success-story-miner

success-story-miner is a skill for Claude Code from igptai/skills. It costs 84 tokens per session (1,363 once invoked), scanned A, original, MIT.

An email-analysis workflow that finds customer messages showing satisfaction, positive results, praise, or material suitable for testimonials and case studies.

In plain words
What is it for?
Use it to identify potential success stories, collect testimonial material, and find customer outcomes for case studies.
Why use it?
It helps customer-success teams locate evidence of customer value without manually searching every email thread.

Skill for Claude Code

Written for Claude Code: Claude Code plugin machinery.

Part of the igpt-cs plugin — 5 skills, 1 agent shipped together

Good fit Use it to identify potential success stories, collect testimonial material, and find customer outcomes for case studies.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/igptai/skills/success-story-miner
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 igptai/skills --skill success-story-miner
Clone the repo
git clone --depth 1 https://github.com/igptai/skills

Made for: Claude Code.

Or install igpt-cs, the plugin that ships this one along with the rest of its 5 skills, 1 agent.

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 success-story-miner

README.md
[![agentmods](https://agentmods.dev/badge/skills/igptai/skills/success-story-miner/github.svg)](https://agentmods.dev/skills/igptai/skills/success-story-miner)
Your own site
<a href="https://agentmods.dev/skills/igptai/skills/success-story-miner"><img src="https://agentmods.dev/badge/skills/igptai/skills/success-story-miner/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 success-story-miner

Your own site · 80×15
<a href="https://agentmods.dev/skills/igptai/skills/success-story-miner"><img src="https://agentmods.dev/badge/skills/igptai/skills/success-story-miner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,363 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 $0.00084 $0.01363
Opus 5 $0.00042 $0.00681
Sonnet 5 $0.00017 $0.00273
Haiku 4.5 $0.00008 $0.00136

Measured 12d ago against content hash 057af693b079, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

success-story-miner 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/igpt-cs/skills/success-story-miner/SKILL.md · 149 lines

How it starts

The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Success Story Miner

Prerequisites

This skill needs the iGPT MCP at https://mcp.igpt.ai/.

If the MCP tools aren't available or return an auth error, tell the user to install the iGPT plugin (/plugin marketplace add igptai/skills) or add https://mcp.igpt.ai/ as a connector, then complete OAuth and say "ready". Retry once after they confirm. Never invent tokens or OAuth URLs. For deeper troubleshooting: https://raw.githubusercontent.com/igptai/skills/main/shared/mcp-guard.md


What This Skill Does

Scans all customer email threads for moments of genuine satisfaction — customers sharing positive results, expressing appreciation, reporting outcomes achieved, and making statements that could be used as testimonials or case study material.


Workflow

  1. Before calling any tool, collect these values from the user. Offer the defaults and let the user override them; do not invent values they did not give.

    • [time_range] — what window of email to scan. The user may give this in any form ("last 12 months", "the last year", "May 2024", "since launch"). Default: the last 12 months. Keep the user's natural phrasing for use in the ask input; convert to ISO dates separately for the search call.
    • [account_scope] — either "all" (default) or the name of a specific customer account to focus on.
    • [account_clause] — derived. When [account_scope] is not "all", set to " for account [account_scope]". When [account_scope] is "all", set to empty string.
  2. Call search with:

    • query: thank you great results love excellent working well impressed achieved solved helped outcome success (if [account_scope] is not "all", append the account name to the query)
    • date_from: ISO start date derived from [time_range]
    • date_to: ISO end date derived from [time_range] (or today if open-ended)
  3. Call ask with:

    • input: Review all customer email threads from [time_range][account_clause]. Find every moment where a customer expressed genuine satisfaction, reported a positive outcome or result, praised the product or team, or said something that could be used as a testimonial or case study quote. For each success moment note the customer, what they said, the context of the win, and the potential value as a proof point or case study.
    • output_format: { "strict": true, "schema": { "type": "object", "description": "Customer success story and testimonial report mined from email history", "additionalProperties": false, "properties": { "as_of": { "type": "string", "description": "ISO8601 date when this report was generated" }, "success_moments": { "type": "array", "description": "List of every customer success moment found in email", "items": { "type": "object", "description": "A single customer success moment with testimonial potential", "additionalProperties": false, "properties": { "customer": { "type": "string", "description": "Name of the customer company" }, "contact": { "type": "string", "description": "Name or role of the customer contact who expressed this success" }, "date": { "type": "string", "description": "ISO8601 date when this success moment appeared in email" }, "success_type": { "type": "string", "description": "Category of success moment", "enum": [ "quantified_result", "problem_solved", "team_praise", "product_praise", "recommendation_offer", "renewal_enthusiasm", "expansion_interest", "general_satisfaction" ] }, "quote_or_summary": { "type": "string", "description": "Direct quote or close paraphrase of what the customer said" }, "business_context": { "type": "string", "description": "Brief description of the business situation that led to this success" }, "testimonial_potential": { "type": "string", "description": "How strong this moment is as testimonial or case study material", "enum": ["excellent", "good", "moderate", "low"] }, "case_study_angle": { "type": "string", "description": "The story angle this success moment could support in a case study" } }, "required": [ "customer", "contact", "date", "success_type", "quote_or_summary", "business_context", "testimonial_potential", "case_study_angle" ] } }, "excellent_count": { "type": "number", "description": "Number of success moments rated as excellent testimonial material" }, "top_candidates": { "type": "array", "description": "The top three customers most suitable for case study or testimonial outreach", "items": { "type": "string", "description": "Name of a customer who is a strong case study candidate" } }, "summary": { "type": "string", "description": "One or two sentence summary of success moments found and top case study candidates" } }, "required": [ "as_of", "success_moments", "excellent_count", "top_candidates", "summary" ] } }

Read the full file on GitHub · 149 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 · 149 lines · 84 tokens per session scan A 057af693b079

Subscribe to this mod's changes

success-story-miner is a skill published in the GitHub repository igptai/skills (16 stars, last pushed 4mo ago), licensed MIT. It adds 84 tokens to every session and 1,363 once invoked, about $0.0004 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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