| name | content-ideas |
| version | 2.2.0 |
| description | Your For You page for content creators. Scrapes tracked competitors across social media platforms, scores what's performing, and turns it into actionable, differentiated content ideas backed by real engagement data. Use this whenever the user wants competitor/creator research, a content feed or "for you" page, trending-topic ideas in their niche, to see what's working on social, to track what creators are posting, or to generate video/post briefs from what's performing — even if they don't say "find ideas." First run walks through setup.
|
| argument-hint | [topic filter] |
| user-invocable | true |
| allowed-tools | Bash, Read, Write, AskUserQuestion |
| metadata | {"requires":{"env":["SCRAPECREATORS_API_KEY"],"bins":["python3"]}} |
content-ideas
Your For You page. Scrapes every platform where your tracked creators publish,
scores what's performing, and turns it into content ideas you can act on.
Designed to run daily — each run creates a dated feed under $CONTENT_HOME/research/.
The output is a single self-contained HTML page (two tabs: Posts — one
sortable, filterable feed merging tracked-account posts and discovered niche
outliers — and Ideas) that you can open in a browser, react to, and
keep. Reactions are captured for future personalization.
Resolve the skill directory
Everything this skill runs lives under its own folder. The skill installs the
same way on Claude Code and Codex, so resolve SKILL_DIR against both plugin
caches (and a plain repo checkout) once, before anything else:
SKILL_DIR="$(ls -d "$HOME/.codex/plugins/cache/"*/content-ideas/*/skills/content-ideas/ "$HOME/.codex/skills/"*/skills/content-ideas/ 2>/dev/null | sort -V | tail -1)"
SKILL_DIR="${SKILL_DIR%/}"
if [ -z "$SKILL_DIR" ] || [ ! -f "$SKILL_DIR/scripts/scrape.py" ]; then
CLAUDE_ROOT="$(ls -d "$HOME/.claude/plugins/cache/content-ideas/content-ideas/"*/ 2>/dev/null | sort -V | tail -1)"
CLAUDE_ROOT="${CLAUDE_ROOT%/}"
[ -n "$CLAUDE_ROOT" ] && [ -f "$CLAUDE_ROOT/skills/content-ideas/scripts/scrape.py" ] && SKILL_DIR="$CLAUDE_ROOT/skills/content-ideas"
fi
if [ -z "$SKILL_DIR" ] || [ ! -f "$SKILL_DIR/scripts/scrape.py" ]; then
for dir in "${CLAUDE_PLUGIN_ROOT:-}/skills/content-ideas" "${CLAUDE_PLUGIN_ROOT:-}" "${GEMINI_EXTENSION_DIR:-}/skills/content-ideas" "./skills/content-ideas" "."; do
[ -n "$dir" ] && [ -f "$dir/scripts/scrape.py" ] && SKILL_DIR="$dir" && break
done
fi
echo "$SKILL_DIR"
If you can already see this file's path, just use its directory. The two
scripts you'll call are $SKILL_DIR/scripts/scrape.py and
$SKILL_DIR/scripts/generate_feed.py. The renderer template is
$SKILL_DIR/assets/for-you-template.html (the generator finds it automatically).
Resolve the content home
All persistent files this skill reads and writes — the brand/ profile and the
dated research/ runs — live under one stable base, never the current
working directory. The skill runs daily and is invoked from anywhere, so the
base must be the same every time or it loses the profile and the run history.
Resolve it once and capture the concrete path:
CONTENT_HOME="${CONTENT_HOME:-$HOME/Documents/Content}"
mkdir -p "$CONTENT_HOME/brand" "$CONTENT_HOME/research"
echo "$CONTENT_HOME"
Throughout this guide every brand/... and research/... path is relative to
$CONTENT_HOME (so brand/profile.md means $CONTENT_HOME/brand/profile.md).
Use the printed absolute path for every Read/Write of those files — the
file tools don't expand shell variables, so writing a bare brand/profile.md
would land it in the wrong directory. (Credentials stay separate, in
~/.config/content/.env.) The scrape/generate scripts read CONTENT_HOME
themselves, so a relative research/{today} passed to them resolves here too.
Step 0: First-run setup
Run this before anything else, even if the user gave a topic. Detect first
run by checking whether ~/.config/content/.env exists and contains
SETUP_COMPLETE=true. Check silently. If it's already set up, skip to Step 1.
0a. Welcome + API key
Setup has three quick parts: an API key, your profile (built from your own
channels), and the competitors you want to track. Only the key is required —
the rest the skill bootstraps for you and you can refine any time. Nothing to
install; one ScrapeCreators API key covers all four platforms — X, Instagram,
TikTok, and YouTube (including transcripts).
