The config key skills.external_dirs and core resolution (get_all_skills_dirs,
get_external_skills_dirs in agent/skill_utils.py) already existed but several
code paths still only scanned SKILLS_DIR. Now external dirs are respected
everywhere:
- skills_categories(): scan all dirs for category discovery
- _get_category_from_path(): resolve categories against any skills root
- skill_manager_tool._find_skill(): search all dirs for edit/patch/delete
- credential_files.get_skills_directory_mount(): mount all dirs into
Docker/Singularity containers (external dirs at external_skills/<idx>)
- credential_files.iter_skills_files(): list files from all dirs for
Modal/Daytona upload
- tools/environments/ssh.py: rsync all skill dirs to remote hosts
- gateway _check_unavailable_skill(): check disabled skills across all dirs
Usage in config.yaml:
skills:
external_dirs:
- ~/repos/agent-skills/hermes
- /shared/team-skills
* feat(skills): add content size limits for agent-created skills
Agent writes via skill_manage (create/edit/patch/write_file) are now
constrained to prevent unbounded growth:
- SKILL.md and supporting files: 100,000 character limit
- Supporting files: additional 1 MiB byte limit
- Patches on oversized hand-placed skills that reduce the size are
allowed (shrink path), but patches that grow beyond the limit are
rejected
Hand-placed skills and hub-installed skills have NO hard limit —
they load and function normally regardless of size. Hub installs
get a warning in the log if SKILL.md exceeds 100k chars.
This mirrors the memory system's char_limit pattern. Without this,
the agent auto-grows skills indefinitely through iterative patches
(hermes-agent-dev reached 197k chars / 72k tokens — 40x larger than
the largest skill in the entire skills.sh ecosystem).
Constants: MAX_SKILL_CONTENT_CHARS (100k), MAX_SKILL_FILE_BYTES (1MiB)
Tests: 14 new tests covering all write paths and edge cases
* feat(skills): add fuzzy matching to skill patch
_patch_skill now uses the same 8-strategy fuzzy matching engine
(tools/fuzzy_match.py) as the file patch tool. Handles whitespace
normalization, indentation differences, escape sequences, and
block-anchor matching. Eliminates exact-match failures when agents
patch skills with minor formatting mismatches.
Validate category names in _create_skill() before using them as
filesystem path segments. Previously, categories like '../escape' or
'/tmp/pwned' could write skill files outside ~/.hermes/skills/.
Adds _validate_category() that rejects slashes, backslashes, absolute
paths, and non-alphanumeric characters (reuses existing VALID_NAME_RE).
Tests: 5 new tests for traversal, absolute paths, and valid categories.
Salvaged from PR #1939 by Gutslabs.
Centralizes two widely-duplicated patterns into hermes_constants.py:
1. get_hermes_home() — Path resolution for ~/.hermes (HERMES_HOME env var)
- Was copy-pasted inline across 30+ files as:
Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
- Now defined once in hermes_constants.py (zero-dependency module)
- hermes_cli/config.py re-exports it for backward compatibility
- Removed local wrapper functions in honcho_integration/client.py,
tools/website_policy.py, tools/tirith_security.py, hermes_cli/uninstall.py
2. parse_reasoning_effort() — Reasoning effort string validation
- Was copy-pasted in cli.py, gateway/run.py, cron/scheduler.py
- Same validation logic: check against (xhigh, high, medium, low, minimal, none)
- Now defined once in hermes_constants.py, called from all 3 locations
- Warning log for unknown values kept at call sites (context-specific)
31 files changed, net +31 lines (125 insertions, 94 deletions)
Full test suite: 6179 passed, 0 failed
Changes the policy for agent-created skills with critical security
findings from 'block' (silently rejected) to 'ask' (allowed with
warning logged). The agent created the skill, so blocking it entirely
is too aggressive — let it through but log the findings.
- Policy: agent-created dangerous changed from block to ask
- should_allow_install returns None for 'ask' (vs True/False)
- format_scan_report shows 'NEEDS CONFIRMATION' for ask
- skill_manager_tool.py caller handles None (allows with warning)
- force=True still overrides as before
Based on PR #2271 by redhelix (closed — 3200 lines of unrelated
Mission Control code excluded).
* feat: improve memory prioritization — user preferences over procedural knowledge
Inspired by OpenAI Codex's memory prompt improvements (openai/codex#14493)
which focus memory writes on user preferences and recurring patterns
rather than procedural task details.
Key insight: 'Optimize for reducing future user steering — the most
valuable memory prevents the user from having to repeat themselves.'
Changes:
- MEMORY_GUIDANCE (prompt_builder.py): added prioritization hierarchy
and the core principle about reducing user steering
- MEMORY_SCHEMA (memory_tool.py): reordered WHEN TO SAVE list to put
corrections first, added explicit PRIORITY guidance
- Memory nudge (run_agent.py): now asks specifically about preferences,
corrections, and workflow patterns instead of generic 'anything'
- Memory flush (run_agent.py): now instructs to prioritize user
preferences and corrections over task-specific details
* feat: more aggressive skill creation and update prompting
Press harder on skill updates — the agent should proactively patch
skills when it encounters issues during use, not wait to be asked.
Changes:
- SKILLS_GUIDANCE: 'consider saving' → 'save'; added explicit instruction
to patch skills immediately when found outdated/wrong
- Skills header: added instruction to update loaded skills before finishing
if they had missing steps or wrong commands
- Skill nudge: more assertive ('save the approach' not 'consider saving'),
now also prompts for updating existing skills used in the task
- Skill nudge interval: lowered default from 15 to 10 iterations
- skill_manage schema: added 'patch it immediately' to update triggers
- Add 'emoji' field to ToolEntry and 'get_emoji()' to ToolRegistry
- Add emoji= to all 50+ registry.register() calls across tool files
- Add get_tool_emoji() helper in agent/display.py with 3-tier resolution:
skin override → registry default → hardcoded fallback
- Replace hardcoded emoji maps in run_agent.py, delegate_tool.py, and
gateway/run.py with centralized get_tool_emoji() calls
- Add 'tool_emojis' field to SkinConfig so skins can override per-tool
emojis (e.g. ares skin could use swords instead of wrenches)
- Add 11 tests (5 registry emoji, 6 display/skin integration)
- Update AGENTS.md skin docs table
Based on the approach from PR #1061 by ForgingAlex (emoji centralization
in registry). This salvage fixes several issues from the original:
- Does NOT split the cronjob tool (which would crash on missing schemas)
- Does NOT change image_generate toolset/requires_env/is_async
- Does NOT delete existing tests
- Completes the centralization (gateway/run.py was missed)
- Hooks into the skin system for full customizability
The security scanner (skills_guard.py) was only wired into the hub install path.
All other write paths to persistent state — skills created by the agent, memory
entries, cron prompts, and context files — bypassed it entirely. This closes
those gaps:
- file_operations: deny-list blocks writes to ~/.ssh, ~/.aws, ~/.hermes/.env, etc.
- code_execution_tool: filter secret env vars from sandbox child process
- skill_manager_tool: wire scan_skill() into create/edit/patch/write_file with rollback
- skills_guard: add "agent-created" trust level (same policy as community)
- memory_tool: scan content for injection/exfil before system prompt injection
- prompt_builder: scan AGENTS.md, .cursorrules, SOUL.md for prompt injection
- cronjob_tools: scan cron prompts for critical threats before scheduling
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Added a new `skill_manager_tool` to enable agents to create, update, and delete their own skills, enhancing procedural memory capabilities.
- Updated the skills directory structure to support user-created skills in `~/.hermes/skills/`, allowing for better organization and management.
- Enhanced the CLI and documentation to reflect the new skill management functionalities, including detailed instructions on creating and modifying skills.
- Implemented a manifest-based syncing mechanism for bundled skills to ensure user modifications are preserved during updates.
