social-signal-intelligence

social-signal-intelligence is a skill for Claude Code from aniganti/pm-superpowers. It costs 43 tokens per session (750 once invoked), scanned A, original, MIT.

A method for turning public X/Twitter posts, profiles, trends, and product conversations into product insights. X, formerly called Twitter, is a social network where people publicly discuss products, companies, and launches.

In plain words
What is it for?
Use it to research launch feedback, competitor chatter, creator activity, support issues, and market trends. It can also prepare evidence for strategy, prioritisation, or risk reviews.
Why use it?
It helps product managers review scattered social feedback and competitor discussion in an organised way. It also makes clear when the required X/Twitter research tools are not available.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: mentions Claude Code; built for hermes-agent.

Part of the hermes-tweet plugin — 1 skill shipped together

Good fit Use it to research launch feedback, competitor chatter, creator activity, support issues, and market trends. It can also prepare evidence for strategy, prioritisation, or risk reviews.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aniganti/pm-superpowers/social-signal-intelligence
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 aniganti/pm-superpowers --skill social-signal-intelligence
Clone the repo
git clone --depth 1 https://github.com/aniganti/pm-superpowers

Made for: Claude Code.

Or install hermes-tweet, the plugin that ships this one along with the rest of its 1 skill.

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 social-signal-intelligence

README.md
[![agentmods](https://agentmods.dev/badge/skills/aniganti/pm-superpowers/social-signal-intelligence/github.svg)](https://agentmods.dev/skills/aniganti/pm-superpowers/social-signal-intelligence)
Your own site
<a href="https://agentmods.dev/skills/aniganti/pm-superpowers/social-signal-intelligence"><img src="https://agentmods.dev/badge/skills/aniganti/pm-superpowers/social-signal-intelligence/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 social-signal-intelligence

Your own site · 80×15
<a href="https://agentmods.dev/skills/aniganti/pm-superpowers/social-signal-intelligence"><img src="https://agentmods.dev/badge/skills/aniganti/pm-superpowers/social-signal-intelligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 750 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
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 Rogue Agent · line 35
    Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.
    Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
How audits are shown
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 $0.00043 $0.00750
Opus 5 $0.00022 $0.00375
Sonnet 5 $0.00009 $0.00150
Haiku 4.5 $0.00004 $0.00075

Measured 12d ago against content hash 30e39eeb2613, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

social-signal-intelligence 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 12d 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.

plugins/hermes-tweet/skills/social-signal-intelligence/SKILL.md · 77 lines

How it starts

The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Social Signal Intelligence

Purpose

Turn X/Twitter evidence into product strategy inputs. Use Hermes Tweet to research public posts, profiles, trends, and product conversations, then summarize the signal in a format PMs can feed into strategy, competitive analysis, prioritization, pre-mortems, or decision logs.

Setup Checks

  1. Inspect the tools available in the current session. Never infer availability from this Skill.
  2. Require tweet_explore before starting any live workflow.
  3. Require tweet_read before live research.
  4. Require tweet_action and explicit approval before account-changing work.
  5. Keep HERMES_TWEET_ENABLE_ACTIONS unset or false for read-only strategy work.

Runtime Boundary

This Skill provides instructions only. It does not install or expose Hermes tools in Claude Code.

If a required tool is unavailable, stop before live work. Explain that the user must run the workflow in Hermes Agent with the native Hermes Tweet plugin enabled. Do not invent results, silently substitute another tool, or imply that setup succeeded. You may still organize evidence the user already supplied.

Instructions

  1. Classify the request:
    • Discovery: the user asks what X/Twitter data or actions are available.
    • Research: the user needs public posts, trends, accounts, or market signal evidence.
    • Action: the user asks to post, reply, like, follow, DM, create monitors, run webhooks, start extraction jobs, upload media, or run giveaway actions.
  2. Apply the runtime boundary. Stop if the tool required for this request is unavailable.
  3. Use tweet_explore first to find the endpoint, capability, or route.
  4. Use tweet_read only for catalog-listed public read-only endpoints.
  5. Use tweet_action only after the user approves the exact endpoint, method, payload, and reason.
  6. Cite returned public URLs and timestamps. Mark missing links as "URL not verified".
  7. Connect findings to the relevant PM workflow:
    • competitive-landscape for competitor positioning.
    • strategy for market and user context.
    • pre-mortem for launch risks.
    • decision-log for evidence behind a product call.
    • prioritization for demand and urgency signals.

Read the full file on GitHub · 77 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. 12d ago First seen · 77 lines · 43 tokens per session scan A 30e39eeb2613

Subscribe to this mod's changes

social-signal-intelligence is a skill published in the GitHub repository aniganti/pm-superpowers (47 stars, last pushed 28d ago), licensed MIT. It adds 43 tokens to every session and 750 once invoked, about $0.0002 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 2026-08-30.

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