Show this as a normal message, then call AskUserQuestion (don't repeat the
welcome inside the modal):
I turn your social presence into a daily For You feed: I build a profile from
your own channels, track the competitors you pick, and surface what's
performing as content ideas backed by real engagement. I just need a
ScrapeCreators API key (one key covers all four platforms; 100 free calls, no
card).
AskUserQuestion — "Add your ScrapeCreators API key?"
- Open scrapecreators.com to grab a free key
- I'll paste a key now
- Skip for now
If they pick "Open scrapecreators.com", run open https://scrapecreators.com,
then ask them to paste the key. When the user pastes a key, write
~/.config/content/.env (create dirs; append, don't clobber other keys):
SCRAPECREATORS_API_KEY={key}
SETUP_COMPLETE=true
If they skip, write only SETUP_COMPLETE=true.
0b. Manual alternative
If they'd rather configure by hand, tell them to add those two lines to
~/.config/content/.env. Offer to write the file if they paste the key here.
0c. Build your brand profile
This is what personalizes everything: ideas get framed against your niche,
pillars, and goal, and checked against what you've already posted. Build it from
the user's own presence rather than a long questionnaire.
Ask for their own channels (AskUserQuestion: "Set up your profile now?" →
I'll share my handles / Skip — I'll add it later). When they share
handles — free-form across any platforms (@me on X, a YouTube channel, a
TikTok, etc.) — normalize them into the {platform: [handle]} shape and scrape
them like competitors, but over a much wider window (--days 90, the max) so
you characterize their work from a full quarter, not just recent posts:
python3 "$SKILL_DIR/scripts/scrape.py" \
'{"x": ["me"], "youtube": ["@mychannel"]}' \
--pillars "" --days 90
From the returned posts (plus comments/transcripts), draft the profile:
- Niche, Audience, Voice Notes — infer from recurring topics, framing, tone.
- Content Pillars — the 3–5 themes their posts actually cluster into. These
drive
--pillars on every future run, so get them right.
- My Social Profiles — handle, follower count, bio, and a one-line content-
style note per platform, taken from the scrape.
- Target Platforms / Research Channels — the platforms they're active on.
- Search Terms — concrete keywords from their top topics.
Two things you can't scrape — ask (AskUserQuestion), then fold the answers in:
- Content Goal — why they post (lead gen / awareness / growth / thought
leadership / selling…), where they drive traffic, and what they're promoting.
- Pillar confirmation — show the 3–5 pillars you inferred and let them
edit or confirm before writing.
Write brand/profile.md per the schema in FILE-SCHEMAS.md. If the scrape
returned enough of their own posts, also write an initial brand/my-content.md
(performance summary, what's working, topics covered, and audience requests
distilled from their comments) — this powers anti-cannibalization and the "your
audience is asking for" banner from day one.
If they skipped (or there's no API key yet to scrape with), don't block:
build a minimal brand/profile.md from a 2–3 question Q&A (niche, rough
pillars, goal), note that re-running setup with a key auto-enriches it, and move
on.
0d. Track competitors
Ask who they want to track (AskUserQuestion: list them now / skip and use an
example). If they list handles, create brand/tracked-accounts/{platform}.md
files per the schema in the plugin's FILE-SCHEMAS.md. If they skip, run a
small example so they see the shape, and tell them they can add real
competitors later.
End of first-run setup. Then continue with the user's original request.
Step 1: Load context
1a. Ingest the previous run's feedback into taste memory
Before anything else, fold the last run's reactions into your memory — this
is what makes each run better than the one before. List the dated subfolders of
$CONTENT_HOME/research/ (YYYY-MM-DD) and take the most recent one. If it has a
feedback.json, read it and distill each entry in reviews[] (▲ "more like
this" / ▼ "less" / a note) into the generalizable taste signal, not the
one-off:
- "▲ on three contrarian takes in the user's niche" → "gravitates toward
contrarian takes"; "▼ on listicles" → "listicle formats don't land." A
note often states the reason directly — use it.
- Record these to your project memory (the auto-memory you maintain) as the
user's content taste — the same place 1b recalls from. Update an existing
taste note rather than duplicating it; let a single ▼ inform, not override, an
established preference. Don't record one-off reactions with no pattern,
anything already obvious from
brand/profile.md, or post/run specifics (those
live in research/). Taste only.
If there's no prior dated folder, no feedback.json, or no reactions in it,
skip silently. If auto-memory isn't available in this environment, skip too —
the reactions stay in feedback.json for whenever it is. (The current run's
reactions are ingested by the next run, the same way — there's no end-of-run
distillation step.)
