Agent skill

Reply Maker

by aeonfun in aeonfun/aeon

Draft copy-paste-ready X replies - two options per reply-worthy tweet from tracked accounts, topics, or lists (default), or ready-to-post responses to engagement opps in recent logs (from-logs)

MITAuto-check passedWriting & Content

Install Reply Maker

skills CLI
$ npx skills add aeonfun/aeon --skill reply-maker -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aeonfun/aeon reply-maker --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aeonfun/aeon.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/reply-maker .claude/skills/reply-maker && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
reply-maker
GitHub stars
767
Token cost
~6k tokens
SKILL.md length
2,612 words
Files
1
Skills in repo
82
Repo updated
First seen
Licence
MIT

At a glance

Draft copy-paste-ready X replies - two options per reply-worthy tweet from tracked accounts, topics, or lists (default), or ready-to-post responses to engagement opps in recent logs (from-logs)

  • Works in 7 steps: Strip the prefix. The instruction is… → Load the last draft. Read… → Apply the instruction. Read soul/ for… → …
  • Tasks that involve Social media posts
  • SKILL.md covers Preamble (both modes), Voice, Save drafts + offer revision… and Banned sycophancy phrases, plus 3 more sections
  • Calls jq and make; reaches api.x.ai and x.com; needs XAI_API_KEY

What it does

Reply Maker is an agent skill from aeonfun/aeon. Draft copy-paste-ready X replies - two options per reply-worthy tweet from tracked accounts, topics, or lists (default), or ready-to-post responses to engagement opps in recent logs (from-logs)

Its SKILL.md is about 6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Writing & Content, covering Social media posts. It works with X (Twitter) and Telegram. The repository describes itself as: The most autonomous AI agent framework: runs unattended on GitHub Actions, self-healing skills, drives Claude Code, Grok, Codex & more. No approval loops. Configure once, forget… The licence is MIT.

When your agent uses it

  • Tasks that involve Social media posts

Example prompts

  • “/reply-maker”

Requirements

  • A credential in XAI_API_KEY

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Strip the prefix. The instruction is ${var#revise:} (keep any inner colons). Trim whitespace — e.g. make them shorter, less formal, drop…
  2. Load the last draft. Read memory/drafts/reply-maker-latest.md — the stable path every normal run saves to (see the save steps in A4 / B6)…
  3. Apply the instruction. Read soul/ for voice, then regenerate the saved replies applying the operator's instruction. Keep the same set of…
  4. Re-save the revised drafts to memory/drafts/reply-maker-latest.md (overwrite), so a further revise: refines the newest version.
  5. Re-send via ./notify in the same format the originating mode uses, with a first line flagging it as a revision, e.g. revised…
  6. Re-offer a further revision (the operator is actively iterating, so this is expected, not a nag — skip the daily dedup guard here)
  7. Log under ### reply-maker with - Mode: revise and the instruction (see Log), then end the run — do NOT run Mode A or B.

What it can do on your machine

Read from SKILL.md and the folder at commit f252074. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • jq
    • make

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.x.ai
    • x.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • XAI_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Reply Maker loads about 6k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 2,612 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~51
When it runs · the whole SKILL.md, loaded when a task matches
~6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aeonfun/aeon at commit f252074, republished under its MIT licence (© aeonfun). 2,612 words, ~6,018 tokens.

Download SKILL.mdSave it as .claude/skills/reply-maker/SKILL.md (or your agent's skills folder).
name
reply-maker
description
Draft copy-paste-ready X replies - two options per reply-worthy tweet from tracked accounts, topics, or lists (default), or ready-to-post responses to engagement opps in recent logs (from-logs)
metadata.title
Reply Maker
metadata.category
productivity
metadata.var
empty = auto-discover reply-worthy tweets and draft two options each; @handle / numeric X list ID / topic = scope the drafting to that; from-logs (or…
metadata.commits
false
metadata.tags
social, meta
metadata.requires
XAI_API_KEY?
<!-- autoresearch: variation B — sharper output via specificity gates, anti-sycophancy lint, post-write self-edit, and skip-gate for low-leverage tweets -->

${var} — selects the mode and scope:

  • empty → Mode A (Reply Drafting): auto-discover reply-worthy tweets across your areas of interest (from recent logs + memory) and draft two reply options for each.
  • @handle / numeric X list ID / topic → Mode A (Reply Drafting) scoped to that handle, list, or topic.
  • from-logs (or --from-logs, optionally followed by an @handle or project name to narrow the scan) → Mode B (From-Logs Engagement): scan recent logs for flagged engagement opportunities and turn them into copy-paste-ready responses.
  • revise:<instruction> → Revise branch: reload the last drafted replies and refine them per the instruction (the Telegram force-reply shape, e.g. revise:make them shorter).

