Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/jmylchreest/aidenpx agentmods add skills/jmylchreest/aide/reflectWrote 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/jmylchreest/aide/reflect)<a href="https://agentmods.dev/skills/jmylchreest/aide/reflect"><img src="https://agentmods.dev/badge/skills/jmylchreest/aide/reflect/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/jmylchreest/aide/reflect"><img src="https://agentmods.dev/badge/skills/jmylchreest/aide/reflect.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 | $0.00000 | $0.03032 |
| Opus 5 | $0.00000 | $0.01516 |
| Sonnet 5 | $0.00000 | $0.00606 |
| Haiku 4.5 | $0.00000 | $0.00303 |
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 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 — 306 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect
Extract candidate instincts — patterns repeatedly observed in this session that might be worth promoting to durable memories.
Your role as the agent running this skill
You propose, the user approves. Nothing this skill does writes a memory or marks anything superseded without an explicit yes from the user.
Detectors emit proposals into a holding bucket (they're never auto-promoted to memories). When this skill runs, your job is to make those proposals reviewable — to add the judgement that mechanical matching can't:
- Classify intent of user prompts in convergence windows — was the user actually correcting the previous edit, or just commenting?
- Rewrite the content into a useful memory — see below. This is the
most important step; the detector's content is a
[DRAFT — rewrite…]placeholder, never the finished memory. - Judge semantic conflicts — which existing memories (if any) does this proposal supersede?
- Recommend an action — accept (with rewritten content + supersedes), reject (why), or leave open for the user to think about.
Then stop and ask. Surface each proposal to the user with your
recommendation. Wait for explicit approval before running
aide reflect accept|reject. The CLI is the write surface; your role is
to make the user's approval decision as well-informed as possible, not to
make it for them.
Concretely: do not chain "list proposals → accept proposals" in the same turn. List, summarise, recommend, wait, then act on user instruction.
Why rewriting is the default, not the exception
The detector emits an observation ("cat was run 5 times in 1 minute").
That's a structural signal, not a useful memory. A useful memory captures:
- Why the repetition / convergence happened (the underlying need or mistake the agent kept circling around).
- What the canonical alternative is (a specific file path, command, pattern, or piece of project knowledge).
- Scope (this codebase / this kind of task / this directory).
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 · 306 lines · 0 tokens per session scan A c4136c26f339
reflect is a skill published in the GitHub repository jmylchreest/aide (17 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,032 tokens. 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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