1b. Recall taste and load brand context
Read whatever brand context exists (all optional — degrade gracefully):
brand/profile.md — niche, pillars, search terms, content goal, audiencebrand/tracked-accounts/*.md — tracked creators per platformbrand/my-content.md — the user's own content performance + audience requests
Recall the user's content taste from your memory. This skill stores an
evolving taste profile in your project memory (the auto-memory you maintain). Before generating ideas, recall what you know about what this
user gravitates toward — preferred topics, formats, angles, creators they keep
saving, and what doesn't land for them. If relevant taste signals are already
surfaced in context, use them; if not and memory is available, look for taste
notes tagged for this skill. This is the single most important personalization
input: engagement metrics measure what audiences like, taste memory measures
what this user likes. If auto-memory isn't available, fall back to engagement
signals alone (and to brand/my-content.md if present).
If there are no tracked accounts and no topic filter, ask for handles or a
topic before scraping.
1c. Refresh your own content (my-content.md)
Before generating ideas, bring brand/my-content.md up to date — this is the
per-run counterpart to the one-time build in Step 0c, and it's what keeps
anti-cannibalization and the "your audience is asking for" banner honest as the
user keeps posting. (my-content.md is declared updated each run in
FILE-SCHEMAS.md; this is the step that does it.)
Take the user's own handles from the ## My Social Profiles section of the
brand/profile.md you just loaded, normalize them into the {platform: [handle]}
shape, and re-scrape them over a window wide enough to catch their own cadence
(--days 30 — a creator's own posts are sparser than the merged competitor
feed, but keep it "recent," not the 90-day profile build from Step 0c):
python3 "$SKILL_DIR/scripts/scrape.py" \
'{"x": ["me"], "youtube": ["@mychannel"]}' \
--pillars "<pillars from profile.md>" --days 30
The scraper already pulls comments on the top posts, so the returned data
carries the audience replies you need. Rewrite brand/my-content.md from it per
the schema in FILE-SCHEMAS.md (performance summary, what's working / not,
topics covered, and audience requests distilled from the comments) — it's
replaced, not appended. Use this fresh version, not the copy you read in 1b, for
the rest of the run.
Best-effort — never block the feed. If profile.md has no own handles (the
user skipped profile setup), or the scrape returns nothing or errors, keep the
existing my-content.md and continue. This refresh is an enrichment, not a gate.
Step 2: Create the daily run folder
List existing dated subfolders of $CONTENT_HOME/research/ (YYYY-MM-DD). The most recent
one that is not today is the last-run date — pass it as --since in Step 3
so the scrape only keeps posts on/after that day. If there are no prior dated
folders, there's no --since.
Either way, the scraper enforces a recency window so the daily feed never
surfaces stale posts: by default it keeps only the last 7 days (--days).
--since can only narrow that window, never widen it — so first runs and
long-gap runs are both bounded to a week by default. (The script's hard cap is
90 days; for the daily feed keep it tight — a month at most. The 90-day window
is for one-off profile builds in Step 0c, not the daily feed.)
Create $CONTENT_HOME/research/{today}/.
If $CONTENT_HOME/research/{today}/feed-data.json already exists, ask whether to:
- Refresh — re-pull and rebuild (reuse the same
--since / --days)
- Expand — widen the window: drop
--since and/or raise --days (keep the
feed within ~30 days) when the user wants more than the last week
- View — just (re)open the existing feed (skip to Step 6)
Step 3: Scrape competitors
Build a JSON object mapping each platform to its tracked handles. Pass content
pillars (from brand/profile.md, or the user's niche/topic) via --pillars so
the script scores relevance, and the last-run date via --since. Leave --days
at its default (7) unless the user asks for a wider window, then raise it (max
31).
python3 "$SKILL_DIR/scripts/scrape.py" \
'{"x": ["h1","h2"], "instagram": ["h3"], "youtube": ["@h4"]}' \
--pillars "<the user's content pillars>" \
--since 2026-04-15 \
--days 7
Tell the user this takes a few minutes; progress streams to stderr. The script
fetches all accounts in parallel, drops anything outside the recency window,
scores engagement and relevance, flags outliers, and pulls comments/transcripts
on top posts. It returns:
{ "results": { "x": { "h1": [ {post}, ... ] } }, "errors": [] }
Each post has text, url, author, date, platform, engagement,
score (weighted), relevance (0–1 vs pillars), baseline (Nx the account
average), outlier (bool), and — on top posts — comments / transcript.
On errors: report which accounts failed and proceed with what came back.
Ad-hoc: fetch specific posts by URL
When the user hands you specific post URLs (a competitor's viral post, a link
they saw), use URL mode instead of profile mode. It returns a flat [post]
array with the same shape:
python3 "$SKILL_DIR/scripts/scrape.py" urls "https://x.com/u/status/1" "https://www.tiktok.com/@u/video/2" --pillars "..."
Step 4: Review the scored data
The script pre-computes score, baseline, relevance, and outlier.
Identify the top-performing posts and the topics/themes/angles driving
engagement — especially high-relevance ones. This is the raw material for the
Ideas tab.
