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 yugash007/edu-agent-skills --skill weak-area-trackergit clone --depth 1 https://github.com/yugash007/edu-agent-skillsWrote 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/yugash007/edu-agent-skills/weak-area-tracker)<a href="https://agentmods.dev/skills/yugash007/edu-agent-skills/weak-area-tracker"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/weak-area-tracker/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/yugash007/edu-agent-skills/weak-area-tracker"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/weak-area-tracker.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 | $0.00025 | $0.00785 |
| Opus 5 | $0.00013 | $0.00392 |
| Sonnet 5 | $0.00005 | $0.00157 |
| Haiku 4.5 | $0.00003 | $0.00078 |
Grade A, and why
weak-area-tracker 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 10d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Maintain a prioritized, evidence-based log of topics where the learner consistently struggles. Drives which skills to activate next and prevents ignoring recurring problems in favor of always-new content.
Activation
check-understanding,challenge-generator, ormisconception-detectorflags a repeated error. Returning learner with prior weak areas. Planning a study/revision session. Learner reports a covered topic is still unclear.- Skip if: first encounter with a topic (not a weak area yet). Error is a one-time slip. Learner declines tracking.
- Routing: feed into
lesson-planandrevision-mode. Use tracker to decide betweenchallenge-generator(reinforce) vsteach-concept(re-teach) vssocratic-mode(deepen). Mark confirmed improvements and remove from active tracking.
Inputs
- Error event (topic, type, session date, recurrence count), prior weak-area log, learner's current goals.
Severity Scoring
Score each weak area 1–5: score = recurrence × type_weight + staleness_bonus
- Type weights: Surface=1, Structural=2, Deep=3
- Staleness: +1 if not addressed in 3+ sessions
Intervention selection: Score 1–2 → challenge-generator. Score 3–4 → teach-concept re-teach. Score 5 → socratic-mode then misconception-detector.
Workflow
- Ingest — New error: add to log (recurrence=1) or increment existing entry + update
last_seen. - Score — Compute severity from recurrence, type, staleness, and self-reported confidence.
- Triage — Rank by score. Surface top 2 for current session. Max 2 weak areas per session.
- Select Intervention — Match score to appropriate skill (see above).
- Confirm Improvement — After intervention, spot-check via
check-understandingorchallenge-generator. 2 consecutive clean passes → "resolving." 3 consecutive → "resolved" and archived. Regression after resolution → re-open. - Maintain — Cap active list at 5. Archive resolved items. Warn if topic persists 5+ sessions without resolution.
What ships with it
2 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.
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.
- 10d ago First seen · 63 lines · 25 tokens per session scan A 19fff5cc8487
weak-area-tracker is a skill published in the GitHub repository yugash007/edu-agent-skills (7 stars, last pushed 3mo ago), licensed MIT. It adds 25 tokens to every session and 785 once invoked, about $0.0001 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 2026-08-31.
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