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 peterod99/consultant-skills --skill case-study-extractiongit clone --depth 1 https://github.com/peterod99/consultant-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/peterod99/consultant-skills/case-study-extraction)<a href="https://agentmods.dev/skills/peterod99/consultant-skills/case-study-extraction"><img src="https://agentmods.dev/badge/skills/peterod99/consultant-skills/case-study-extraction/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/peterod99/consultant-skills/case-study-extraction"><img src="https://agentmods.dev/badge/skills/peterod99/consultant-skills/case-study-extraction.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 | $0.00035 | $0.01123 |
| Opus 5 | $0.00017 | $0.00562 |
| Sonnet 5 | $0.00007 | $0.00225 |
| Haiku 4.5 | $0.00003 | $0.00112 |
Grade A, and why
case-study-extraction 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 8d 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Case Study Extraction
When to use
After a significant client project closes, extract a deployable proof asset before the engagement fades from memory. A case study is a 3–5 minute LinkedIn post or a 500-word document that converts prospects 3–5x better than generic positioning. Run this within 30 days of project completion while details are fresh and client is still celebrating wins.
The framework
- Schedule a debrief interview. Spend 30 minutes with your champion (the person who hired you) asking: What did they expect before you arrived? What changed? What surprised them? What would they tell a peer in the same situation? Record or take detailed notes.
- Extract the before/during/after arc. Before: their challenge, constraint, or gap (the status quo). During: your intervention and key decisions (the method: brief; don't oversell process). After: the measurable improvement in their terms (revenue, time, efficiency, risk reduction, staff retention, customer satisfaction).
- Quantify the result in their language, not yours. "Increased conversion rate by 23%" beats "implemented a new sales process." "Cut customer acquisition cost from $200 to $84" beats "optimized the funnel." Dollar figures, percentages, and time-saved metrics resonate; methodological descriptions do not.
- Write a 3–5 sentence narrative. Open with the problem (1 sentence). Middle: the intervention and why it worked (2 sentences). Close: the outcome and one insight or lesson (2 sentences). Keep it conversational, not marketing-speak.
- Add one insight or lesson. After describing the result, ask your client: What would you tell a peer facing the same challenge? Capture their unfiltered answer. That's the credibility knot: not your genius, but real client voice.
- Attach a proof metric. One number (revenue generated, time saved, percentage improvement, or staff impact) that proves the project mattered. Specificity beats roundness; $47k in new ARR generated beats "significant revenue impact."
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.
- 8d ago First seen · 39 lines · 35 tokens per session scan A 2e30f075d440
case-study-extraction is a skill published in the GitHub repository peterod99/consultant-skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 35 tokens to every session and 1,123 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 2026-08-31.
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