Preamble (both modes)

Read memory/MEMORY.md for context on active projects and open engagement follow-ups.

Then read memory/logs/ — the window depends on the mode:

  • Mode A: the last 2 days of memory/logs/ for recent fetch-tweets, narrative-tracker, and prior reply-maker outputs (used as a candidate pool and for reply de-duplication).
  • Mode B: the last 7 days of memory/logs/ for engagement opportunities flagged by other skills (mention-radar, fetch-tweets, reply-maker) or noted in MEMORY.md "Known Follow-ups".

Parse ${var} to pick the branch (trim whitespace, compare case-insensitively):

  • If ${var} starts with revise: — run the Revise branch (below) and stop. This is the shape scripts/telegram-route.sh sends when the operator replies to a "refine these replies?" force-reply prompt; catch it before mode parsing.
  • If ${var} is from-logs or --from-logs — optionally followed by a whitespace-separated @handle or project name — run Mode B (From-Logs Engagement). Treat any trailing token as an optional filter that narrows the opportunity scan to that handle/project.
  • Otherwise run Mode A (Reply Drafting), treating ${var} as the scope: empty, @handle, numeric X list ID, or a topic string.

Voice

If soul files exist (soul/SOUL.md, soul/STYLE.md, soul/examples/), read them and mirror that voice in every reply. Match sentence length, vocabulary choices, punctuation habits, and the kinds of things the operator would never say.

If no soul files exist (or the bodies are empty placeholders), write replies that are:

  • Direct and substantive — no fluff, no sycophancy
  • Under 280 characters each (X replies; DMs and GitHub comments may run longer — see Mode B)
  • Opinionated but grounded in specifics
  • The kind of reply that adds to the conversation, not noise

Either way, when responding to someone who cosigned/mentioned/attributed the operator (Mode B): acknowledge without groveling — no "thanks so much for the kind words!", just the actual response.


Revise branch (revise:… — Telegram force-reply)

The operator tapped the "refine these replies?" prompt and sent a free-text revision instruction. Handle it before Mode A/B:

  1. Strip the prefix. The instruction is ${var#revise:} (keep any inner colons). Trim whitespace — e.g. make them shorter, less formal, drop reply B on #2.
  2. Load the last draft. Read memory/drafts/reply-maker-latest.md — the stable path every normal run saves to (see the save steps in A4 / B6). If it's missing or empty, there's nothing to refine: send ./notify "Nothing to revise yet — run reply-maker first, then reply here to refine the drafts." and end the run.
  3. Apply the instruction. Read soul/ for voice, then regenerate the saved replies applying the operator's instruction. Keep the same set of target tweets and the same A/B two-option structure (Mode A) or ready-to-post list (Mode B) — you're refining wording, not re-discovering candidates. Re-enforce the hard reply rules: ≤280 chars for X replies, no sycophancy (see Banned sycophancy phrases), specifics not gestures.
  4. Re-save the revised drafts to memory/drafts/reply-maker-latest.md (overwrite), so a further revise: refines the newest version.
  5. Re-send via ./notify in the same format the originating mode uses, with a first line flagging it as a revision, e.g. revised (${var#revise:}):. Use ./notify -f <file> for multi-line output.
  6. Re-offer a further revision (the operator is actively iterating, so this is expected, not a nag — skip the daily dedup guard here):
    bash
    ./notify "Want another pass? Reply with a change and I'll revise again." \
      --force-reply --placeholder "e.g. make them shorter" \
      --context "reply-maker::revise"
  7. Log under ### reply-maker with - **Mode:** revise and the instruction (see Log), then end the run — do NOT run Mode A or B.

Mode A — Reply Drafting

Generate two reply options for 5 reply-worthy tweets from tracked X accounts, a list, or a topic.