Step 5: Build the feed
Two tabs. Everything shown has proven engagement. Build a FEED_DATA object
and write it (Step 6). Field-by-field structure is in the plugin's
FILE-SCHEMAS.md (feed-data.json).
Tab 1 — Posts. One flat posts[] array merging two sources into a single
sortable, filterable feed (the page handles sorting and grouping client-side —
do not pre-sort or pre-group):
-
Tracked-account posts — every post from tracked accounts (no engagement
gate). Set performance / performanceDirection vs the account baseline
(e.g. "+210% vs baseline", "up").
-
Discovered niche outliers — statistical outliers (outlier: true, z-score
2+, or baseline 2x+). Set zScore and a why line.
Per post, regardless of source, provide: a 1–3 sentence text summary,
url, handle + displayName (creator filter), platform, an engagement
object, a hook callout when notable, and the two fields that make the feed
work — timestamp (ISO 8601, drives Recent sort + relative time) and
sortValue (numeric total engagement/reach, drives the default Popular
sort). A post is flagged as an outlier (intensity-scaled badge + accent bar)
whenever it has a zScore or performanceDirection: "up" — so a tracked
post that beat its baseline shows as an outlier too.
Tab 2 — Ideas. The one place you editorialize (label it as AI suggestion).
Generate up to 10 ideas, each with: a specific differentiated angle, real
evidence from competitor performance, and clear differentiation from what
competitors already covered.
For the generative craft — turning a topic into a differentiated angle, writing
hooks, classifying funnel stage (TOFU/MOFU/BOFU), aligning CTAs, repurposing
across platforms, and producing a full brief — read
references/content-strategy.md. The short version to keep in mind while
building this tab:
- Make YOUR version, never repackage. A good angle answers at least one of:
what do you know the original creator doesn't (expertise), what have you done
the audience hasn't seen (access), or where do you disagree (contrarian)?
- Anti-cannibalization. When
brand/my-content.md exists, don't re-pitch a
topic the user already covered unless the angle has a genuine differentiator
(more depth, different format, an update, a response to feedback). Note prior
coverage explicitly.
- Own-audience demand wins. Requests from the user's own audience
(
brand/my-content.md) outrank competitor signals — foreground them in the
brief's "why now."
- Taste memory biases selection. An idea that aligns with the taste signals
you recalled in Step 1 (topics/formats/angles this user gravitates toward)
is a stronger pick than one justified by engagement alone — and worth calling
out ("this fits a pattern you keep coming back to"). Conversely, deprioritize
anything that matches a recorded "doesn't land" signal.
Step 6: Write and open the feed
Write the feed data to $CONTENT_HOME/research/{today}/feed-data.json — a JSON object with
keys meta, posts, ideas (see FILE-SCHEMAS.md).
Do not write HTML yourself; the generator embeds this JSON into the
template.
Then render it. Default to the live server (lets the user react to items, which
saves to feedback.json for future personalization):
python3 "$SKILL_DIR/scripts/generate_feed.py" "$CONTENT_HOME/research/{today}"
This starts a local server and automatically opens the feed in the user's
default browser. Still hand the user the http://localhost:<port> URL the
command prints, so they can reopen it if the tab closes. (Pass --no-browser to
suppress the auto-open; the URL is printed either way.) The command runs in the
foreground until the user stops it with Ctrl+C, so run it in the background if
you need to keep working.
In a headless/no-display environment, write a self-contained file instead and
point the user at it (the page lets them download their reactions):
python3 "$SKILL_DIR/scripts/generate_feed.py" "$CONTENT_HOME/research/{today}" --static
Then present a short text summary (post count, how many are outliers, a couple
of standout posts) and the page location.
Step 7: Offer next steps
The user reacts to the feed in the browser; their reactions save to
research/{today}/feedback.json on their own — automatically in server mode,
or via the page's download button in static mode. There's no "done" signal and
nothing for you to read or distill now: the file just accumulates reactions,
and the next run folds them into taste memory at Step 1a. This keeps the
workflow simple and, crucially, captures reactions the user makes after this
conversation has ended.
Offer to: dig deeper on any idea, add/remove tracked accounts, or rerun with a
different topic focus.
Notes
- Reactions / feedback → taste memory. The feed page lets the user mark
items (▲ more like this / ▼ less / a note) across both tabs. In server
mode these save to
research/{date}/feedback.json automatically as the user
clicks; in static mode the user downloads that file into the run folder. The
file is just an accumulating list of reactions — no status, no submit step.
The next run reads the previous run's feedback.json at Step 1a and
distills it into your project memory so future runs are personalized — there
is no taste file; taste lives in auto-memory.
- No API key = no run. Both profile and URL mode require
SCRAPECREATORS_API_KEY — every platform, including YouTube transcripts, goes
through ScrapeCreators. If the key is missing, the script returns an error;
stop and show setup instructions rather than inventing data.
