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 vasilyu1983/AI-Agents-public --skill agents-skills-feedback-loopgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/agents-skills-feedback-loop)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/agents-skills-feedback-loop"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/agents-skills-feedback-loop/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/vasilyu1983/ai-agents-public/agents-skills-feedback-loop"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/agents-skills-feedback-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 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 Memory Poisoning · line 19 Skill injects content designed to persist in agent memory or context across interactions. Persistent injection can alter agent behavior long after the initial interaction.Fix: Do not allow untrusted input to persist in agent memory or context. Validate all content before storing and implement memory isolation between sessions.
- medium Rogue Agent · line 38 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00035 | $0.05289 |
| Opus 5 | $0.00017 | $0.02645 |
| Sonnet 5 | $0.00007 | $0.01058 |
| Haiku 4.5 | $0.00003 | $0.00529 |
Grade A, and why
agents-skills-feedback-loop 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 11d 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 — 328 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Skills — Feedback Loop
Use this skill to wire a learnings loop into another skill so it gets better with use, without rewriting SKILL.md automatically.
The loop has four moving parts:
learnings.md— raw, append-only, committed. Shared working memory across machines; created on first append viaappend_learning.py, not seeded empty.learnings.consolidated.md— pruned, dated, committed. Portfolio-grade institutional memory; seeded at wiring time.learnings.local.md— machine-specific notes, gitignored. Use for one-operator-on-one-machine context that should not propagate.scripts/append_learning.py+scripts/consolidate.py— keep raw entries well-shaped and promote durable ones to consolidated.
The name is borrowed from the 2026 "learnings loop" pattern (MindStudio) and Anthropic's own skill-authoring guidance to ask Claude to self-reflect on what went wrong and fold it back into reusable context. The mechanism deliberately does not match MindStudio's: MindStudio's loop has the model rewrite the skill's persistent instructions directly from user corrections — that is the exact auto-rewrite this design forbids (see Anti-Patterns). This skill keeps the same "accumulate corrections across sessions" shape but routes it through append-only raw entries, human-reviewed consolidation, and an eval-gated promotion step before anything touches skill logic — mapped onto this repo's existing 4-type memory schema (see agents-memory).
Quick Reference
| Task | Read or Run | Outcome |
|---|---|---|
| Wire a skill to use the loop | references/wiring-protocol.md |
Adds a 4-line addendum to that skill's SKILL.md, seeds files |
| Format a new learning entry | references/learnings-format.md |
Atomic, dated, 5-section schema that survives pruning |
| Promote raw → consolidated | python3 scripts/consolidate.py <skill-dir> |
Dedup, age out, surface recurring patterns for human review |
| Append a learning safely | python3 scripts/append_learning.py <skill-dir> --section <name> --text "..." |
Validates shape, dates, refuses to grow past the 150-entry cap |
| Audit drift across skills | references/audit-checklist.md |
Find stale loops, missing consolidations, oversized files |
| Capture a cross-cutting behavior with no obvious skill home | references/instinct-pattern.md |
Atomic, confidence-scored complement to the per-skill loop above |
What ships with it
20 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.
- agents/openai.yaml 377 B
- assets/learnings_capture.py 11 KB runs code
- assets/learnings.template.md 762 B
- assets/promotion-eval.template.md 3.6 KB
- data/sources.json 5.7 KB
- learnings.consolidated.md 775 B
- learnings.md 405 B
- references/audit-checklist.md 1.9 KB
- references/closed-loop-capture.md 6.9 KB
- references/consolidation-protocol.md 2.9 KB
- references/evidence-base.md 7.7 KB
- references/instinct-pattern.md 5.6 KB
- references/learnings-format.md 2.5 KB
- references/promotion-protocol.md 4.5 KB
- references/wiring-protocol.md 3.3 KB
- scripts/append_learning.py 3.8 KB runs code
- scripts/bulk_wire.py 4.7 KB runs code
- scripts/consolidate.py 6.4 KB runs code
- scripts/install_capture_hook.py 4.4 KB runs code
- scripts/promote_learning.py 9.4 KB runs code
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.
- 11d ago First seen · 328 lines · 35 tokens per session scan A 7fa91f747985
agents-skills-feedback-loop is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 9d ago), licensed MIT. It adds 35 tokens to every session and 5,289 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 2026-08-30.
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Use this skill when performing manual memory consolidation (Dream-equivalent). Reviews, consolidates, and prunes memory files under /.claude/projects//memory/. Run after major refactors, every 5+ sessions, or when memory quality degrades (broken links, stale references, contradictions, MEMORY.md > 200 lines). Invoke…
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