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Action Converter

Organization
aeonfun
action-converter

5 concrete real-life actions, leverage-scored against open loops with specificity and anti-fluff gates

Overview

Publisheraeonfun
Repositoryaeon
Skill nameaction-converter
Stars
771
Forks
280
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by aeonfun on GitHub. Read the source before you install it.

Installation

Install the Action Converter AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/aeonfun/aeon/tree/main/skills/action-converter
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aeonfun/aeon.git /tmp/aeon
mkdir -p .claude/skills
cp -r /tmp/aeon/skills/action-converter .claude/skills/action-converter
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Action Converter in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Action Converter on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Action Converter is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

${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 AND memory/MEMORY.md is 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 OR memory/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 in aeon.yml, configure missing notification secrets, run the first digest, populate memory/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.

2. Extract open loops

Build a single deduped list of named open loops from every source above. A loop is a specific in-flight thing, not an area. Each loop captures at minimum: id (short slug), text (one phrase), source (where it came from), age_days, urgency_signal (deadline / blocker / stalled / fresh).

Sources to mine:

  • Open PRs — every entry from gh pr list. Loop text: PR #N: <title> with urgency = stalled if >3 days old or review_decision is REQUEST_CHANGES.
  • MEMORY.md "Next Priorities" — each bullet becomes a loop. Skip template lines.
  • memory/topics/*.md — for each topic file, scan for headings or bullets that look like ongoing work (TODO, WIP, "In progress", "Tracking", trailing question marks, dated items in the last 30 days).
  • memory/cron-state.json — every skill with consecutive_failures > 0 OR last_status != success becomes a loop: fix <skill>. Urgency = blocker if consecutive_failures ≥ 3.
  • Recent logs (last 7 days) — any line ending in ?, containing "blocked", "next:", "todo", "follow-up", "unfinished", or naming a deferred decision.
  • Recent articles (last 7 days) — each new article opens a distribution/syndication loop ("syndicate ") if syndicate-article is enabled, and a feedback loop ("respond to comments on ") if traffic is plausible.
  • ${var} — if set, add a synthetic loop "advance ${var}" so at least one action ties to the requested focus area.

Deduplicate by similarity in text. Cap the loop list at 25.

3. Score loops

Score every loop on three 1–5 axes. Total = leverage × urgency × concreteness.

Axis135
leveragepersonal hygieneuseful but localunblocks others, shippable artifact, or compounds
urgencynice-to-havethis weektoday (deadline / blocker / >5 day stall on hot loop)
concreteness"think about X"known shape, no draftnext step is one named action

Drop any loop scoring <8 from the candidate pool. If ${var} is set, give a +0.5 leverage bump to loops touching that area.

4. Convert loops to actions

Convert the top loops into actions until you have 5 distinct ones. Constraints on every action:

  1. Specificity gate — must name at least one of: a file path, a PR number, a person/handle, a project/repo, a tool/CLI command, a URL, a tracked entity from MEMORY.md. Generic "reach out to people" / "review your goals" / "explore opportunities" fails this gate.
  2. Banned-phrase lint — reject any action whose action text contains: go for a walk, drink water, take a break, reflect, journal, meditate, brainstorm, review your, think about, consider, look into, explore opportunities, reach out to people, network with, clean up your inbox, organize your, plan tomorrow, do some reading, check social media. These are filler, not actions.
  3. Time estimate — must fit in ≤2 hours; bias toward 30–60 min slots.
  4. Definition of done — one observable check. "PR opened" / "commit pushed" / "message sent to " / "doc has section X with ≥3 items". Not "feel better" or "have more clarity".
  5. Anti-template (14-day novelty check) — for each candidate action, extract the verb + main noun. Reject if the same verb+noun appears in any memory/logs/*.md from the last 14 days. (Different verb+same noun is fine — only the bigram blocks.)
  6. Score ≥4 on the 1–5 quality scale below. Anything <4 is dropped and replaced from the next loop in the queue.

