Borrowing it
Nothing to install: this file belongs to pangon/ai-sdlc-framework. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pangon/ai-sdlc-framework/main/.claude/skills/SDLC-elicit/SKILL.mdgit clone --depth 1 https://github.com/pangon/ai-sdlc-frameworkWrote 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/pangon/ai-sdlc-framework/sdlc-elicit)<a href="https://agentmods.dev/skills/pangon/ai-sdlc-framework/sdlc-elicit"><img src="https://agentmods.dev/badge/skills/pangon/ai-sdlc-framework/sdlc-elicit/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/pangon/ai-sdlc-framework/sdlc-elicit"><img src="https://agentmods.dev/badge/skills/pangon/ai-sdlc-framework/sdlc-elicit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, 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 Excessive Agency · line 127 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 138 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 169 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00044 | $0.03782 |
| Opus 5.5 | $0.00018 | $0.01513 |
| Sonnet 5.5 | $0.00009 | $0.00756 |
| Haiku 4.5 | $0.00004 | $0.00378 |
Grade A, and why
SDLC-elicit 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 2mo 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructions
You are running an interactive elicitation session for the Specification phase of an AI-first SDLC project. This skill supports both creating new artifacts and modifying existing ones.
Phase Validation
Before doing anything else, read the **Phase**: field of the ### Current State subsection in CLAUDE.md. Then follow the matching case below:
-
Not initialized— Stop, recommend/SDLC-init, and do not proceed. -
Specification— proceed normally with the Setup steps below. -
DesignorCode— the project has advanced beyond Specification. Warn that modifying Specification artifacts may impact downstream design, tasks, or deployed code. If the user confirms, proceed but flag downstream dependencies that could be affected.
Setup
- Read
1-spec/CLAUDE.spec.md(phase instructions and existing artifact indexes). - Read
1-spec/stakeholders.mdto understand existing stakeholders. - Check the
## Decisions Relevant to This Phaseindex in1-spec/CLAUDE.spec.md: read any decisions whose trigger conditions apply, and apply their enforcement rules throughout the elicitation session.
Artifact Traceability Chain
Specification artifacts form a traceability chain. Each level should decompose into the next:
- Stakeholder → Goals — every stakeholder should have at least one associated goal. A stakeholder with no goals has no defined value proposition in the project.
- Goal → User Stories — every goal should have at least one associated user story. Review the linked user stories against the goal's success criteria and flag coverage gaps.
- User Story → Requirements — every user story should have at least one associated requirement. Review the linked requirements against the story's acceptance criteria and flag obvious coverage gaps.
- Constraint → Requirements — constraints may also generate requirements directly (e.g., a compliance constraint produces a compliance requirement, a technology constraint produces a compatibility requirement). When a constraint implies a verifiable obligation, derive a requirement from it and link both.
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.
- 2mo ago First seen · 176 lines · 44 tokens per session scan A ea6ab785963f
SDLC-elicit is a skill published in the GitHub repository pangon/ai-sdlc-framework (130 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 44 tokens to every session and 3,782 once invoked, about $0.0002 per session on Opus 5.5. 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.
SDLC-elicit compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| SDLC-elicitpangon/ai-sdlc-framework | Skill | 130 | 2mo ago | Apache-2.0 | A | 44 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |