* refactor(skills): shipped-set slim — 15 skills to optional, github six-way merge, pdf absorbs OCR+nano-pdf, channel-gated teams pipeline
Maintainer-directed shipped-skills curation (skills index 1,900 -> ~1,400
tok/call on desktop; every session pays the index, so this is a per-call
diet on all installs):
- optional-skills moves (installable via skills hub, history preserved):
creative comfyui/ascii-art/excalidraw/pretext/sketch/touchdesigner-mcp;
ALL of mlops (huggingface-hub, llama-cpp, serving-llms-vllm,
weights-and-biases, evaluating-llms-harness — subcategory structure
kept); research-paper-writing (55 supporting files, 17.3K-tok load);
openhue; blogwatcher (first taught the cronjob monitor-field watch
pattern + web_extract instead of pre-cron manual workflows)
- DELETED session-librarian (Aug-12 'inspired by Perplexity Computer'
port, never maintainer-intended; session_search covers discovery)
- github: six skills (auth, issues, pr-workflow, issue-to-pr,
code-review, repo-management) merged into ONE software-development/
github skill — routing body + complete per-workflow references;
benbarclay authorship credited; codebase-inspection rides along;
discipline pins from test_github_issue_to_pr_skill.py preserved
against the reference body in the new test_github_skill.py
- pdf absorbs ocr-and-documents + nano-pdf as references/ + scripts
(extract_pymupdf, extract_marker converted to the argparse house
standard its contract test enforces)
- NEW session_platforms frontmatter gate (metadata.hermes): hides a
skill from the index on gateway channels it is not for; fail-open on
unknown platform; teams-meeting-pipeline gated to [teams, cron]
- blocked-page-recovery: research -> new web category; trigger-first
description ('Use when a fetch fails: 403/429, paywall, WAF, bot
wall.') so the model actually reaches for it on blocked fetches
- docs regenerated via generate-skill-docs.py (195 pages); related_skills
swept repo-wide; tests: 1672 passed (2 openclaw failures pre-existing
on clean main, Windows-local)
* chore: ignore .skills_prompt_snapshot.json (local index cache, accidentally committed)
Two Windows agent-loop friction fixes:
1. tools/mcp_tool.py (#56536): shutil.which(cmd, path=env_path) reads
executable extensions from the PARENT process PATHEXT, not the MCP
subprocess env — a stdio MCP config supplying both PATH and PATHEXT
could fail to resolve a command its own env can locate, and startup
then got a bare command name. On Windows, when the first which() call
misses and the config env carries PATHEXT (any key casing), retry the
resolution with the config's PATHEXT temporarily applied.
2. skills/ + optional-skills/ (#50606): 42 SKILL.md files that declare
platforms: [.., windows] used python3 in their command examples.
python3 does not exist on native Windows (the toolchain probe in the
system prompt reports python3=missing), so every copy-pasted example
burned a failed agent turn before self-correction. Replaced the
command word python3 -> python (python3-config / python3.x version
strings untouched). python is the spelling that exists in every
Hermes-managed environment (Windows native, uv-managed venvs on all
three OSes); agents on POSIX hosts additionally see the probed
toolchain line and adapt either way.
The bundled docx, xlsx, powerpoint, and pdf skills were adapted from
Anthropic's document skills and carried their proprietary LICENSE.txt
(no derivatives, no redistribution). Flagged as critical license
findings by the SkillEvaluator Tier 1 scan of our skill tree.
This replaces all four with clean-room rewrites:
- Authored from scratch against library knowledge only (python-docx,
openpyxl, python-pptx, pypdf/reportlab/pdfplumber — all MIT/BSD) by
isolated subagents given functional specs, with an explicit
prohibition on reading the prior skill content or anthropics/skills;
session transcripts retained as provenance evidence.
- MIT licensed (LICENSE file per skill), author: Nous Research.
- Each skill: SKILL.md to house standards + argparse helper scripts
with UTF-8-explicit I/O + its own e2e pytest suite (fixtures built
on the fly, non-ASCII round-trips run under LC_ALL=C).
- All four pass SkillEvaluator Tier 1 pii+unicode+lint 3/3.
tests/skills/test_office_document_skills.py rewritten against the new
contracts: MIT/no-Anthropic-text invariants, scripts documented in
SKILL.md, argparse CLI shape, and a no-locale-default-open() check
(which caught and fixed a real gap: pdfplumber text reads are fine,
but the invariant scan now guards every future script).
Docs pages regenerated for the four skills (scoped; unrelated
generator drift excluded).
Honest capability deltas vs the old versions are documented per
SKILL.md (e.g. tracked-changes accept/reject and OOXML XSD validation
are not reimplemented; form flattening limits stated).
anydoc converts the PDF text layer only and emits no image placeholders
or page markers, so a mostly-scanned PDF extracts 'successfully' into
section headers with empty bodies — silent data loss the model cannot
detect. Count per-page text via poppler pdftotext and prepend an
EXTRACTION COVERAGE WARNING naming the empty pages and the recovery
path (pdftoppm + vision_analyze, or the ocr-and-documents skill).
Found on a 311-page HOA resale package where 198 scanned pages
(CC&Rs, Bylaws, Articles, insurance certs) vanished without a trace.
The bundled office skills (#68595) read user documents and agent-authored
payloads with the locale-default codec:
- docx/powerpoint validators/base.py opened OOXML part XML in text mode
before handing it to lxml. On Windows (cp1251/GBK) the bytes decode to
mojibake that lxml then parses, so validation runs against silently
corrupted document text; on locales where the UTF-8 bytes don't decode
the validator crashes with UnicodeDecodeError instead of validating.
Opening as bytes lets lxml honor the encoding declared in the XML prolog.
- The pdf form scripts (fill_fillable_fields, fill_pdf_form_with_annotations,
create_validation_image, check_bounding_boxes) read the fields JSON the
agent authors — UTF-8 by construction — with the locale codec, so
non-ASCII form values (any Cyrillic/CJK/accented input) either crash or
get written into the user's PDF as mojibake. The json.dump writers use
ensure_ascii=True and were already safe; only the readers needed pinning.
Adds a contract test asserting every document/payload reader is
locale-independent, plus a live regression test that runs
check_bounding_boxes.py on a non-ASCII fields.json under a forced
non-UTF-8 locale — it fails without the fix on both POSIX (C locale)
and Windows (cp1251 chokes on the 0x98 byte of U+2018).
Teknium