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bojansandhaus/tool-repair-skill-for-hermes-and-opencode
Hermes Tool Repair Skill - deterministic tool call repair for LLM agents. Catches common JSON formatting mistakes open models make and fixes them before dispatch, with repair notes that teach the model to self-correct.
PROJECT TOPICS
INSTALL REFERENCE
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PROJECT README
A harness-level fix for LLM tool calling. Catches the common JSON formatting mistakes open models make and fixes them deterministically before the tool executor ever sees them. Ships with adapters for four agent frameworks:
| Adapter | Language | Repair strategy |
|---|---|---|
| Hermes (two calls) | Python | Mutate args pre-dispatch, then append repair notes to the result. Built and tested against this library's own 34-test Python suite and 21-test TypeScript parity suite. |
| OpenCode (plugin) | TypeScript | tool.execute.before hook, mutates args directly |
| Claude Code (hooks) | Bash + jq | PreToolUse block so the model retries, PostToolUse telemetry. Cannot mutate arguments: PreToolUse can only allow or block, so this adapter corrects by feedback, not in place. |
| DeepSeek Harness (hook bridge) | Bash + jq | PreToolUse command hook registered with a harness bridge plugin. Also cannot mutate arguments: the seam allows deny, not rewrite. |
Based on the approach that made DeepSeek V4 Pro outperform Opus 4.7 on tool calling (see CommandCode's write-up on tool call repairs, their deep dive, and this video walkthrough).
Open models (DeepSeek, GLM, Qwen, Kimi) make the same tiny JSON mistakes in tool calls over and over. Each mistake triggers a validation error. The model retries with the same bad format. The session degrades through 50+ wasted retry cycles. The model never learns because the error messages are opaque.
These mistakes are not random. They are a small finite set of patterns caused by the model's training distribution leaking through the tool boundary.
Most people frame this as a model problem: "DeepSeek is bad at tool calling, wait for the next version." That is wrong. It is a harness problem. The harness sits between the model and the tool executor. It decides what to do with the model's output: reject it and waste tokens retrying, or fix it silently and move on. A harness that repairs deterministically turns a bad-at-tool-calling model into a functional one in about 200 lines of code.
The model did not change. The harness got more forgiving in exactly the places it needed to be.
| Pattern | What the model sends | What it should be |
|---|---|---|
| Null omission | {"cmd": "ls", "timeout": null} |
{"cmd": "ls"} |
| Null preserved | {"name": null} where name is required |
unchanged, so the validator reports it |
| Stringified array | {"files": "[\"a\",\"b\"]"} |
{"files": ["a", "b"]} |
| Empty object | {"files": {}} |
{"files": []} |
| Bare string | {"files": "main.ts"} |
{"files": ["main.ts"]} |
| Markdown autolink | {"filePath": "/x/[f.md](http://f.md)"} |
{"filePath": "/x/f.md"} |
flowchart TD
subgraph Harness["HARNESS BOUNDARY"]
direction TB
P["Parse JSON"] --> V{"Schema Valid?"}
V -->|"Yes"| D["Execute Tool"]
V -->|"No"| W["Walk Issue List by Path"]
W --> R["Apply Repairs<br/>in Priority Order"]
R --> RV{"Re-validate"}
RV -->|"Pass"| D
RV -->|"Fail"| E["Return Readable Error<br/>with Guidance"]
end
M["Model Output<br/>(raw tool call JSON)"] --> P
D --> N["Tool Result<br/>+ Repair Note"]
E --> N
N --> B["Back to Model"]
Everything inside the HARNESS BOUNDARY box is your agent framework. The model provides the raw JSON and receives the result. All repair logic, validation, and correction notes are handled at the harness layer.