Quality 1–5: 1 = filler, 2 = vague, 3 = specific but low-leverage, 4 = specific + tied to a real loop, 5 = specific + high-leverage + would visibly move the project today.

If after exhausting the loop list you have <5 surviving actions, fill the rest from the category pool below but only with category-specific candidates that pass all gates above. Categories exist as a fallback, not a checklist:

  • Build — ship, write, create, deploy, fix, prototype, refactor against a named file/PR
  • Connect — DM/reply/quote a named handle about a named topic; comment on a named PR/issue
  • Learn — read a named paper/doc/repo and write a 5-bullet takeaway to memory/topics/
  • Health/Energy — only if tied to a named, novel, non-banned action (rare; usually skipped)
  • Money — concrete revenue/funding/deal step naming a counterparty
  • Position — write a named tweet/cast/post on a named claim
  • Explore — lateral move tied to a named external signal from this week's logs

If even with the category pool you can't reach 5, output fewer (3 or 4) and flag ACTION_CONVERTER_THIN in the notify — don't pad.

5. Compose the output

Build one today's shape line: ≤14 words capturing the dominant theme of the 5 actions ("Ship 2 PRs, unblock failing skill, syndicate yesterday's article" — not "be productive today"). This becomes the lede.

Order the 5 actions by descending quality score, then by descending urgency.

6. Send via ./notify

Use this exact format (./notify renders the Markdown per-channel — just write it cleanly):

*5 Actions — ${today}*
Shape: <today's shape line>

1. <action — one imperative sentence, names a specific entity>
why: <≤18 words, what makes this leverage today, names a specific signal>
done: <one observable check>
loop: <loop id or "category:<name>" if filled from pool>

2. <action>
why: <…>
done: <…>
loop: <…>

3. <action>
why: <…>
done: <…>
loop: <…>

4. <action>
why: <…>
done: <…>
loop: <…>

5. <action>
why: <…>
done: <…>
loop: <…>

sources: memory=<lines> logs=<days> topics=<files> prs=<open> cron_failing=<n> mode=<OK|BOOTSTRAP|THIN>

Notification rules:

  • Drop any action whose done: line couldn't be written without hand-waving.
  • If mode is ACTION_CONVERTER_NO_CONTEXT, skip the action list entirely and notify: *Action Converter — no context yet* plus a one-line pointer ("Populate memory/MEMORY.md or run a skill to seed memory/logs/").
  • If mode is ACTION_CONVERTER_BOOTSTRAP, prefix the shape line with Bootstrap mode: and pull all actions from the setup-completion pool.

7. Log to memory/logs/${today}.md

Append:

### action-converter
- **Mode:** OK | BOOTSTRAP | THIN | NO_CONTEXT
- **Focus:** <var or "general">
- **Shape:** <today's shape line>
- **Actions:** N (quality avg <x.x>/5)
- **Loops anchored:** <list of loop ids surfaced>
- **Loops carried over:** <list of high-score loops not chosen, for tomorrow>
- **Notification sent:** yes

Carrying loops forward in the log is what powers the 14-day novelty check and lets the next run see what's been deferred.

Network note

gh pr list works in a GitHub Actions run via the gh CLI (handles auth internally, so no token touches the command line). If gh is unavailable or returns empty, treat the open-PR loop source as prs=0 and continue — do not block the whole run.

No outbound HTTP is required. All inputs are local files and gh. No new env vars.

Frequently asked questions

What does the Action Converter AI skill do?

5 concrete real-life actions, leverage-scored against open loops with specificity and anti-fluff gates

Why use Action Converter on TypingMind?

Because you install it once and use it with any model. Action Converter is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Action Converter in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aeonfun/aeon/tree/main/skills/action-converter. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Action Converter?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Action Converter?

As many as you like. As long as a model supports skills, you can use Action Converter with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Action Converter AI skill free?

Yes. It is published on GitHub by aeonfun under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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