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 igptai/skills --skill success-story-minergit clone --depth 1 https://github.com/igptai/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/igptai/skills/success-story-miner)<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.
<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>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 | $0.00084 | $0.01363 |
| Opus 5 | $0.00042 | $0.00681 |
| Sonnet 5 | $0.00017 | $0.00273 |
| Haiku 4.5 | $0.00008 | $0.00136 |
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.
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
-
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.
-
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)
-
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" ] } }
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.
- 12d ago First seen · 149 lines · 84 tokens per session scan A 057af693b079
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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