A1. Gather candidate tweets

Goal: assemble 10–15 candidates posted in the last 6 hours (the high-leverage reply window — the algorithm rewards early replies, and the OP is still likely to engage back). Recency fallback: if the 6h window yields fewer than 3 candidates after the skip gate, widen to 12h and retry before failing the run.

For every candidate, capture: @handle, full tweet text, tweet URL, posted_at (ISO), engagement counts (likes, replies, retweets if available), and a one-line why-this-tweet note.

Path A — X.AI API (primary). XAI_API_KEY is injected into this skill's environment (declared in requires:), so the direct curl to https://api.x.ai/v1/responses is the primary fetch path (full contract in Fetching at the bottom). Preflight the key, then call Grok's x_search, capturing the HTTP status so any fallback decision is fact-based. x_search searches X live and takes 30–120s — set the Bash tool timeout to ≥180000 when you run this (a slow curl is not a missing key).

bash
[ -n "$XAI_API_KEY" ] && echo KEY_PRESENT || echo KEY_UNSET
TO_DATE=$(date -u +%Y-%m-%dT%H:%M:%SZ)
FROM_DATE=$(date -u -d "6 hours ago" +%Y-%m-%dT%H:%M:%SZ 2>/dev/null || date -u -v-6H +%Y-%m-%dT%H:%M:%SZ)

If KEY_PRESENT (it will be), Path A is required. Build the payload file /tmp/xai-rm-payload.json per ${var} (three shapes below — each branch writes the same fixed file), then:

bash
HTTP=$(./secretcurl -s -o /tmp/xai-rm.json -w '%{http_code}' --max-time 150 -X POST "https://api.x.ai/v1/responses" \
  -H "Content-Type: application/json" -H "Authorization: Bearer {XAI_API_KEY}" -d @/tmp/xai-rm-payload.json)
echo "xai http=$HTTP bytes=$(wc -c </tmp/xai-rm.json)"

On HTTP=200 with a non-empty body, parse /tmp/xai-rm.json and mark xai=ok:

bash
jq -r '.output[] | select(.type == "message") | .content[] | select(.type == "output_text") | .text' /tmp/xai-rm.json

The payload file /tmp/xai-rm-payload.json depends on ${var}. Whichever branch matches, build it with jq -n --arg (never a shell-interpolated string) and write it to that one fixed path — the ./secretcurl call above then sends it with -d @/tmp/xai-rm-payload.json:

If ${var} looks like an X list ID (numeric):

bash
LIST_ID="${var}"
jq -n --arg list_id "$LIST_ID" --arg from "$FROM_DATE" --arg to "$TO_DATE" '{
  model: "grok-4.7",
  input: [{role: "user", content: ("Look at X list https://x.com/i/lists/" + $list_id + ". Return the 12 most reply-worthy original posts (not retweets, not replies) by members of this list between " + $from + " and " + $to + ". Reply-worthy = has a take, claim, question, or framing worth engaging — NOT pure self-promo, breaking news without analysis, or threads already past 500 replies. For each: @handle, full tweet text, tweet URL, posted_at ISO timestamp, like/reply/retweet counts.")}],
  tools: [{type: "x_search", from_date: $from, to_date: $to}]
}' > /tmp/xai-rm-payload.json

If ${var} looks like a @handle — same query intent, scoped to that handle's recent original posts:

bash
HANDLE="${var}"
jq -n --arg handle "$HANDLE" --arg from "$FROM_DATE" --arg to "$TO_DATE" '{
  model: "grok-4.7",
  input: [{role: "user", content: ("Look at recent original posts (not retweets, not replies) by " + $handle + " on X between " + $from + " and " + $to + ". Return the 12 most reply-worthy. Reply-worthy = has a take, claim, question, or framing worth engaging — NOT pure self-promo, breaking news without analysis, or threads already past 500 replies. For each: @handle, full tweet text, tweet URL, posted_at ISO timestamp, like/reply/retweet counts.")}],
  tools: [{type: "x_search", from_date: $from, to_date: $to}]
}' > /tmp/xai-rm-payload.json

If ${var} is a topic (or empty) — same query intent with ${var} (or the top 2–3 topics from memory/MEMORY.md when empty) as the search query. When empty, also pull tweet candidates surfaced in the last 2 days of fetch-tweets logs as a backup pool.

