Borrowing it
Nothing to install: this file belongs to sunholo-data/ailang-parse. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/sunholo-data/ailang-parse/main/.claude/skills/fix-parser-gap/SKILL.mdgit clone --depth 1 https://github.com/sunholo-data/ailang-parseWrote 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/sunholo-data/ailang-parse/fix-parser-gap)<a href="https://agentmods.dev/skills/sunholo-data/ailang-parse/fix-parser-gap"><img src="https://agentmods.dev/badge/skills/sunholo-data/ailang-parse/fix-parser-gap/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/sunholo-data/ailang-parse/fix-parser-gap"><img src="https://agentmods.dev/badge/skills/sunholo-data/ailang-parse/fix-parser-gap.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 | $0.00116 | $0.01598 |
| Opus 5.5 | $0.00046 | $0.00639 |
| Sonnet 5.5 | $0.00023 | $0.00320 |
| Haiku 4.5 | $0.00012 | $0.00160 |
Grade A, and why
fix-parser-gap 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 1mo 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fix Parser Gap
Implements missing ECMA-376 Office parsing features using a TDD red-to-green workflow: write a failing test first, read the spec, implement in AILANG, verify the test goes green, then re-run benchmarks.
Workflow: Red -> Green -> Benchmark
Step 1: Select the Gap
If the user specifies a feature, use that. Otherwise pick the highest-priority unresolved gap from the audit:
# Show current gap scores (0% = complete gap, 100% = fully handled)
uv run benchmarks/office/eval_gaps.py --verbose
Consult resources/reference.md for the priority table and spec page numbers.
Step 2: RED — Write or Update the Failing Test
This step is critical. No implementation without a failing test first.
Two places tests live:
-
benchmarks/office/eval_gaps.py— Gap analysis checks. Each check is a function likecheck_custom_heading_styles(output)that returns a score from 0.0-1.0. If a check doesn't exist for the gap, add one. If it exists but is too lenient, tighten it. -
benchmarks/officedocbench/ground_truth/challenge_*.json— Hand-verified ground truth for challenge files. These files have"verified": truesoannotate.pywon't overwrite them. If the ground truth doesn't reflect the expected correct behavior, fix it.
Also create or verify the challenge test file in data/test_files/challenge/ — these are generated by benchmarks/create_challenge_files.py using python-docx/openpyxl. If the existing challenge file doesn't test the feature well enough, update the generator.
Run the test to confirm it fails (RED):
uv run benchmarks/office/eval_gaps.py --verbose # Check the specific gap shows 0%
Step 3: Read the Spec
Read the relevant ECMA-376 spec section for the feature being implemented:
# Spec PDF is at:
specs/ecma-376/part1/ECMA-376-1-5th-edition-december-2016-Part1.pdf
Use the page numbers from resources/reference.md. Focus on:
- The XML element structure (tag names, attributes, child elements)
- Where the element appears in the ZIP (which XML file)
- How relationships work (if the feature uses
r:idreferences)
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.
- 1mo ago Changed e16b0249c2f0
- 2mo ago First seen · 154 lines · 116 tokens per session scan A 550bc0101067
fix-parser-gap is a skill published in the GitHub repository sunholo-data/ailang-parse (0 stars, last pushed 4d ago), licensed Apache-2.0. It adds 116 tokens to every session and 1,598 once invoked, about $0.0005 per session on Opus 5.5. 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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