peec-content-intel

peec-content-intel is a skill for Claude Code from AntonioBlago/peec-ai-skills. It costs 117 tokens per session (3,087 once invoked), scanned A, original, MIT.

A research workflow that turns one weak Peec AI prompt into a publish-ready content brief. It combines AI-visibility data, website and search data, and forum discussions to explain what the content should cover.

In plain words
What is it for?
Use it for a Peec prompt with enough measurement data when the brand is losing visibility. It produces sub-queries, buyer problems, competitor findings, an outline, keywords, and outreach targets.
Why use it?
It replaces guesswork about keywords, customer problems, competitors, and article structure. It also checks the intended page type, such as a landing page, so the brief matches the audience’s stage.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter. Also seen: mentions subagents.

Good fit Use it for a Peec prompt with enough measurement data when the brand is losing visibility. It produces sub-queries, buyer problems, competitor findings, an outline, keywords, and outreach targets.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/antonioblago/peec-ai-skills/peec-content-intel
Install

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.

Any agent
npx skills add AntonioBlago/peec-ai-skills --skill peec-content-intel
Clone the repo
git clone --depth 1 https://github.com/AntonioBlago/peec-ai-skills

Made for: Claude Code.

Wrote 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.

agentmods badge for peec-content-intel

README.md
[![agentmods](https://agentmods.dev/badge/skills/antonioblago/peec-ai-skills/peec-content-intel/github.svg)](https://agentmods.dev/skills/antonioblago/peec-ai-skills/peec-content-intel)
Your own site
<a href="https://agentmods.dev/skills/antonioblago/peec-ai-skills/peec-content-intel"><img src="https://agentmods.dev/badge/skills/antonioblago/peec-ai-skills/peec-content-intel/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.

agentmods 80×15 button for peec-content-intel

Your own site · 80×15
<a href="https://agentmods.dev/skills/antonioblago/peec-ai-skills/peec-content-intel"><img src="https://agentmods.dev/badge/skills/antonioblago/peec-ai-skills/peec-content-intel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 117 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,087 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00117 $0.03087
Opus 5 $0.00059 $0.01543
Sonnet 5 $0.00023 $0.00617
Haiku 4.5 $0.00012 $0.00309

Measured 11d ago against content hash 6489b18f95e7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

peec-content-intel 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 11d 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.

skills/peec-content-intel/SKILL.md · 285 lines

How it starts

The opening of the file, as written. The whole thing — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Peec Content Intel

Role

For one Peec prompt that the brand is losing, produce one publish-ready content brief: sub-queries, verbatim buyer pains, competitor breakdown, outline, focus keywords, and outreach targets.

Input

  • project_id — Peec project (read from setup_state.json per pre-flight; do not re-resolve)
  • prompt_id — the target prompt (must have ≥24h of data)
  • optional date_range — default last 28 days
  • language — read from setup_state.json (prompt_language); user can override per-run, but never default to en silently
  • target_country — read from setup_state.json; drives forum source picks (DE→reddit/r/de+gutefrage+t3n, AT→reddit+derstandard, US→reddit+quora, CH→reddit/r/de+r/fr) and SERP/GSC market filters
  • page_typerequired. Must be one of the values in setup_state.page_type_taxonomy. If the user doesn't pass it, the skill infers the best fit from the funnel stage of the target prompt + competitor top-url classifications, then ASKS the user to confirm ("Suggested page_type: landing_page (Decision-stage, competitors use PRODUCT_PAGE). OK? [y/n/override]"). Never guess silently — a brief with the wrong page_type is a wasted publish cycle.
  • business_type — read from setup_state.json. Used to validate page_type selection against the allowed taxonomy matrix.
  • audience — read from setup_state.json. audience.primary and audience.pain_points feed the brief's "Why this page wins" + "Voice / tonality" sections directly; do not restate them in the brief prose, USE them.

Output

One markdown brief per prompt, saved at briefs/<YYYY-MM-DD>_<prompt-slug>/brief.md, plus the raw data next to it (competitor-urls.json, forum-pains.json, scoring.json). No dashboards.

Every brief starts with a front-matter block that downstream skills (peec-report, peec-learn) consume for attribution:

---
brief_id:      <YYYY-MM-DD>_<prompt-slug>
prompt_id:     pr_xxxxxx
business_type: b2b-service
page_type:     landing_page           # MUST be in setup_state.page_type_taxonomy
target_url:    /seo-retainer          # planned publish path
funnel_stage:  Decision                # from the prompt's topic
audience:
  primary:     "Shop-Owner DACH, 3-20 MA, Shopify"
  pain_hook:   "3 Agenturen gewechselt, keine Ergebnisse"
success_metric_4w:
  prompt_visibility: "0% → ≥15%"
  zone_visibility:   "4% → ≥20%"
---

Read the full file on GitHub · 285 lines

Changes

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.

  1. 11d ago First seen · 285 lines · 117 tokens per session scan A 6489b18f95e7

Subscribe to this mod's changes

peec-content-intel is a skill published in the GitHub repository AntonioBlago/peec-ai-skills (9 stars, last pushed 4mo ago), licensed MIT. It adds 117 tokens to every session and 3,087 once invoked, about $0.0006 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 2026-08-31.

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