Key design rule: a repair only fires when it is unambiguously the right thing, so legitimate data survives. The rules that could touch arbitrary content are narrow on purpose. A string is only parsed as an array when it parses as one; an auto-link is only unwrapped when the link text equals the URL's own path component; an array-shaped repair only fires when the schema says the field is an array; a null is only dropped when the schema neither requires the field nor admits null for it. So writeFile content that happens to be JSON-shaped, a real [click](https://example.com) link, and bracketed prose like [1, 2] and [3, 4] all pass through untouched, and the test suite asserts each of those cases.
tool_repair.py (the core library)Standalone Python module with no dependencies beyond stdlib. Main entry point:
from agent.tool_repair import repair_function_args
repaired_args, repair_notes = repair_function_args(
function_name="readFile",
function_args={"path": "/tmp/test.txt", "limit": None},
tool_schema=None, # optional JSON schema for type-aware repairs
)
# repaired_args = {"path": "/tmp/test.txt"}
# repair_notes = ["[repair: null values removed for optional fields]"]
Can be imported and used by any agent framework, not just Hermes.
The library is built and tested: 34 Python tests, 21 TypeScript parity tests, and CI runs both plus the module self-test. Integrating it into a Hermes harness is two calls at the harness layer, between the model's output and the tool executor:
agent/agent_runtime_helpers.py. sanitize_tool_call_arguments() is a harness function that walks tool calls before dispatch. Call repair_function_args() on the parsed dict after json.loads() has already succeeded. If repairs trigger, write the fixed JSON back to the call's arguments.
agent/tool_dispatch_helpers.py. make_tool_result_message() is a harness function that builds the tool result before it goes back to the model. Call deduplicate_repair_notes() there to append the repair notes to the result content.
The model reads the repair note alongside the successful result and adapts on the next turn. The harness did the fixing. The model just benefits from seeing what was fixed.
references/plugin.yaml declares the hook surface (transform_llm_output) and plugin-architecture.md sketches the wiring. The pre_tool_call hook today can block a call but cannot modify arguments, so the two-call integration above is what you write; plugin-architecture.md describes the hook surface that would make it a config toggle instead.
This repo ships adapters for two other agent frameworks in the adapters/
directory. Each adapter wraps the same core tool_repair.py library with the
harness-specific wiring.
| Adapter | Location | Key mechanism |
|---|---|---|
| Hermes | SKILL.md |
sanitize_tool_call_arguments pre-dispatch + repair notes. |
| OpenCode | adapters/opencode/ |
tool.execute.before TS plugin, mutates args directly |
| Claude Code | adapters/claude-code/ |
PreToolUse block + PostToolUse telemetry (bash + jq) |
| DeepSeek Harness | adapters/deepseek-harness/ |
PreToolUse command hook via a harness bridge plugin (bash + jq) |
OpenCode has the cleanest integration because its tool.execute.before hook
supports argument mutation. Claude Code and DeepSeek Harness are the most
limited: both can only block a call, not mutate it, so they waste a turn when
they detect a pattern. The DeepSeek Harness adapter reads the harness' own
payload keys (tool_name, tool_input), which is why it is a separate script
rather than a shared one.
See each adapter's README for setup instructions.
[1, 2] and [3, 4] and a real link like [click](https://example.com) pass through untouched, which the test suite asserts.The core library (tool_repair.py) needs nothing beyond Python standard library.
| Adapter | Dependencies |
|---|---|
| Hermes | Hermes Agent (any recent version), stdlib only for the library |
| OpenCode | TypeScript, OpenCode CLI |
| Claude Code | bash, jq |
| DeepSeek Harness | @deepseek-ai/dsh, bash, jq |
No pip packages, no npm modules, no external services for the core library.
cp references/tool_repair.py /your/project/tool_repair.py
from tool_repair import repair_function_args
fixed, notes = repair_function_args("my_tool", {"some_field": None})
Copy the library, then add the two calls described in Components:
cp references/tool_repair.py /path/to/hermes/agent/tool_repair.py
Or prompt your agent:
Clone
https://github.com/bojansandhaus/tool-repair-skill-for-hermes-and-opencode.git, copyreferences/tool_repair.pyinto the Hermes agent directory, then callrepair_function_argsinsidesanitize_tool_call_argumentsafterjson.loads()succeeds anddeduplicate_repair_notesinmake_tool_result_message. There is no Hermes config key for this; it needs those two code changes.
