Borrowing it
Nothing to install: this file belongs to yeison-liscano/demo_http_mcp. 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/yeison-liscano/demo_http_mcp/main/.claude/skills/analyzing-impact/SKILL.mdgit clone --depth 1 https://github.com/yeison-liscano/demo_http_mcpWrote 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/yeison-liscano/demo_http_mcp/analyzing-impact)<a href="https://agentmods.dev/skills/yeison-liscano/demo_http_mcp/analyzing-impact"><img src="https://agentmods.dev/badge/skills/yeison-liscano/demo_http_mcp/analyzing-impact/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/yeison-liscano/demo_http_mcp/analyzing-impact"><img src="https://agentmods.dev/badge/skills/yeison-liscano/demo_http_mcp/analyzing-impact.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 | $0.00102 | $0.00877 |
| Opus 5 | $0.00051 | $0.00439 |
| Sonnet 5 | $0.00020 | $0.00175 |
| Haiku 4.5 | $0.00010 | $0.00088 |
Grade A, and why
analyzing-impact 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.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Impact Analysis
Evaluate the security, performance, operational, and business impact of a proposed feature, architectural change, or implementation plan.
Workflow
- Understand the proposal — Read the plan, feature description, code diff, or design doc the user provides. If the proposal is verbal, summarize it back and confirm understanding before proceeding.
- Map the affected surface — Identify all systems, services, data stores, APIs, and user-facing behaviors that the change touches directly or indirectly.
- Analyze each impact dimension — Evaluate the proposal across all dimensions below.
- Risk matrix — Score each dimension and produce an overall risk assessment.
- Recommendations — Provide concrete mitigations for identified risks.
Impact Dimensions
Security Impact
- Does this introduce new attack surface (new endpoints, new inputs, new data flows)?
- Does it change authentication or authorization behavior?
- Does it handle sensitive data differently?
- Does it introduce new dependencies with their own attack surface?
- Could it be exploited if partially deployed or rolled back?
Performance Impact
- Expected load changes (new queries, API calls, compute)
- Memory and storage implications
- Latency impact on critical paths
- Scalability ceiling changes
- Cache invalidation or warming requirements
Operational Impact
- Deployment complexity (migrations, feature flags, coordination)
- Monitoring and alerting requirements
- Rollback feasibility and procedure
- On-call impact (new failure modes, new runbooks needed)
- Infrastructure changes required
Data Impact
- Schema changes (migrations, backward compatibility)
- Data volume changes
- Data retention and compliance implications
- Backup and recovery considerations
- Data consistency during rollout
Business Impact
- User-facing behavior changes
- Backward compatibility with existing clients/integrations
- Feature flag and gradual rollout strategy
- Documentation and communication requirements
- Dependencies on other teams or services
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 · 109 lines · 102 tokens per session scan A cf7a525570f3
analyzing-impact is a skill published in the GitHub repository yeison-liscano/demo_http_mcp (0 stars, last pushed 5mo ago), licensed MIT. It adds 102 tokens to every session and 877 once invoked, about $0.0005 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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