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 OutlineDriven/odin-claude-plugin --skill reflectgit clone --depth 1 https://github.com/OutlineDriven/odin-claude-pluginWrote 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/outlinedriven/odin-claude-plugin/reflect)<a href="https://agentmods.dev/skills/outlinedriven/odin-claude-plugin/reflect"><img src="https://agentmods.dev/badge/skills/outlinedriven/odin-claude-plugin/reflect.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Data Exfiltration · line 34 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
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 | $0.00042 | $0.00647 |
| Opus 5 | $0.00021 | $0.00324 |
| Sonnet 5 | $0.00008 | $0.00129 |
| Haiku 4.5 | $0.00004 | $0.00065 |
Grade A, and why
reflect 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 3d 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect
Contract
| Field | Bound contract |
|---|---|
| Trigger | Reflect on a completed task to improve invoked skills. |
| Authority | Human-only: explicit invocation required before any mutation. |
| Side effect | Edits approved skills and may file backlog items. |
| Done | Approved improvements applied and rejections explained. |
Refusals
- Edits without explicit human approval: rejected. Every "apply now" or "apply on approval" proposal requires explicit human sign-off before any file change.
- Analysis beyond the named skills: rejected. Do not widen the analysis beyond the invoked skills the human supplied.
- Remote, credential, publish, deploy, or irreversible changes: rejected.
Inputs
The human must supply:
- The completed task context (what was attempted, what happened, what remains).
- The list of invoked skill slugs or names.
Optional: evidence of failure patterns, specific lines or sections to target, or scope constraints.
Procedure
- Collect context. Gather the task context and the list of invoked skills from the human. Identify which skill files are reachable in the workspace. Done when: the task context and skill list are gathered and reachable files are identified.
- Analyze. Examine the invoked skills for failure patterns, missing coverage, unclear scope, or improvement opportunities. Do not widen the analysis beyond the named skills. Done when: each skill has been examined and findings are listed.
- Propose. Present each finding as a discrete improvement proposal. Label each as "apply now", "apply on approval", or "backlog item". Keep proposals scoped to one skill. Done when: every finding is presented as a labeled proposal.
- Get approval. For every "apply now" or "apply on approval" proposal, obtain explicit human approval before making any file change. Done when: every proposal has an approval decision recorded.
- Execute. Apply approved edits to the skill files. File any approved backlog items. Report every applied change and every rejected or deferred proposal. Done when: all approved edits are applied and all rejections are recorded.
What ships with it
1 file 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.
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.
- 3d ago First seen · 50 lines · 42 tokens per session scan A 1d5b7b667ea4
reflect is a skill published in the GitHub repository OutlineDriven/odin-claude-plugin (35 stars, last pushed yesterday), licensed Apache-2.0. It adds 42 tokens to every session and 647 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 2026-09-04.
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