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 rampstackco/claude-skills --skill jtbd-framinggit clone --depth 1 https://github.com/rampstackco/claude-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/rampstackco/claude-skills/jtbd-framing)<a href="https://agentmods.dev/skills/rampstackco/claude-skills/jtbd-framing"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills/jtbd-framing/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/rampstackco/claude-skills/jtbd-framing"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills/jtbd-framing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00164 | $0.04378 |
| Opus 5 | $0.00082 | $0.02189 |
| Sonnet 5 | $0.00033 | $0.00876 |
| Haiku 4.5 | $0.00016 | $0.00438 |
Grade A, and why
jtbd-framing 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 9d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- jtbd-framing — 95% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jobs-to-be-Done Framing
A senior product leader's playbook for the Jobs-to-be-Done framework as applied methodology. Job statements, struggling moments, hire and fire criteria, the difference between feature-thinking and job-thinking. Honest about where JTBD adds clarity and where it becomes performative ritual.
JTBD has become one of the more cited and less practiced frameworks in product. Teams cite it in strategy docs, run job-statement workshops, produce wall-sized artifacts, and continue building from feature requests and persona archetypes the next quarter. The methodology gets the credit; the practice gets skipped.
This skill is JTBD as applied product methodology. The framework's actual contribution: surfacing what users are trying to ACCOMPLISH (the job) rather than treating users as preference-aggregators (feature requests) or demographic archetypes (persona theater). When the framing is grounded in struggling moments and hire/fire criteria, it produces decisions; when it stops at the job-statement worksheet, it produces ritual.
This skill is honest about both modes. JTBD genuinely earns its keep in discovery, prioritization, and positioning when applied with rigor. It becomes ceremony when teams treat job statements as deliverables rather than as analytical tools.
The voice is the senior product leader who has used JTBD well and watched plenty of teams use it badly. Concrete, opinionated about what the framework actually contributes, willing to call out where it gets oversold.
When to use this skill: applying JTBD to a discovery cycle, replacing persona-driven prioritization with job-driven prioritization, reframing positioning around what users hire the product to do, or auditing whether existing JTBD work in the org is driving decisions.
What this skill is for
This skill spans JTBD as a framing technique within product work. The PM-skill distinction:
discovery-research-synthesisis broader synthesis discipline; JTBD is one framing technique within it.jtbd-framing(this skill) is the specific JTBD methodology, its strengths, and its failure modes.pm-spec-writingis downstream: specs reference jobs as input.creative-directionis positioning territory; JTBD informs positioning but does not replace creative direction.roadmap-planningis downstream: roadmap can be organized around jobs rather than features.
What ships with it
8 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.
- references/applying-jtbd-to-discovery.md 11 KB
- references/applying-jtbd-to-positioning.md 10 KB
- references/applying-jtbd-to-prioritization.md 11 KB
- references/common-jtbd-failures.md 11 KB
- references/functional-emotional-social-dimensions.md 10 KB
- references/hire-and-fire-criteria.md 10 KB
- references/identifying-struggling-moments.md 9.6 KB
- references/job-statement-structure-patterns.md 9.1 KB
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.
- 9d ago First seen · 267 lines · 164 tokens per session scan A 31dce3d89f9b
jtbd-framing is a skill published in the GitHub repository rampstackco/claude-skills (830 stars, last pushed yesterday), licensed MIT. It adds 164 tokens to every session and 4,378 once invoked, about $0.0008 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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