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 suboss87/FDEOps --skill evaluategit clone --depth 1 https://github.com/suboss87/FDEOpsWrote 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/suboss87/fdeops/evaluate)<a href="https://agentmods.dev/skills/suboss87/fdeops/evaluate"><img src="https://agentmods.dev/badge/skills/suboss87/fdeops/evaluate/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/suboss87/fdeops/evaluate"><img src="https://agentmods.dev/badge/skills/suboss87/fdeops/evaluate.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 | $0.00030 | $0.00247 |
| Opus 5.5 | $0.00012 | $0.00099 |
| Sonnet 5.5 | $0.00006 | $0.00049 |
| Haiku 4.5 | $0.00003 | $0.00025 |
Grade A, and why
evaluate 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 20d 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.
What it actually says
evaluate
Purpose
Evaluation results, failure analysis and release evidence.
Before investigating or acting:
- Read the task context contract.
- Read the method.
Load further references only when the task needs them. Everything linked is included in this skill; no other skill pack is required.
Principles
- Work directly from the supplied permitted context. Standalone work does not require an engagement folder or initialization. Record filenames in the method are optional persistence destinations when no engagement is bound.
- If called by @fde, reuse its current sanitized packet and scope. Do not restart setup, discovery or questions already answered.
- The task context contract controls persistence and authority in both modes. Preserve unknowns and distinguish implementation, verification, deployment and acceptance.
- Use the customer's repository instructions and available tools. Report a missing capability or unrun check honestly; do not claim that installing a skill provisions infrastructure.
What ships with it
11 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.
- .fde-generated.json 1.1 KB
- agents/openai.yaml 202 B
- references/build.md 4.7 KB
- references/debug.md 3.4 KB
- references/eval-pack.md 5.9 KB
- references/integrate.md 4.7 KB
- references/qa.md 2.6 KB
- references/review.md 5.3 KB
- references/ship.md 10 KB
- references/task-context.md 6.6 KB
- references/verification.md 5.5 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.
- 20d ago Changed b1c9bf68dfce
- 1mo ago Changed · +4 lines 396110a1cb9a
- 1mo ago First seen · 22 lines · 30 tokens per session scan A 6a8f9490294e
evaluate is a skill published in the GitHub repository suboss87/FDEOps (941 stars, last pushed 5d ago), licensed MIT. It adds 30 tokens to every session and 247 once invoked, about $0.0001 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 2026-09-16.
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