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 timurgaleev/memex --skill citation-fixergit clone --depth 1 https://github.com/timurgaleev/memexWrote 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/timurgaleev/memex/citation-fixer)<a href="https://agentmods.dev/skills/timurgaleev/memex/citation-fixer"><img src="https://agentmods.dev/badge/skills/timurgaleev/memex/citation-fixer.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 System Prompt Leakage · line 30 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
- medium Excessive Agency · line 177 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00057 | $0.01524 |
| Opus 5 | $0.00028 | $0.00762 |
| Sonnet 5 | $0.00011 | $0.00305 |
| Haiku 4.5 | $0.00006 | $0.00152 |
Grade A, and why
citation-fixer 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 8d 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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Citation Fixer Skill
Convention: see conventions/quality.md (via
get_skill conventions/quality) for the canonical citation format every fix should match.Output rule: all links MUST be deterministic (built from verified lookup data, not composed by LLM). See
_output-rules.md(viaget_skill _output-rules).
Contract
This skill guarantees:
- Every brain page is scanned for citation compliance.
- Missing citations are flagged with specific location.
- Malformed citations are fixed to match the standard format.
- Tweet / post references without URLs are resolved via the agent's web
search tooling and patched with deterministic
https://x.com/<handle>/status/<id>links. - Results reported with counts (scanned, fixed, remaining).
Phases
- Scan pages.
page_listthe brain and read each page (page_get), checking for inline[Source: ...]citations. - Identify issues:
- Facts without any citation
- Citations missing date
- Citations missing source type
- Citations with wrong format
- Tweet references without
x.comURLs
- Fix format issues. Rewrite malformed citations to match
conventions/quality.md, writing the corrected page back withpage_put. - Resolve tweet references via the agent's web search tooling.
- Report results. Count: pages scanned, citations found, issues fixed, tweets resolved, remaining gaps.
Tweet resolution pipeline
For each broken tweet reference, follow this chain. The lookup goes through whatever web search / X lookup tooling the host agent has — never through invented URLs.
Step 1: Identify broken references
Scan the page for patterns that indicate tweet references without URLs:
- Contains words like
tweeted,posted,said on X,RT,retweet,X post - Contains quoted text that looks like a tweet (short, punchy, often starts with a quote)
- Has
[Source: ... X/Twitter ...]without anx.comURL - References engagement metrics (likes, impressions) without a link
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.
- 8d ago First seen · 212 lines · 57 tokens per session scan A 62483ee7f322
citation-fixer is a skill published in the GitHub repository timurgaleev/memex (8 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 1,524 once invoked, about $0.0003 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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