Copy the TypeScript adapter into your OpenCode plugins directory:
cp -r adapters/opencode/* ~/.config/opencode/plugins/
Or prompt your agent:
Clone
https://github.com/bojansandhaus/tool-repair-skill-for-hermes-and-opencode.gitand copy the TypeScript plugin fromadapters/opencode/to~/.config/opencode/plugins/.
Copy the hook scripts and configure in claude.json:
cp adapters/claude-code/*.sh .claude/hooks/
chmod +x .claude/hooks/*.sh
Or prompt your agent:
Clone
https://github.com/bojansandhaus/tool-repair-skill-for-hermes-and-opencode.git, copy the hook scripts fromadapters/claude-code/to.claude/hooks/, make them executable, and add thepre_tool_useandpost_tool_usehook entries toclaude.json.
{
"hooks": {
"pre_tool_use": {
"matcher": "*",
"command": "bash .claude/hooks/pre_tool_use.sh"
},
"post_tool_use": {
"matcher": "*",
"command": "bash .claude/hooks/post_tool_use.sh"
}
}
}
Copy the hook, then register it with one of the bridge plugins the harness
ships (@deepseek-ai/dsh-hooks-claude-code or @deepseek-ai/dsh-hooks-codex):
mkdir -p ~/.dsh/tool-repair
cp adapters/deepseek-harness/pre_tool_use.sh ~/.dsh/tool-repair/pre_tool_use.sh
chmod +x ~/.dsh/tool-repair/pre_tool_use.sh
Write ~/.dsh/tool-repair/hooks.json:
{
"hooks": {
"PreToolUse": [
{
"matcher": "*",
"hooks": [
{ "type": "command", "command": "bash ~/.dsh/tool-repair/pre_tool_use.sh" }
]
}
]
}
}
And add the bridge to ~/.dsh/cordis.patch.yml:
- id: hooks-claude-code
name: '@deepseek-ai/dsh-hooks-claude-code'
config:
configPath: ~/.dsh/tool-repair/hooks.json
Or prompt your agent:
Clone
https://github.com/bojansandhaus/tool-repair-skill-for-hermes-and-opencode, copyadapters/deepseek-harness/pre_tool_use.shto~/.dsh/tool-repair/pre_tool_use.sh, make it executable, write ahooks.jsonregistering it as aPreToolUsecommand hook, and add a@deepseek-ai/dsh-hooks-claude-codeentry pointing at it in~/.dsh/cordis.patch.yml.
See adapters/deepseek-harness/README.md for the Codex bridge variant, the
verification steps, and the limitations.
git clone https://github.com/bojansandhaus/tool-repair-skill-for-hermes-and-opencode.git
cd tool-repair-skill-for-hermes-and-opencode
import json
from tool_repair import repair_function_args
def dispatch_tool(name, args_json):
args = json.loads(args_json)
if isinstance(args, dict):
fixed_args, notes = repair_function_args(name, args)
if notes:
print(f"Repaired {name}: {notes}")
args_json = json.dumps(fixed_args)
# proceed with the tool call
Two calls, in sanitize_tool_call_arguments and make_tool_result_message, as
shown in Components. There is no config key for this; it is harness code.
repair_function_args pre-dispatch, deduplicate_repair_notes on the result)MIT. Free to use, modify, and distribute. This is a direct implementation of patterns discovered by the CommandCode team. Credit for the original insight goes to them.
CLASSIFICATION EVIDENCE
系统优先读取 GitHub Topics,再与站内分类词典和词根规则比对。当前命中: claude-code、json、json-repair。