bash
TOPIC="${var}"   # when empty, substitute the top 2–3 topics from memory/MEMORY.md
jq -n --arg topic "$TOPIC" --arg from "$FROM_DATE" --arg to "$TO_DATE" '{
  model: "grok-4.7",
  input: [{role: "user", content: ("Search X for the 12 most reply-worthy original posts (not retweets, not replies) about " + $topic + " between " + $from + " and " + $to + ". Reply-worthy = has a take, claim, question, or framing worth engaging — NOT pure self-promo, breaking news without analysis, or threads already past 500 replies. For each: @handle, full tweet text, tweet URL, posted_at ISO timestamp, like/reply/retweet counts.")}],
  tools: [{type: "x_search", from_date: $from, to_date: $to}]
}' > /tmp/xai-rm-payload.json

Path B — memory logs + WebSearch (last-resort fallback only). Reach here only on a real Path A failure, and record the true reason — key-unset | http-<code> | empty | timeout — never "XAI_API_KEY unavailable" when the key was set. Use in order until you have ≥3 candidates:

  1. Recent fetch-tweets outputs in memory/logs/ — already have URLs and handles.
  2. WebSearch for very recent posts on memory topics (filter: posted within last 6h, original post not reply). Lower quality — WebSearch favours older high-engagement tweets, so prioritise results dated within the last 6h.
A2. Filter and select 5 tweets

Apply the skip gate first. Discard any candidate that is:

  • Pure self-promo (launching a product, "buy my course", subscribe links)
  • Breaking-news repost without an angle of its own
  • A thread already past ~500 replies (your reply will not be seen)
  • Older than 6 hours (reply window has closed; don't waste a reply slot)
  • A handle/URL already replied to in the last 7 days of reply-maker logs (no duplicates)

From the survivors, rank by leverage = recency × take-strength × room-to-add:

  • Recency: minutes-ago > hours-ago. Tweets <60min old are top priority.
  • Take-strength: a clear claim/question/framing you can either reinforce with evidence or challenge with a flipped premise.
  • Room-to-add: not already swarmed; thread isn't full of stronger replies; you have actual context to contribute.
  • Bias toward authors whose audience overlaps your interests (from memory/MEMORY.md) — replies on those accounts get seen by people who care about the same things.

Pick the top 5. If fewer than 5 survive the gate, output what you have and add REPLY_MAKER_DEGRADED to the notification subject line.

A3. Generate two replies per tweet

For each of the 5 selected tweets, draft two reply options with distinct angles:

Option A — "Evidence add"

  • Builds on their point with a specific datum, named project, named person, concrete number, link, or counterexample they didn't include
  • Tone: collaborative, substantive, calmly confident
  • Must contain at least one named entity, number, or specific reference — vague "great insight, here's another angle" is banned

Option B — "Frame challenge"

  • States the premise you're pushing back on explicitly (one short clause), then offers the contrarian angle, flipped framing, or sharper read
  • Tone: direct, opinionated, not contrarian-for-its-own-sake
  • Must contain the actual disagreement, not a hedge — vague "interesting, but have you considered..." is banned
Hard reply rules (apply to both A and B)
  • ≤ 280 characters including any handle prefix
  • No sycophancy — see the ## Banned sycophancy phrases section below. Any draft containing a banned phrase must be rewritten.
  • No hedging stacks — "It could be argued that…", "Just my two cents but…", "Maybe I'm wrong but…" — pick a position
  • Specifics, not gestures — names, projects, numbers, links. If you can't cite one, don't write the reply
  • Stand alone — readers may not see the original tweet; reply must make sense on its own
  • Match soul voice if soul files are populated
Self-edit pass (do this for every reply before finalizing)

For each draft reply, score 1–5 on each:

  • Specific: cites a name/number/project/claim?
  • Standalone: makes sense without reading the parent?
  • Non-sycophantic: passes the banned-phrase list?
  • Voice-matched: sounds like the soul files (or neutral-direct if no soul)?

If any score is < 4, rewrite that reply once before moving on. If the rewrite still scores < 4, drop that tweet from the list and pull the next-ranked candidate from step A2.

