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 skills add naveedharri/benai-skills --skill competitor-radargit clone --depth 1 https://github.com/naveedharri/benai-skillsWrote 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/naveedharri/benai-skills/competitor-radar)<a href="https://agentmods.dev/skills/naveedharri/benai-skills/competitor-radar"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/competitor-radar/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.
<a href="https://agentmods.dev/skills/naveedharri/benai-skills/competitor-radar"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/competitor-radar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Memory Poisoning · line 31 Skill injects content designed to persist in agent memory or context across interactions. Persistent injection can alter agent behavior long after the initial interaction.Fix: Do not allow untrusted input to persist in agent memory or context. Validate all content before storing and implement memory isolation between sessions.
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 | $0.00123 | $0.00724 |
| Opus 5 | $0.00062 | $0.00362 |
| Sonnet 5 | $0.00025 | $0.00145 |
| Haiku 4.5 | $0.00012 | $0.00072 |
Grade A, and why
competitor-radar 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Radar
Tracks a fixed roster of competitors weekly and renders one branded HTML dashboard: follower counts, posting cadence, median engagement, week-over-week subscriber growth, standout post of the week, and SEO, per platform. Two tabs: Demo (a sample niche) and Actual (real competitors, with a focus/blur toggle so only "you" shows on camera).
Files
config.json: the stable roster (who to track, handles per platform, platforms to include, Apify actor ids). Edit to add/remove competitors.radar_data.js: the weekly artifact.WEEK_ENDING,AV(base64 avatars, stable), andDATA({demo, actual}). The refresh step rewrites this.assets/template.html: the fixed dashboard shell (CSS, render logic, tabs, focus mode). Never regenerate; carries a single/*__RADAR_DATA__*/marker.scripts/build_dashboard.py: deterministic build (template + radar_data.js toindex.html). No network.
Weekly refresh workflow
- Read
config.json(roster) and the currentradar_data.js(last week's numbers, needed for week-over-week deltas). - Gather fresh data for each creator per
references/data-sources.md. Do NOT fabricate; mark missing asnull. - Compute deltas per
references/data-sources.md. - Rewrite
radar_data.jswith the newWEEK_ENDINGand refreshedDATA. Handle avatars perreferences/data-sources.md. - Build:
python3 scripts/build_dashboard.py <skill_dir> ../index.html. - Deploy and Slack the live URL per
references/deploy.md.
Self-improvement
This skill is never finished. Improve it as you use it.
- When the user corrects how a step was done, update the relevant reference file (
references/data-sources.md,references/deploy.md) or this SKILL.md so the correction sticks. Do not just fix it for this run. - When a correction is a hard rule ("always X", "never Y"), add it as a permanent rule here.
- When the user says an output was genuinely good, save it to
references/examples/so it becomes a model for future runs. - Keep the skill small: when you add something, run the deletion test and cut anything that no longer changes behavior.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 40 lines · 123 tokens per session scan A 18ae9330337d
competitor-radar is a skill published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed today), licensed MIT. It adds 123 tokens to every session and 724 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 2026-09-05.
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