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 kaustubhhiware/agent-skills-fullstack-development --skill mistake-memory-guardrailsgit clone --depth 1 https://github.com/kaustubhhiware/agent-skills-fullstack-developmentWrote 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/kaustubhhiware/agent-skills-fullstack-development/mistake-memory-guardrails)<a href="https://agentmods.dev/skills/kaustubhhiware/agent-skills-fullstack-development/mistake-memory-guardrails"><img src="https://agentmods.dev/badge/skills/kaustubhhiware/agent-skills-fullstack-development/mistake-memory-guardrails/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/kaustubhhiware/agent-skills-fullstack-development/mistake-memory-guardrails"><img src="https://agentmods.dev/badge/skills/kaustubhhiware/agent-skills-fullstack-development/mistake-memory-guardrails.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 | $0.00032 | $0.00546 |
| Opus 5 | $0.00016 | $0.00273 |
| Sonnet 5 | $0.00006 | $0.00109 |
| Haiku 4.5 | $0.00003 | $0.00055 |
Grade A, and why
mistake-memory-guardrails 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mistake Memory Guardrails
Global guardrails for repository edit tasks.
Scope
Apply this skill to any repository edit task, including code, docs, tests, config, infra, and planning files.
Execution Order
- Read
AGENT_MISTAKES.md. If missing, create it using the required schema. - Read target files and map applicable
scope_tags. - Run guardrail checks before proposing or applying changes.
- Run a post-edit self-check against known mistake patterns before final response.
- Record every detected mistake occurrence.
Guardrail Checks
- Requirement fidelity and scope discipline:
- Change only what the user requested.
- Reject unrelated refactors and speculative additions.
- Cross-file consistency for shared facts/configs/interfaces:
- Keep shared names, paths, status values, and contracts aligned across touched files.
- Flag stale duplicated facts and mismatched defaults.
- Assertion quality:
- Replace vague claims with measurable criteria.
- Require explicit thresholds, formulas, or pass/fail conditions when asserting quality or correctness.
- Reproducibility metadata when claims depend on run conditions:
- For benchmark/performance/behavior claims, capture model/tool version, dataset revision, seed, environment, and run conditions when relevant.
- Separate deterministic correctness checks from environment-sensitive comparisons.
- Safety and regression checks:
- Verify edits do not silently break existing behavior.
- Call out potential regressions and missing validation coverage.
Mistake Recording Rules
- Record every detected mistake occurrence.
- Deduplicate by normalized
pattern+scope_tags+prevention_rule.- Normalization: lowercase, trim whitespace, collapse repeated spaces.
- If a deduplicated entry exists:
- Increment
occurrence_count. - Update
last_seen. - Append new evidence (file:line and/or commit).
- Increment
- If no matching entry exists:
- Append a new entry with all required fields from
AGENT_MISTAKES.md.
- Append a new entry with all required fields from
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 · 71 lines · 32 tokens per session scan A a844afdef64c
mistake-memory-guardrails is a skill published in the GitHub repository kaustubhhiware/agent-skills-fullstack-development (16 stars, last pushed 3mo ago), licensed MIT. It adds 32 tokens to every session and 546 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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