A4. Notify

Send via ./notify with this format (link first so the operator can open the source quickly):

*Reply Maker — ${today}*

*1.* https://x.com/handle/status/123  (@handle, 42m ago, 18💬)
> [first ~80 chars of tweet]…
why: [one-line reason this is reply-worthy]
A: [evidence-add reply]
B: [frame-challenge reply]

*2.* …
… (5 total, or fewer with REPLY_MAKER_DEGRADED if skip gate trimmed below 5)

source-status: xai=ok|fail|skip, memory=N, websearch=ok|fail|skip

If zero candidates survive the skip gate from any source, send a single REPLY_MAKER_EMPTY — [one-line reason] notification and stop.

Otherwise, after notifying, save the drafts and offer a revision (see Save drafts + offer revision).

A5. Log

Append to memory/logs/${today}.md under the shared ### reply-maker heading (see Log below), using the Mode A template.


Show full SKILL.md (1,092 more words)Show less

Mode B — From-Logs Engagement

Turn flagged engagement opportunities from recent logs into ready-to-post replies — read the last 7 days of logs, draft specific responses, send as copy-paste-ready output. This mode makes no outbound API calls — no X.AI curl, no WebSearch — it works purely from local memory/ files.

Projects-of-interest list: if memory/topics/projects-of-interest.md exists, treat the project names listed there as the things to watch for mentions, cosigns, attributions, and fork moments. If the file is missing or empty, fall back to any project names that appear in recent logs or in MEMORY.md. If a filter token was passed (from-logs @handle or from-logs <project>), narrow the scan to opportunities involving that handle/project.

B1. Collect unactioned engagement opportunities

Read memory/logs/ for the last 7 days. Look for:

  • Log entries flagging engagement opps (e.g. "Engagement opps: N flagged" with N > 0) — extract the named handles/accounts
  • Any person who cosigned, mentioned, or attributed one of the operator's projects-of-interest
  • GitHub attribution or fork moments not yet acknowledged
  • Entries in MEMORY.md "Known Follow-ups" explicitly flagging engagement opps
  • Cosigns or mentions surfaced in mention-radar, fetch-tweets, or reply-maker runs

Build a list: { person/account, context, what_they_did, link_if_known, days_ago }

B2. Filter and prioritize

Apply these rules:

  • Drop any opp older than 14 days — window is likely closed
  • De-dupe: skip opps where recent logs already show "replied to @X" or "acknowledged" for that handle
  • Rank by: recency (fresher first) × leverage (high-follower or influential account first)
  • Cap at 5 opportunities
B3. Draft ready-to-post responses

For each opportunity:

  • Type: X reply / X DM / GitHub comment / X post
  • Target: @handle or URL
  • Draft text: exact text, ready to copy-paste
  • Keep under 280 chars for X replies; longer is fine for DMs or GitHub comments
  • Voice: if soul/SOUL.md and soul/STYLE.md are populated, match that voice; otherwise use a clear, direct, neutral tone. Either way: acknowledge without groveling, no "thanks so much for the kind words!" — just the actual response.
B4. Check for staleness

If any opportunity is 5+ days old, prepend aging to that entry in the output.

B5. Skip if empty

If after filtering there are zero unactioned opps, log ENGAGEMENT_ACT_SKIP: no unactioned opps (under the ### reply-maker heading) and exit without sending a notification.

B6. Write output to a temp file, then send via ./notify -f
*Reply Maker (from-logs) — ${today}*

*1. @handle* (N days ago) — [one-line summary of what they did]
link: [URL or "no link found"]
type: [X reply / X post / DM / GitHub comment]
draft: "[ready-to-post text]"

*2. @handle* ...

[if any opps are 5+ days old:]
some opps aging — act or drop

Write this to /tmp/reply-maker-from-logs.md then run ./notify -f /tmp/reply-maker-from-logs.md.

After notifying, save the drafts and offer a revision (see Save drafts + offer revision).

B7. Log

Append to memory/logs/${today}.md under the shared ### reply-maker heading (see Log below), using the Mode B template.


Save drafts + offer revision (both modes)

After a normal run (Mode A or B) has drafted and notified replies, do two things so the operator can refine them from Telegram. Skip both when the run sent nothing (REPLY_MAKER_EMPTY, or Mode B's ENGAGEMENT_ACT_SKIP).

