANDmemory/MEMORY.mdis the unmodified template (matches "Last consolidated: never" AND "Configure notification channels"). Notify the operator and stop — do not invent actions out of thin air. - ACTION_CONVERTER_BOOTSTRAP — if
memory/logs/has <3 distinct dates in the last 14 days ORmemory/MEMORY.md"Next Priorities" still contains template entries ("Configure notification channels", "Run first digest"). Switch the action pool to setup-completion actions: enable specific skills inaeon.yml, configure missing notification secrets, run the first digest, populatememory/topics/for the first tracked thread, etc. These are still real, named, completable actions — not generic onboarding advice. - ACTION_CONVERTER_OK — otherwise. Use the full leverage-scored loop pipeline below.
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.
- 5d ago First seen · 155 lines · 23 tokens per session scan A a13f89dd5427
action-converter is a skill published in the GitHub repository aeonfun/aeon (715 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 2,556 once invoked, about $0.0001 per session on Opus 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 2026-08-30.
Other skills, from other repositories
agent-framework-py-release
Use when cutting a Python release for the microsoft/agent-framework monorepo. Triggers on "bump py versions", "cut a python release", "prepare release PR for python", "release py packages", "bump python to X.Y.Z", or similar requests to bump Python package versions and prepare a release PR. Handles all four lifecycle…
python-package-management
Guide for managing packages in the Agent Framework Python monorepo, including creating new connector packages, versioning, and the lazy-loading pattern. Use this when adding, modifying, or releasing packages.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
verify-samples-tool
How to use the verify-samples tool to run, verify, and manage sample definitions in the Agent Framework repository. Use this when adding, updating, or running sample verification.
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.
build-and-test
How to build and test .NET projects in the Agent Framework repository. Use this when verifying or testing changes.