  1. Persist the drafts to a stable path a later revise: run can reload:

    bash
    mkdir -p memory/drafts

    Write the full draft body you just sent — all selected tweets with their A/B options (Mode A), or the ready-to-post list (Mode B) — to memory/drafts/reply-maker-latest.md, overwriting any previous file. Only the newest draft is revisable.

  2. Offer a revision — a separate ./notify (force_reply can't share a message with inline buttons):

    bash
    ./notify "Want to refine these replies? Reply with a change and I'll revise them." \
      --force-reply --placeholder "e.g. make them shorter" \
      --context "reply-maker::revise"

    The reply routes back as var="revise:<instruction>" and re-dispatches this skill into the Revise branch.

    Dedup — once per produced draft. Before offering, scan the last ~2 days of memory/logs/ for a FORCE_REPLY_OFFERED: revise line dated ${today}; if present, skip the offer. When you send it, append the marker under the run's ### reply-maker entry:

    - FORCE_REPLY_OFFERED: revise

Banned sycophancy phrases

Edit this list as tastes change — any draft reply (either mode) containing one of these (openings or closings) must be rewritten:

  • Openings: "Great point", "Love this", "100%", "This 👆", "Couldn't agree more", "So well said", "💯"
  • Closings: "Curious to hear your thoughts!" (engagement-hook noise)

Log

Append one entry to memory/logs/${today}.md under a single ### reply-maker heading, with a **Mode:** discriminator line naming which branch ran.

Mode A (reply drafting):

### reply-maker
- **Mode:** A (reply drafting)
- **Var:** ${var:-<empty>}
- **Candidates collected:** N
- **Survived skip gate:** N
- **Replies generated:** N×2
- **Handles:** @h1, @h2, …
- **Source status:** xai=ok|fail|skip, memory=N, websearch=ok|fail|skip
- **Notification:** sent | degraded | empty
- **Tweet URLs:** [list, for future-day dedup]

The Tweet URLs line is what tomorrow's run reads to avoid duplicate replies — keep it consistent.

Mode B (from-logs engagement):

### reply-maker
- **Mode:** B (from-logs engagement)
- **Opps found:** N unactioned (scanned last 7 days of logs)
- **Drafted:** N responses
- **Handles:** @handle1, @handle2, …
- **Notification sent:** yes
- ENGAGEMENT_ACT_OK

If skipped: ENGAGEMENT_ACT_SKIP: <reason> (still under ### reply-maker).

Revise (Telegram force-reply):

### reply-maker
- **Mode:** revise
- **Instruction:** [the operator's revision instruction]
- **Base draft:** memory/drafts/reply-maker-latest.md (reloaded + re-saved)  (or: none — nothing to revise)
- **Notification:** sent

Fetching

XAI_API_KEY is injected into this skill's environment (declared in requires:). It is present and valid. Mode A's primary fetch path is a direct curl to https://api.x.ai/v1/responses with Authorization: Bearer {XAI_API_KEY}. There is no network sandbox blocking this; earlier versions of this skill claimed there was — that is stale and wrong. Just make the call.

You MUST attempt the direct curl before any fallback. The rules:

  1. Check, don't assume. Run [ -n "$XAI_API_KEY" ] && echo KEY_PRESENT || echo KEY_UNSET. If KEY_PRESENT (it will be), you are required to try Path A.
  2. Allow enough time. The x_search call typically takes 30–120s (it searches X live). When you invoke the Bash tool for the curl, set the tool's timeout to at least 180000 (180s), and add --max-time 150 to the curl itself so it fails cleanly rather than hanging. A curl that is slow is not a missing key — do not treat a timeout as "key unavailable".
  3. Capture the HTTP status so the fallback decision is based on fact, not assumption. Build the payload to the fixed file /tmp/xai-rm-payload.json first (see the three jq -n --arg shapes in A1), then send it with -d @/tmp/xai-rm-payload.json:
    bash
    HTTP=$(./secretcurl -s -o /tmp/xai-rm.json -w '%{http_code}' --max-time 150 -X POST "https://api.x.ai/v1/responses" \
      -H "Content-Type: application/json" -H "Authorization: Bearer {XAI_API_KEY}" -d @/tmp/xai-rm-payload.json)
    echo "xai http=$HTTP bytes=$(wc -c </tmp/xai-rm.json)"
    Then parse /tmp/xai-rm.json with the standard jq extractor. HTTP=200 with a non-empty body → use it (xai=ok).
  4. Fall back only on a real failure, and record the true reason — never write "XAI_API_KEY unavailable" when the key was set. Use one of: key-unset (only if step 1 said KEY_UNSET), http-<code> (non-2xx), empty (200 but no tweets parsed), timeout (curl exceeded --max-time).

WebSearch and the memory-log candidate pool are last-resort fallbacks only — lower quality (WebSearch favours old high-engagement tweets). Never reach for them while the key works. Mode B is fetch-free by design: it reads only local memory/ files, so it makes no curl and no API call; ./notify -f still handles delivery via .pending-notify/ if needed.

Environment Variables Required

  • XAI_API_KEY — X.AI API key for Grok's x_search tool. Declared in requires:, so it is injected into this skill's environment and is Mode A's primary fetch path. If it is ever unset, Mode A degrades to the memory-log pool + WebSearch at lower quality. Mode B requires no environment variables and uses only local memory files and ./notify.

© aeonfun, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/reply-maker of aeonfun/aeon.

Open the folder on GitHubat commit f252074

Compare with similar skills

Reply Maker next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Reply Maker compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Reply Maker this skillaeonfun/aeon767—~6kAutomated safety check: PassMIT
X/Twitter Research via Grokartwist-polyakov/polyakov-claude-skills206—~1.8kAutomated safety check: NotesMIT
ContentGerstep/cybos104—~616Automated safety check: PassNone
Social Contentfreekmurze/dotfiles1k22 repos~2.1kAutomated safety check: PassNone
Typefullyfreekmurze/dotfiles1k1 repos~3.4kAutomated safety check: NotesNone
Changelog Social RecapFlorianBruniaux/claude-code-ultimate-guide6.1k—~1.8kAutomated safety check: NotesCC-BY-SA-4.0

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Questions about Reply Maker

What does Reply Maker do?

Draft copy-paste-ready X replies - two options per reply-worthy tweet from tracked accounts, topics, or lists (default), or ready-to-post responses to engagement opps in recent logs (from-logs). Reply Maker is an agent skill from aeonfun/aeon.

When should I use Reply Maker?

Reply Maker fits situations like: tasks that involve Social media posts.

How do I install Reply Maker in Claude Code?

Run `npx skills add aeonfun/aeon --skill reply-maker -a claude-code`. Or copy the skill folder (skills/reply-maker in aeonfun/aeon) into .claude/skills/reply-maker in your project. Claude Code loads it when a task matches its description.

How do I install Reply Maker in Codex?

Run `npx skills add aeonfun/aeon --skill reply-maker -a codex`. Or copy the skill folder (skills/reply-maker in aeonfun/aeon) into .agents/skills/reply-maker in your project. Codex loads it when a task matches its description.

Can I use Reply Maker in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aeonfun/aeon --skill reply-maker -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/reply-maker, .gemini/skills/reply-maker, .github/skills/reply-maker and .opencode/skills/reply-maker in your project.

What does Reply Maker need to run?

Going by SKILL.md and its folder, Reply Maker needs the command-line tools its instructions call (jq and make) and credentials named XAI_API_KEY. Our summary lists: A credential in XAI_API_KEY.

Does Reply Maker access the network?

SKILL.md names 2 domains. In commands or code: api.x.ai and x.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Reply Maker safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Reply Maker use?

Reply Maker is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Reply Maker use?

About 6k tokens (SKILL.md is roughly 24k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Reply Maker?

Skills that share tags, products or a category with Reply Maker: X/Twitter Research via Grok (artwist-polyakov/polyakov-claude-skills, 206 stars), Content (Gerstep/cybos, 104 stars), Social Content (freekmurze/dotfiles, 1k stars) and Typefully (freekmurze/dotfiles, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Reply Maker?

aeonfun (a GitHub organization) maintains it in aeonfun/aeon, which has 767 GitHub stars. The repository holds 82 skills in this directory. The repository was last updated on October 6, 2026.

Source: aeonfun/aeon on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.